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Version:	3.0.1
Date:	2025-09-20
Title:	Antimicrobial Resistance Data Analysis
Description:	Functions to simplify and standardise antimicrobial resistance (AMR) data analysis and to work with microbial and antimicrobial properties by using evidence-based methods, as described in <doi:10.18637/jss.v104.i03>.
Depends:	R (≥ 3.0.0)
Suggests:	cleaner, cli, crayon, curl, data.table, dplyr, ggplot2,knitr, openxlsx, parallelly, pillar, progress, readxl, recipes,rlang, rmarkdown, rstudioapi, rvest, skimr, testthat, tibble,tidymodels, tidyselect, tinytest, vctrs, xml2
VignetteBuilder:	knitr,rmarkdown
URL:	https://amr-for-r.org,https://github.com/msberends/AMR
BugReports:	https://github.com/msberends/AMR/issues
License:	GPL-2 | file LICENSE
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.3
NeedsCompilation:	no
Packaged:	2025-09-20 10:56:39 UTC; m.s.berends
Author:	Matthijs S. Berends [aut, cre], Dennis Souverein [aut, ctb], Erwin E. A. Hassing [aut, ctb], Aislinn Cook [ctb], Andrew P. Norgan [ctb], Anita Williams [ctb], Annick Lenglet [ctb], Anthony Underwood [ctb], Anton Mymrikov [ctb], Bart C. Meijer [ctb], Christian F. Luz [ctb], Dmytro Mykhailenko [ctb], Eric H. L. C. M. Hazenberg [ctb], Gwen Knight [ctb], Jane Hawkey [ctb], Jason Stull [ctb], Javier Sanchez [ctb], Jonas Salm [ctb], Judith M. Fonville [ctb], Kathryn Holt [ctb], Larisse Bolton [ctb], Matthew Saab [ctb], Natacha Couto [ctb], Peter Dutey-Magni [ctb], Rogier P. Schade [ctb], Sofia Ny [ctb], Alex W. Friedrich [ths], Bhanu N. M. Sinha [ths], Casper J. Albers [ths], Corinna Glasner [ths]
Maintainer:	Matthijs S. Berends <m.s.berends@umcg.nl>
Repository:	CRAN
Date/Publication:	2025-09-20 11:40:03 UTC

TheAMR Package

Description

Welcome to theAMR package.

TheAMR package is a peer-reviewed,free and open-source R package withzero dependencies to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods.Our aim is to provide a standard for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. We are a team ofmany different researchers from around the globe to make this a successful and durable project!

This work was published in the Journal of Statistical Software (Volume 104(3);doi:10.18637/jss.v104.i03) and formed the basis of two PhD theses (doi:10.33612/diss.177417131 anddoi:10.33612/diss.192486375).

After installing this package, R knows~79 000 distinct microbial species (updated June 2024) and all~620 antimicrobial and antiviral drugs by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI 2011-2025 and EUCAST 2011-2025 are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013).It was designed to work in any setting, including those with very limited resources. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of theUniversity of Groningen and theUniversity Medical Center Groningen.

TheAMR package is available in English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Turkish, Ukrainian, Urdu, and Vietnamese. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.

Download Our Reference Data

All reference data sets in the AMR package - including information on microorganisms, antimicrobials, and clinical breakpoints - are freely available for download in multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, and Stata.

For maximum compatibility, we also provide machine-readable, tab-separated plain text files suitable for use in any software, including laboratory information systems.

Visitour website for direct download links, or explore the actual files inour GitHub repository.

Author(s)

Maintainer: Matthijs S. Berendsm.s.berends@umcg.nl (ORCID)

Authors:

• Dennis Souverein (ORCID) [contributor]

• Erwin E. A. Hassing [contributor]

Other contributors:

• Aislinn Cook (ORCID) [contributor]

• Andrew P. Norgan (ORCID) [contributor]

• Anita Williams (ORCID) [contributor]

• Annick Lenglet (ORCID) [contributor]

• Anthony Underwood (ORCID) [contributor]

• Anton Mymrikov [contributor]

• Bart C. Meijer [contributor]

• Christian F. Luz (ORCID) [contributor]

• Dmytro Mykhailenko [contributor]

• Eric H. L. C. M. Hazenberg [contributor]

• Gwen Knight (ORCID) [contributor]

• Jane Hawkey (ORCID) [contributor]

• Jason Stull (ORCID) [contributor]

• Javier Sanchez (ORCID) [contributor]

• Jonas Salm [contributor]

• Judith M. Fonville [contributor]

• Kathryn Holt (ORCID) [contributor]

• Larisse Bolton (ORCID) [contributor]

• Matthew Saab [contributor]

• Natacha Couto (ORCID) [contributor]

• Peter Dutey-Magni (ORCID) [contributor]

• Rogier P. Schade [contributor]

• Sofia Ny (ORCID) [contributor]

• Alex W. Friedrich (ORCID) [thesis advisor]

• Bhanu N. M. Sinha (ORCID) [thesis advisor]

• Casper J. Albers (ORCID) [thesis advisor]

• Corinna Glasner (ORCID) [thesis advisor]

Source

To cite AMR in publications use:

Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022). "AMR: An R Package for Working with Antimicrobial Resistance Data."Journal of Statistical Software,104(3), 1-31.doi:10.18637/jss.v104.i03

A BibTeX entry for LaTeX users is:

@Article{,  title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data},  author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner},  journal = {Journal of Statistical Software},  year = {2022},  volume = {104},  number = {3},  pages = {1--31},  doi = {10.18637/jss.v104.i03},}

See Also

Useful links:

• https://amr-for-r.org

• https://github.com/msberends/AMR

• Report bugs athttps://github.com/msberends/AMR/issues

Deprecated Functions, Arguments, or Datasets

Description

These objects are so-called 'Deprecated'.They will be removed in a future version of this package. Using these will give a warning with the name of the alternative object it has been replaced by (if there is one).

Usage

ab_class(...)ab_selector(...)

Options for the AMR package

Description

This is an overview of all the package-specificoptions() you can set in theAMR package.

Options

• AMR_antibiogram_formatting_type
  Anumeric (1-22) to use inantibiogram(), to indicate which formatting type to use.

• AMR_breakpoint_type
  Acharacter to use inas.sir(), to indicate which breakpoint type to use. This must be either "ECOFF", "animal", or "human".

• AMR_capped_mic_handling
  Acharacter to use inas.sir(), to indicate how capped MIC values (<,<=,>,>=) should be interpreted. Must be one of"standard","strict","relaxed", or"inverse" - the default is"standard".

• AMR_cleaning_regex
  Aregular expression (case-insensitive) to use inas.mo() and allmo_* functions, to clean the user input. The default is the outcome ofmo_cleaning_regex(), which removes texts between brackets and texts such as "species" and "serovar".

• AMR_custom_ab
  A file location to an RDS file, to use custom antimicrobial drugs with this package. This is explained inadd_custom_antimicrobials().

• AMR_custom_mo
  A file location to an RDS file, to use custom microorganisms with this package. This is explained inadd_custom_microorganisms().

• AMR_eucastrules
  Acharacter to set the default types of rules foreucast_rules() function, must be one or more of:"breakpoints","expert","other","custom","all", and defaults toc("breakpoints", "expert").

• AMR_guideline
  Acharacter to set the default guideline for interpreting MIC values and disk diffusion diameters withas.sir(). Can be only the guideline name (e.g.,"CLSI") or the name with a year (e.g."CLSI 2019"). The default to the latest implemented EUCAST guideline, currently"EUCAST 2025". Supported guideline are currently EUCAST (2011-2025) and CLSI (2011-2025).

• AMR_ignore_pattern
  Aregular expression to ignore (i.e., makeNA) any match given inas.mo() and allmo_* functions.

• AMR_include_PKPD
  Alogical to use inas.sir(), to indicate that PK/PD clinical breakpoints must be applied as a last resort - the default isTRUE.

• AMR_substitute_missing_r_breakpoint
  Alogical to use inas.sir(), to indicate that missing R breakpoints must be substituted with"R" - the default isFALSE.

• AMR_include_screening
  Alogical to use inas.sir(), to indicate that clinical breakpoints for screening are allowed - the default isFALSE.

• AMR_keep_synonyms
  Alogical to use inas.mo() and allmo_* functions, to indicate if old, previously valid taxonomic names must be preserved and not be corrected to currently accepted names. The default isFALSE.

• AMR_locale
  Acharacter to set the language for theAMR package, can be one of these supported language names orISO 639-1 codes: English (en), Arabic (ar), Bengali (bn), Chinese (zh), Czech (cs), Danish (da), Dutch (nl), Finnish (fi), French (fr), German (de), Greek (el), Hindi (hi), Indonesian (id), Italian (it), Japanese (ja), Korean (ko), Norwegian (no), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Spanish (es), Swahili (sw), Swedish (sv), Turkish (tr), Ukrainian (uk), Urdu (ur), or Vietnamese (vi). The default is the current system language (if supported, English otherwise).

• AMR_mo_source
  A file location for a manual code list to be used inas.mo() and allmo_* functions. This is explained inset_mo_source().

Saving Settings Between Sessions

Settings inR are not saved globally and are thus lost whenR is exited. You can save your options to your own.Rprofile file, which is a user-specific file. You can edit it using:

  utils::file.edit("~/.Rprofile")

In this file, you can set options such as...

 options(AMR_locale = "pt") options(AMR_include_PKPD = TRUE)

...to add Portuguese language support of antimicrobials, and allow PK/PD rules when interpreting MIC values withas.sir().

Share Options Within Team

For a more global approach, e.g. within a (data) team, save an options file to a remote file location, such as a shared network drive, and have each user read in this file automatically at start-up. This would work in this way:

1. Save a plain text file to e.g. "X:/team_folder/R_options.R" and fill it with preferred settings.

2. For each user, open the.Rprofile file usingutils::file.edit("~/.Rprofile") and put in there:

     source("X:/team_folder/R_options.R")

3. Reload R/RStudio and check the settings withgetOption(), e.g.getOption("AMR_locale") if you have set that value.

Now the team settings are configured in only one place, and can be maintained there.

WHOCC: WHO Collaborating Centre for Drug Statistics Methodology

Description

All antimicrobial drugs and their official names, ATC codes, ATC groups and defined daily dose (DDD) are included in this package, using the WHO Collaborating Centre for Drug Statistics Methodology.

WHOCC

This package containsall ~550 antibiotic, antimycotic and antiviral drugs and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC,https://atcddd.fhi.no) and the Pharmaceuticals Community Register of the European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm).

These have become the gold standard for international drug utilisation monitoring and research.

The WHOCC is located in Oslo at the Norwegian Institute of Public Health and funded by the Norwegian government. The European Commission is the executive of the European Union and promotes its general interest.

NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. Seehttps://atcddd.fhi.no/copyright_disclaimer/.

Examples

as.ab("meropenem")ab_name("J01DH02")ab_tradenames("flucloxacillin")

Data Set with 500 Isolates - WHONET Example

Description

This example data set has the exact same structure as an export file from WHONET. Such files can be used with this package, as this example data set shows. The antimicrobial results are from ourexample_isolates data set. All patient names were created using online surname generators and are only in place for practice purposes.

Usage

WHONET

Format

Atibble with 500 observations and 53 variables:

• ⁠Identification number⁠
  ID of the sample

• ⁠Specimen number⁠
  ID of the specimen

• Organism
  Name of the microorganism. Before analysis, you should transform this to a valid microbial class, usingas.mo().

• Country
  Country of origin

• Laboratory
  Name of laboratory

• ⁠Last name⁠
  Fictitious last name of patient

• ⁠First name⁠
  Fictitious initial of patient

• Sex
  Fictitious gender of patient

• Age
  Fictitious age of patient

• ⁠Age category⁠
  Age group, can also be looked up usingage_groups()

• ⁠Date of admission⁠
  Date of hospital admission

• ⁠Specimen date⁠
  Date when specimen was received at laboratory

• ⁠Specimen type⁠
  Specimen type or group

• ⁠Specimen type (Numeric)⁠
  Translation of"Specimen type"

• Reason
  Reason of request with Differential Diagnosis

• ⁠Isolate number⁠
  ID of isolate

• ⁠Organism type⁠
  Type of microorganism, can also be looked up usingmo_type()

• Serotype
  Serotype of microorganism

• Beta-lactamase
  Microorganism produces beta-lactamase?

• ESBL
  Microorganism produces extended spectrum beta-lactamase?

• Carbapenemase
  Microorganism produces carbapenemase?

• ⁠MRSA screening test⁠
  Microorganism is possible MRSA?

• ⁠Inducible clindamycin resistance⁠
  Clindamycin can be induced?

• Comment
  Other comments

• ⁠Date of data entry⁠
  Date this data was entered in WHONET

• AMP_ND10:CIP_EE
  28 different antimicrobials. You can lookup the abbreviations in theantimicrobials data set, or use e.g.ab_name("AMP") to get the official name immediately. Before analysis, you should transform this to a valid antimicrobial class, usingas.sir().

Download Our Reference Data

All reference data sets in the AMR package - including information on microorganisms, antimicrobials, and clinical breakpoints - are freely available for download in multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, and Stata.

For maximum compatibility, we also provide machine-readable, tab-separated plain text files suitable for use in any software, including laboratory information systems.

Visitour website for direct download links, or explore the actual files inour GitHub repository.

Examples

WHONET

Retrieve Antimicrobial Drug Names and Doses from Clinical Text

Description

Use this function on e.g. clinical texts from health care records. It returns alist with all antimicrobial drugs, doses and forms of administration found in the texts.

Usage

ab_from_text(text, type = c("drug", "dose", "administration"),  collapse = NULL, translate_ab = FALSE, thorough_search = NULL,  info = interactive(), ...)

Arguments

Details

This function is also internally used byas.ab(), although it then only searches for the first drug name and will throw a note if more drug names could have been returned. Note: theas.ab() function may use very long regular expression to match brand names of antimicrobial drugs. This may fail on some systems.

Argumenttype

At default, the function will search for antimicrobial drug names. All text elements will be searched for official names, ATC codes and brand names. As it usesas.ab() internally, it will correct for misspelling.

Withtype = "dose" (or similar, like "dosing", "doses"), all text elements will be searched fornumeric values that are higher than 100 and do not resemble years. The output will benumeric. It supports any unit (g, mg, IE, etc.) and multiple values in one clinical text, seeExamples.

Withtype = "administration" (or abbreviations, like "admin", "adm"), all text elements will be searched for a form of drug administration. It supports the following forms (including common abbreviations): buccal, implant, inhalation, instillation, intravenous, nasal, oral, parenteral, rectal, sublingual, transdermal and vaginal. Abbreviations for oral (such as 'po', 'per os') will become "oral", all values for intravenous (such as 'iv', 'intraven') will become "iv". It supports multiple values in one clinical text, seeExamples.

Argumentcollapse

Without usingcollapse, this function will return alist. This can be convenient to use e.g. inside amutate()):
df %>% mutate(abx = ab_from_text(clinical_text))

The returned AB codes can be transformed to official names, groups, etc. with allab_* functions such asab_name() andab_group(), or by using thetranslate_ab argument.

With usingcollapse, this function will return acharacter:
df %>% mutate(abx = ab_from_text(clinical_text, collapse = "|"))

Value

Alist, or acharacter ifcollapse is notNULL

Examples

# mind the bad spelling of amoxicillin in this line,# straight from a true health care record:ab_from_text("28/03/2020 regular amoxicilliin 500mg po tid")ab_from_text("500 mg amoxi po and 400mg cipro iv")ab_from_text("500 mg amoxi po and 400mg cipro iv", type = "dose")ab_from_text("500 mg amoxi po and 400mg cipro iv", type = "admin")ab_from_text("500 mg amoxi po and 400mg cipro iv", collapse = ", ")# if you want to know which antibiotic groups were administered, do e.g.:abx <- ab_from_text("500 mg amoxi po and 400mg cipro iv")ab_group(abx[[1]])if (require("dplyr")) {  tibble(clinical_text = c(    "given 400mg cipro and 500 mg amox",    "started on doxy iv today"  )) %>%    mutate(      abx_codes = ab_from_text(clinical_text),      abx_doses = ab_from_text(clinical_text, type = "doses"),      abx_admin = ab_from_text(clinical_text, type = "admin"),      abx_coll = ab_from_text(clinical_text, collapse = "|"),      abx_coll_names = ab_from_text(clinical_text,        collapse = "|",        translate_ab = "name"      ),      abx_coll_doses = ab_from_text(clinical_text,        type = "doses",        collapse = "|"      ),      abx_coll_admin = ab_from_text(clinical_text,        type = "admin",        collapse = "|"      )    )}

Get Properties of an Antibiotic

Description

Use these functions to return a specific property of an antibiotic from theantimicrobials data set. All input values will be evaluated internally withas.ab().

Usage

ab_name(x, language = get_AMR_locale(), tolower = FALSE, ...)ab_cid(x, ...)ab_synonyms(x, ...)ab_tradenames(x, ...)ab_group(x, language = get_AMR_locale(), ...)ab_atc(x, only_first = FALSE, ...)ab_atc_group1(x, language = get_AMR_locale(), ...)ab_atc_group2(x, language = get_AMR_locale(), ...)ab_loinc(x, ...)ab_ddd(x, administration = "oral", ...)ab_ddd_units(x, administration = "oral", ...)ab_info(x, language = get_AMR_locale(), ...)ab_url(x, open = FALSE, ...)ab_property(x, property = "name", language = get_AMR_locale(), ...)set_ab_names(data, ..., property = "name", language = get_AMR_locale(),  snake_case = NULL)

Arguments

Details

All outputwill be translated where possible.

The functionab_url() will return the direct URL to the official WHO website. A warning will be returned if the required ATC code is not available.

The functionset_ab_names() is a special column renaming function fordata.frames. It renames columns names that resemble antimicrobial drugs. It always makes sure that the new column names are unique. Ifproperty = "atc" is set, preference is given to ATC codes from the J-group.

Value

• Aninteger in case ofab_cid()

• A namedlist in case ofab_info() and multipleab_atc()/ab_synonyms()/ab_tradenames()

• Adouble in case ofab_ddd()

• Adata.frame in case ofset_ab_names()

• Acharacter in all other cases

Source

World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology:https://atcddd.fhi.no/atc_ddd_index/

European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER:https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm

Download Our Reference Data

All reference data sets in the AMR package - including information on microorganisms, antimicrobials, and clinical breakpoints - are freely available for download in multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, and Stata.

For maximum compatibility, we also provide machine-readable, tab-separated plain text files suitable for use in any software, including laboratory information systems.

Visitour website for direct download links, or explore the actual files inour GitHub repository.

See Also

antimicrobials

Examples

# all properties:ab_name("AMX")ab_atc("AMX")ab_cid("AMX")ab_synonyms("AMX")ab_tradenames("AMX")ab_group("AMX")ab_atc_group1("AMX")ab_atc_group2("AMX")ab_url("AMX")# smart lowercase transformationab_name(x = c("AMC", "PLB"))ab_name(x = c("AMC", "PLB"), tolower = TRUE)# defined daily doses (DDD)ab_ddd("AMX", "oral")ab_ddd_units("AMX", "oral")ab_ddd("AMX", "iv")ab_ddd_units("AMX", "iv")ab_info("AMX") # all properties as a list# all ab_* functions use as.ab() internally, so you can go from 'any' to 'any':ab_atc("AMP")ab_group("J01CA01")ab_loinc("ampicillin")ab_name("21066-6")ab_name(6249)ab_name("J01CA01")# spelling from different languages and dyslexia are no problemab_atc("ceftriaxon")ab_atc("cephtriaxone")ab_atc("cephthriaxone")ab_atc("seephthriaaksone")# use set_ab_names() for renaming columnscolnames(example_isolates)colnames(set_ab_names(example_isolates))colnames(set_ab_names(example_isolates, NIT:VAN))if (require("dplyr")) {  example_isolates %>%    set_ab_names()  # this does the same:  example_isolates %>%    rename_with(set_ab_names)  # set_ab_names() works with any AB property:  example_isolates %>%    set_ab_names(property = "atc")  example_isolates %>%    set_ab_names(where(is.sir)) %>%    colnames()  example_isolates %>%    set_ab_names(NIT:VAN) %>%    colnames()}

Add Custom Antimicrobials

Description

Withadd_custom_antimicrobials() you can add your own custom antimicrobial drug names and codes.

Usage

add_custom_antimicrobials(x)clear_custom_antimicrobials()

Arguments

Details

Important: Due to howR works, theadd_custom_antimicrobials() function has to be run in everyR session - added antimicrobials are not stored between sessions and are thus lost whenR is exited.

There are two ways to circumvent this and automate the process of adding antimicrobials:

Method 1: Using the package optionAMR_custom_ab, which is the preferred method. To use this method:

1. Create a data set in the structure of theantimicrobials data set (containing at the very least columns "ab" and "name") and save it withsaveRDS() to a location of choice, e.g."~/my_custom_ab.rds", or any remote location.

2. Set the file location to the package optionAMR_custom_ab:options(AMR_custom_ab = "~/my_custom_ab.rds"). This can even be a remote file location, such as an https URL. Since options are not saved betweenR sessions, it is best to save this option to the.Rprofile file so that it will be loaded on start-up ofR. To do this, open the.Rprofile file using e.g.utils::file.edit("~/.Rprofile"), add this text and save the file:

   # Add custom antimicrobial codes:options(AMR_custom_ab = "~/my_custom_ab.rds")

   Upon package load, this file will be loaded and run through theadd_custom_antimicrobials() function.

Method 2: Loading the antimicrobial additions directly from your.Rprofile file. Note that the definitions will be stored in a user-specificR file, which is a suboptimal workflow. To use this method:

1. Edit the.Rprofile file using e.g.utils::file.edit("~/.Rprofile").

2. Add a text like below and save the file:

    # Add custom antibiotic drug codes: AMR::add_custom_antimicrobials(   data.frame(ab = "TESTAB",              name = "Test Antibiotic",              group = "Test Group") )

Useclear_custom_antimicrobials() to clear the previously added antimicrobials.

See Also

add_custom_microorganisms() to add custom microorganisms.

Examples

# returns a wildly guessed result:as.ab("testab")# now add a custom entry - it will be considered by as.ab() and# all ab_*() functionsadd_custom_antimicrobials(  data.frame(    ab = "TESTAB",    name = "Test Antibiotic",    # you can add any property present in the    # 'antimicrobials' data set, such as 'group':    group = "Test Group"  ))# "testab" is now a new antibiotic:as.ab("testab")ab_name("testab")ab_group("testab")ab_info("testab")# Add Co-fluampicil, which is one of the many J01CR50 codes, see# https://atcddd.fhi.no/ddd/list_of_ddds_combined_products/add_custom_antimicrobials(  data.frame(    ab = "COFLU",    name = "Co-fluampicil",    atc = "J01CR50",    group = "Beta-lactams/penicillins"  ))ab_atc("Co-fluampicil")ab_name("J01CR50")# even antimicrobial selectors work# see ?amr_selectorx <- data.frame(  random_column = "some value",  coflu = as.sir("S"),  ampicillin = as.sir("R"))xx[, betalactams()]

Add Custom Microorganisms

Description

Withadd_custom_microorganisms() you can add your own custom microorganisms, such the non-taxonomic outcome of laboratory analysis.

Usage

add_custom_microorganisms(x)clear_custom_microorganisms()

Arguments

Details

This function will fill in missing taxonomy for you, if specific taxonomic columns are missing, seeExamples.

Important: Due to howR works, theadd_custom_microorganisms() function has to be run in everyR session - added microorganisms are not stored between sessions and are thus lost whenR is exited.

There are two ways to circumvent this and automate the process of adding microorganisms:

Method 1: Using the package optionAMR_custom_mo, which is the preferred method. To use this method:

1. Create a data set in the structure of themicroorganisms data set (containing at the very least column "genus") and save it withsaveRDS() to a location of choice, e.g."~/my_custom_mo.rds", or any remote location.

2. Set the file location to the package optionAMR_custom_mo:options(AMR_custom_mo = "~/my_custom_mo.rds"). This can even be a remote file location, such as an https URL. Since options are not saved betweenR sessions, it is best to save this option to the.Rprofile file so that it will be loaded on start-up ofR. To do this, open the.Rprofile file using e.g.utils::file.edit("~/.Rprofile"), add this text and save the file:

   # Add custom microorganism codes:options(AMR_custom_mo = "~/my_custom_mo.rds")

   Upon package load, this file will be loaded and run through theadd_custom_microorganisms() function.

Method 2: Loading the microorganism directly from your.Rprofile file. Note that the definitions will be stored in a user-specificR file, which is a suboptimal workflow. To use this method:

1. Edit the.Rprofile file using e.g.utils::file.edit("~/.Rprofile").

2. Add a text like below and save the file:

    # Add custom antibiotic drug codes: AMR::add_custom_microorganisms(   data.frame(genus = "Enterobacter",              species = "asburiae/cloacae") )

Useclear_custom_microorganisms() to clear the previously added microorganisms.

See Also

add_custom_antimicrobials() to add custom antimicrobials.

Examples

# a combination of species is not formal taxonomy, so# this will result in "Enterobacter cloacae cloacae",# since it resembles the input best:mo_name("Enterobacter asburiae/cloacae")# now add a custom entry - it will be considered by as.mo() and# all mo_*() functionsadd_custom_microorganisms(  data.frame(    genus = "Enterobacter",    species = "asburiae/cloacae"  ))# E. asburiae/cloacae is now a new microorganism:mo_name("Enterobacter asburiae/cloacae")# its code:as.mo("Enterobacter asburiae/cloacae")# all internal algorithms will work as well:mo_name("Ent asburia cloacae")# and even the taxonomy was added based on the genus!mo_family("E. asburiae/cloacae")mo_gramstain("Enterobacter asburiae/cloacae")mo_info("Enterobacter asburiae/cloacae")# the function tries to be forgiving:add_custom_microorganisms(  data.frame(    GENUS = "BACTEROIDES / PARABACTEROIDES SLASHLINE",    SPECIES = "SPECIES"  ))mo_name("BACTEROIDES / PARABACTEROIDES")mo_rank("BACTEROIDES / PARABACTEROIDES")# taxonomy still works, even though a slashline genus was given as input:mo_family("Bacteroides/Parabacteroides")# for groups and complexes, set them as species or subspecies:add_custom_microorganisms(  data.frame(    genus = "Citrobacter",    species = c("freundii", "braakii complex"),    subspecies = c("complex", "")  ))mo_name(c("C. freundii complex", "C. braakii complex"))mo_species(c("C. freundii complex", "C. braakii complex"))mo_gramstain(c("C. freundii complex", "C. braakii complex"))

Age in Years of Individuals

Description

Calculates age in years based on a reference date, which is the system date at default.

Usage

age(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE, ...)

Arguments

Details

Ages below 0 will be returned asNA with a warning. Ages above 120 will only give a warning.

This function vectorises over bothx andreference, meaning that either can have a length of 1 while the other argument has a larger length.

Value

Aninteger (no decimals) ifexact = FALSE, adouble (with decimals) otherwise

See Also

To split ages into groups, use theage_groups() function.

Examples

# 10 random pre-Y2K birth datesdf <- data.frame(birth_date = as.Date("2000-01-01") - runif(10) * 25000)# add agesdf$age <- age(df$birth_date)# add exact agesdf$age_exact <- age(df$birth_date, exact = TRUE)# add age at millenium switchdf$age_at_y2k <- age(df$birth_date, "2000-01-01")df

Split Ages into Age Groups

Description

Split ages into age groups defined by thesplit argument. This allows for easier demographic (antimicrobial resistance) analysis. The function returns an orderedfactor.

Usage

age_groups(x, split_at = c(0, 12, 25, 55, 75), names = NULL,  na.rm = FALSE)

Arguments

Details

To split ages, the input for thesplit_at argument can be:

• Anumeric vector. A value of e.g.c(10, 20) will splitx on 0-9, 10-19 and 20+. A value of only50 will splitx on 0-49 and 50+.The default is to split on young children (0-11), youth (12-24), young adults (25-54), middle-aged adults (55-74) and elderly (75+).

• A character:

  • "children" or"kids", equivalent of:c(0, 1, 2, 4, 6, 13, 18). This will split on 0, 1, 2-3, 4-5, 6-12, 13-17 and 18+.

  • "elderly" or"seniors", equivalent of:c(65, 75, 85). This will split on 0-64, 65-74, 75-84, 85+.

  • "fives", equivalent of:1:20 * 5. This will split on 0-4, 5-9, ..., 95-99, 100+.

  • "tens", equivalent of:1:10 * 10. This will split on 0-9, 10-19, ..., 90-99, 100+.

Value

Orderedfactor

See Also

To determine ages, based on one or more reference dates, use theage() function.

Examples

ages <- c(3, 8, 16, 54, 31, 76, 101, 43, 21)# split into 0-49 and 50+age_groups(ages, 50)# split into 0-19, 20-49 and 50+age_groups(ages, c(20, 50))age_groups(ages, c(20, 50), names = c("Under 20 years", "20 to 50 years", "Over 50 years"))# split into groups of ten yearsage_groups(ages, 1:10 * 10)age_groups(ages, split_at = "tens")# split into groups of five yearsage_groups(ages, 1:20 * 5)age_groups(ages, split_at = "fives")# split specifically for childrenage_groups(ages, c(1, 2, 4, 6, 13, 18))age_groups(ages, "children")# resistance of ciprofloxacin per age groupif (require("dplyr") && require("ggplot2")) {  example_isolates %>%    filter_first_isolate() %>%    filter(mo == as.mo("Escherichia coli")) %>%    group_by(age_group = age_groups(age)) %>%    select(age_group, CIP) %>%    ggplot_sir(      x = "age_group",      minimum = 0,      x.title = "Age Group",      title = "Ciprofloxacin resistance per age group"    )}

Generate Traditional, Combination, Syndromic, or WISCA Antibiograms

Description

Create detailed antibiograms with options for traditional, combination, syndromic, and Bayesian WISCA methods.

Adhering to previously described approaches (seeSource) and especially the Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) by Bielickiet al., these functions provide flexible output formats including plots and tables, ideal for integration with R Markdown and Quarto reports.

Usage

antibiogram(x, antimicrobials = where(is.sir), mo_transform = "shortname",  ab_transform = "name", syndromic_group = NULL, add_total_n = FALSE,  only_all_tested = FALSE, digits = ifelse(wisca, 1, 0),  formatting_type = getOption("AMR_antibiogram_formatting_type",  ifelse(wisca, 14, 18)), col_mo = NULL, language = get_AMR_locale(),  minimum = 30, combine_SI = TRUE, sep = " + ", sort_columns = TRUE,  wisca = FALSE, simulations = 1000, conf_interval = 0.95,  interval_side = "two-tailed", info = interactive(), ...)wisca(x, antimicrobials = where(is.sir), ab_transform = "name",  syndromic_group = NULL, only_all_tested = FALSE, digits = 1,  formatting_type = getOption("AMR_antibiogram_formatting_type", 14),  col_mo = NULL, language = get_AMR_locale(), combine_SI = TRUE,  sep = " + ", sort_columns = TRUE, simulations = 1000,  conf_interval = 0.95, interval_side = "two-tailed",  info = interactive(), ...)retrieve_wisca_parameters(wisca_model, ...)## S3 method for class 'antibiogram'plot(x, ...)## S3 method for class 'antibiogram'autoplot(object, ...)## S3 method for class 'antibiogram'knit_print(x, italicise = TRUE,  na = getOption("knitr.kable.NA", default = ""), ...)

Arguments

Details

These functions return a table with values between 0 and 100 forsusceptibility, not resistance.

Remember that you should filter your data to let it contain only first isolates! This is needed to exclude duplicates and to reduce selection bias. Usefirst_isolate() to determine them with one of the four available algorithms: isolate-based, patient-based, episode-based, or phenotype-based.

For estimating antimicrobial coverage, especially when creating a WISCA, the outcome might become more reliable by only including the topn species encountered in the data. You can filter on this topn usingtop_n_microorganisms(). For example, usetop_n_microorganisms(your_data, n = 10) as a pre-processing step to only include the top 10 species in the data.

The numeric values of an antibiogram are stored in a long format as theattributelong_numeric. You can retrieve them usingattributes(x)$long_numeric, wherex is the outcome ofantibiogram() orwisca(). This is ideal for e.g. advanced plotting.

Formatting Type

The formatting of the 'cells' of the table can be set with the argumentformatting_type. In these examples,5 indicates the antimicrobial coverage (4-6 the confidence level),15 the number of susceptible isolates, and300 the number of tested (i.e., available) isolates:

1. 5

2. 15

3. 300

4. 15/300

5. 5 (300)

6. 5% (300)

7. 5 (N=300)

8. 5% (N=300)

9. 5 (15/300)

10. 5% (15/300)

11. 5 (N=15/300)

12. 5% (N=15/300)

13. 5 (4-6)

14. 5% (4-6%) -default for WISCA

15. 5 (4-6,300)

16. 5% (4-6%,300)

17. 5 (4-6,N=300)

18. 5% (4-6%,N=300) -default for non-WISCA

19. 5 (4-6,15/300)

20. 5% (4-6%,15/300)

21. 5 (4-6,N=15/300)

22. 5% (4-6%,N=15/300)

The default can be set globally with the package optionAMR_antibiogram_formatting_type, e.g.options(AMR_antibiogram_formatting_type = 5). Do note that for WISCA, the total numbers of tested and susceptible isolates are less useful to report, since these are included in the Bayesian model and apparent from the susceptibility and its confidence level.

Setdigits (defaults to0) to alter the rounding of the susceptibility percentages.

Antibiogram Types

There are various antibiogram types, as summarised by Klinkeret al. (2021,doi:10.1177/20499361211011373), and they are all supported byantibiogram().

For clinical coverage estimations,use WISCA whenever possible, since it provides more precise coverage estimates by accounting for pathogen incidence and antimicrobial susceptibility, as has been shown by Bielickiet al. (2020,doi:10.1001/jamanetworkopen.2019.21124). See the sectionExplaining WISCA on this page. Do note that WISCA is pathogen-agnostic, meaning that the outcome is not stratied by pathogen, but rather by syndrome.

1. Traditional Antibiogram

   Case example: Susceptibility ofPseudomonas aeruginosa to piperacillin/tazobactam (TZP)

   Code example:

   antibiogram(your_data,            antimicrobials = "TZP")

2. Combination Antibiogram

   Case example: Additional susceptibility ofPseudomonas aeruginosa to TZP + tobramycin versus TZP alone

   Code example:

   antibiogram(your_data,            antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))

3. Syndromic Antibiogram

   Case example: Susceptibility ofPseudomonas aeruginosa to TZP among respiratory specimens (obtained among ICU patients only)

   Code example:

   antibiogram(your_data,            antimicrobials = penicillins(),            syndromic_group = "ward")

4. Weighted-Incidence Syndromic Combination Antibiogram (WISCA)

   WISCA can be applied to any antibiogram, see the sectionExplaining WISCA on this page for more information.

   Code example:

   antibiogram(your_data,            antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),            wisca = TRUE)# this is equal to:wisca(your_data,      antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))

   WISCA uses a sophisticated Bayesian decision model to combine both local and pooled antimicrobial resistance data. This approach not only evaluates local patterns but can also draw on multi-centre datasets to improve regimen accuracy, even in low-incidence infections like paediatric bloodstream infections (BSIs).

Grouped tibbles

For any type of antibiogram, groupedtibbles can also be used to calculate susceptibilities over various groups.

Code example:

library(dplyr)your_data %>%  group_by(has_sepsis, is_neonate, sex) %>%  wisca(antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))

Stepped Approach for Clinical Insight

In clinical practice, antimicrobial coverage decisions evolve as more microbiological data becomes available. This theoretical stepped approach ensures empirical coverage can continuously assessed to improve patient outcomes:

1. Initial Empirical Therapy (Admission / Pre-Culture Data)

   At admission, no pathogen information is available.

   • Action: broad-spectrum coverage is based on local resistance patterns and syndromic antibiograms. Using the pathogen-agnostic yet incidence-weighted WISCA is preferred.

   • Code example:

     antibiogram(your_data,            antimicrobials = selected_regimens,            mo_transform = NA) # all pathogens set to `NA`# preferred: use WISCAwisca(your_data,      antimicrobials = selected_regimens)

2. Refinement with Gram Stain Results

   When a blood culture becomes positive, the Gram stain provides an initial and crucial first stratification (Gram-positive vs. Gram-negative).

   • Action: narrow coverage based on Gram stain-specific resistance patterns.

   • Code example:

     antibiogram(your_data,            antimicrobials = selected_regimens,            mo_transform = "gramstain") # all pathogens set to Gram-pos/Gram-neg

3. Definitive Therapy Based on Species Identification

   After cultivation of the pathogen, full pathogen identification allows precise targeting of therapy.

   • Action: adjust treatment to pathogen-specific antibiograms, minimizing resistance risks.

   • Code example:

     antibiogram(your_data,            antimicrobials = selected_regimens,            mo_transform = "shortname") # all pathogens set to 'G. species', e.g., E. coli

By structuring antibiograms around this stepped approach, clinicians can make data-driven adjustments at each stage, ensuring optimal empirical and targeted therapy while reducing unnecessary broad-spectrum antimicrobial use.

Inclusion in Combination Antibiograms

Note that for combination antibiograms, it is important to realise that susceptibility can be calculated in two ways, which can be set with theonly_all_tested argument (default isFALSE). See this example for two antimicrobials, Drug A and Drug B, about howantibiogram() works to calculate the %SI:

--------------------------------------------------------------------                    only_all_tested = FALSE  only_all_tested = TRUE                    -----------------------  ----------------------- Drug A    Drug B   considered   considered  considered   considered                    susceptible    tested    susceptible    tested--------  --------  -----------  ----------  -----------  ---------- S or I    S or I        X            X           X            X   R       S or I        X            X           X            X  <NA>     S or I        X            X           -            - S or I      R           X            X           X            X   R         R           -            X           -            X  <NA>       R           -            -           -            - S or I     <NA>         X            X           -            -   R        <NA>         -            -           -            -  <NA>      <NA>         -            -           -            ---------------------------------------------------------------------

Plotting

All types of antibiograms as listed above can be plotted (usingggplot2::autoplot() or baseR'splot() andbarplot()). As mentioned above, the numeric values of an antibiogram are stored in a long format as theattributelong_numeric. You can retrieve them usingattributes(x)$long_numeric, wherex is the outcome ofantibiogram() orwisca().

The outcome ofantibiogram() can also be used directly in R Markdown / Quarto (i.e.,knitr) for reports. In this case,knitr::kable() will be applied automatically and microorganism names will even be printed in italics at default (see argumentitalicise).

You can also use functions from specific 'table reporting' packages to transform the output ofantibiogram() to your needs, e.g. withflextable::as_flextable() orgt::gt().

Explaining WISCA

WISCA (Weighted-Incidence Syndromic Combination Antibiogram) estimates the probability of empirical coverage for combination regimens.

It weights susceptibility by pathogen prevalence within a clinical syndrome and provides credible intervals around the expected coverage.

For more background, interpretation, and examples, seethe WISCA vignette.

Author(s)

Implementation: Dr. Larisse Bolton and Dr. Matthijs Berends

Source

• Bielicki JAet al. (2016).Selecting appropriate empirical antibiotic regimens for paediatric bloodstream infections: application of a Bayesian decision model to local and pooled antimicrobial resistance surveillance dataJournal of Antimicrobial Chemotherapy 71(3);doi:10.1093/jac/dkv397

• Bielicki JAet al. (2020).Evaluation of the coverage of 3 antibiotic regimens for neonatal sepsis in the hospital setting across Asian countriesJAMA Netw Open. 3(2):e1921124;doi:10.1001/jamanetworkopen.2019.21124

• Klinker KPet al. (2021).Antimicrobial stewardship and antibiograms: importance of moving beyond traditional antibiograms.Therapeutic Advances in Infectious Disease, May 5;8:20499361211011373;doi:10.1177/20499361211011373

• Barbieri Eet al. (2021).Development of a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) to guide the choice of the empiric antibiotic treatment for urinary tract infection in paediatric patients: a Bayesian approachAntimicrobial Resistance & Infection Control May 1;10(1):74;doi:10.1186/s13756-021-00939-2

• M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition, 2022,Clinical and Laboratory Standards Institute (CLSI).https://clsi.org/standards/products/microbiology/documents/m39/.

Examples

# example_isolates is a data set available in the AMR package.# run ?example_isolates for more info.example_isolates# Traditional antibiogram ----------------------------------------------antibiogram(example_isolates,  antimicrobials = c(aminoglycosides(), carbapenems()))antibiogram(example_isolates,  antimicrobials = aminoglycosides(),  ab_transform = "atc",  mo_transform = "gramstain")antibiogram(example_isolates,  antimicrobials = carbapenems(),  ab_transform = "name",  mo_transform = "name")# Combined antibiogram -------------------------------------------------# combined antimicrobials yield higher empiric coverageantibiogram(example_isolates,  antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),  mo_transform = "gramstain")# you can use any antimicrobial selector with `+` too:antibiogram(example_isolates,  antimicrobials = ureidopenicillins() + c("", "GEN", "tobra"),  mo_transform = "gramstain")# names of antimicrobials do not need to resemble columns exactly:antibiogram(example_isolates,  antimicrobials = c("Cipro", "cipro + genta"),  mo_transform = "gramstain",  ab_transform = "name",  sep = " & ")# Syndromic antibiogram ------------------------------------------------# the data set could contain a filter for e.g. respiratory specimensantibiogram(example_isolates,  antimicrobials = c(aminoglycosides(), carbapenems()),  syndromic_group = "ward")# now define a data set with only E. coliex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]# with a custom language, though this will be determined automatically# (i.e., this table will be in Spanish on Spanish systems)antibiogram(ex1,  antimicrobials = aminoglycosides(),  ab_transform = "name",  syndromic_group = ifelse(ex1$ward == "ICU",    "UCI", "No UCI"  ),  language = "es")# WISCA antibiogram ----------------------------------------------------# WISCA are not stratified by species, but rather on syndromesantibiogram(example_isolates,  antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),  syndromic_group = "ward",  wisca = TRUE)# Print the output for R Markdown / Quarto -----------------------------ureido <- antibiogram(example_isolates,  antimicrobials = ureidopenicillins(),  syndromic_group = "ward",  wisca = TRUE)# in an Rmd file, you would just need to return `ureido` in a chunk,# but to be explicit here:if (requireNamespace("knitr")) {  cat(knitr::knit_print(ureido))}# Generate plots with ggplot2 or base R --------------------------------ab1 <- antibiogram(example_isolates,  antimicrobials = c("AMC", "CIP", "TZP", "TZP+TOB"),  mo_transform = "gramstain")ab2 <- antibiogram(example_isolates,  antimicrobials = c("AMC", "CIP", "TZP", "TZP+TOB"),  mo_transform = "gramstain",  syndromic_group = "ward")if (requireNamespace("ggplot2")) {  ggplot2::autoplot(ab1)}if (requireNamespace("ggplot2")) {  ggplot2::autoplot(ab2)}plot(ab1)plot(ab2)

Antimicrobial Selectors

Description

These functions allow for filtering rows and selecting columns based on antimicrobial test results that are of a specific antimicrobial class or group, without the need to define the columns or antimicrobial abbreviations. They can be used in baseR, tidyverse, tidymodels, anddata.table.

Simply puy, if you have a column name that resembles an antimicrobial drug, it will be picked up by any of these functions that matches its pharmaceutical class, code or name: column names "cefazolin", "kefzol", "CZO" and "J01DB04" would all be included in the following selection:

library(dplyr)my_data_with_all_these_columns %>%  select(cephalosporins())

Usage

aminoglycosides(only_sir_columns = FALSE, only_treatable = TRUE,  return_all = TRUE, ...)aminopenicillins(only_sir_columns = FALSE, return_all = TRUE, ...)antifungals(only_sir_columns = FALSE, return_all = TRUE, ...)antimycobacterials(only_sir_columns = FALSE, return_all = TRUE, ...)betalactams(only_sir_columns = FALSE, only_treatable = TRUE,  return_all = TRUE, ...)betalactams_with_inhibitor(only_sir_columns = FALSE, return_all = TRUE,  ...)carbapenems(only_sir_columns = FALSE, only_treatable = TRUE,  return_all = TRUE, ...)cephalosporins(only_sir_columns = FALSE, only_treatable = TRUE,  return_all = TRUE, ...)cephalosporins_1st(only_sir_columns = FALSE, return_all = TRUE, ...)cephalosporins_2nd(only_sir_columns = FALSE, return_all = TRUE, ...)cephalosporins_3rd(only_sir_columns = FALSE, only_treatable = TRUE,  return_all = TRUE, ...)cephalosporins_4th(only_sir_columns = FALSE, return_all = TRUE, ...)cephalosporins_5th(only_sir_columns = FALSE, return_all = TRUE, ...)fluoroquinolones(only_sir_columns = FALSE, only_treatable = TRUE,  return_all = TRUE, ...)glycopeptides(only_sir_columns = FALSE, return_all = TRUE, ...)isoxazolylpenicillins(only_sir_columns = FALSE, only_treatable = TRUE,  return_all = TRUE, ...)lincosamides(only_sir_columns = FALSE, only_treatable = TRUE,  return_all = TRUE, ...)lipoglycopeptides(only_sir_columns = FALSE, return_all = TRUE, ...)macrolides(only_sir_columns = FALSE, return_all = TRUE, ...)monobactams(only_sir_columns = FALSE, return_all = TRUE, ...)nitrofurans(only_sir_columns = FALSE, return_all = TRUE, ...)oxazolidinones(only_sir_columns = FALSE, return_all = TRUE, ...)penicillins(only_sir_columns = FALSE, return_all = TRUE, ...)phenicols(only_sir_columns = FALSE, return_all = TRUE, ...)polymyxins(only_sir_columns = FALSE, only_treatable = TRUE,  return_all = TRUE, ...)quinolones(only_sir_columns = FALSE, only_treatable = TRUE,  return_all = TRUE, ...)rifamycins(only_sir_columns = FALSE, return_all = TRUE, ...)streptogramins(only_sir_columns = FALSE, return_all = TRUE, ...)sulfonamides(only_sir_columns = FALSE, return_all = TRUE, ...)tetracyclines(only_sir_columns = FALSE, only_treatable = TRUE,  return_all = TRUE, ...)trimethoprims(only_sir_columns = FALSE, return_all = TRUE, ...)ureidopenicillins(only_sir_columns = FALSE, return_all = TRUE, ...)amr_class(amr_class, only_sir_columns = FALSE, only_treatable = TRUE,  return_all = TRUE, ...)amr_selector(filter, only_sir_columns = FALSE, only_treatable = TRUE,  return_all = TRUE, ...)administrable_per_os(only_sir_columns = FALSE, return_all = TRUE, ...)administrable_iv(only_sir_columns = FALSE, return_all = TRUE, ...)not_intrinsic_resistant(only_sir_columns = FALSE, col_mo = NULL,  version_expected_phenotypes = 1.2, ...)

Arguments

Details

These functions can be used in data set calls for selecting columns and filtering rows. They work with baseR, the Tidyverse, anddata.table. They are heavily inspired by theTidyverse selection helpers such aseverything(), but are not limited todplyr verbs. Nonetheless, they are very convenient to use withdplyr functions such asselect(),filter() andsummarise(), seeExamples.

All selectors can also be used intidymodels packages such asrecipe andparsnip. See for more infoour tutorial on using antimicrobial selectors for predictive modelling.

All columns in the data in which these functions are called will be searched for known antimicrobial names, abbreviations, brand names, and codes (ATC, EARS-Net, WHO, etc.) according to theantimicrobials data set. This means that a selector such asaminoglycosides() will pick up column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc.

Theamr_class() function can be used to filter/select on a manually defined antimicrobial class. It searches for results in theantimicrobials data set within the columnsgroup,atc_group1 andatc_group2.

Theadministrable_per_os() andadministrable_iv() functions also rely on theantimicrobials data set - antimicrobials will be matched where a DDD (defined daily dose) for resp. oral and IV treatment is available in theantimicrobials data set.

Theamr_selector() function can be used to internally filter theantimicrobials data set on any results, seeExamples. It allows for filtering on a (part of) a certain name, and/or a group name or even a minimum of DDDs for oral treatment. This function yields the highest flexibility, but is also the least user-friendly, since it requires a hard-coded filter to set.

Thenot_intrinsic_resistant() function can be used to only select antimicrobials that pose no intrinsic resistance for the microorganisms in the data set. For example, if a data set contains only microorganism codes or names ofE. coli andK. pneumoniae and contains a column "vancomycin", this column will be removed (or rather, unselected) using this function. It currently applies'EUCAST Expected Resistant Phenotypes' v1.2 (2023) to determine intrinsic resistance, using theeucast_rules() function internally. Because of this determination, this function is quite slow in terms of performance.

Value

When used inside selecting or filtering, this returns acharacter vector of column names, with additional class"amr_selector". When used individually, this returns an'ab' vector with all possible antimicrobials that the function would be able to select or filter.

Full list of supported (antimicrobial) classes

• aminoglycosides() can select:
  amikacin (AMK), amikacin/fosfomycin (AKF), apramycin (APR), arbekacin (ARB), astromicin (AST), bekanamycin (BEK), dibekacin (DKB), framycetin (FRM), gentamicin (GEN), gentamicin-high (GEH), habekacin (HAB), hygromycin (HYG), isepamicin (ISE), kanamycin (KAN), kanamycin-high (KAH), kanamycin/cephalexin (KAC), micronomicin (MCR), neomycin (NEO), netilmicin (NET), pentisomicin (PIM), plazomicin (PLZ), propikacin (PKA), ribostamycin (RST), sisomicin (SIS), streptoduocin (STR), streptomycin (STR1), streptomycin-high (STH), tobramycin (TOB), and tobramycin-high (TOH)

• aminopenicillins() can select:
  amoxicillin (AMX) and ampicillin (AMP)

• antifungals() can select:
  amorolfine (AMO), amphotericin B (AMB), amphotericin B-high (AMH), anidulafungin (ANI), butoconazole (BUT), caspofungin (CAS), ciclopirox (CIX), clotrimazole (CTR), econazole (ECO), fluconazole (FLU), flucytosine (FCT), fosfluconazole (FFL), griseofulvin (GRI), hachimycin (HCH), ibrexafungerp (IBX), isavuconazole (ISV), isoconazole (ISO), itraconazole (ITR), ketoconazole (KET), manogepix (MGX), micafungin (MIF), miconazole (MCZ), nystatin (NYS), oteseconazole (OTE), pimaricin (PMR), posaconazole (POS), rezafungin (RZF), ribociclib (RBC), sulconazole (SUC), terbinafine (TRB), terconazole (TRC), and voriconazole (VOR)

• antimycobacterials() can select:
  4-aminosalicylic acid (AMA), calcium aminosalicylate (CLA), capreomycin (CAP), clofazimine (CLF), delamanid (DLM), enviomycin (ENV), ethambutol (ETH), ethambutol/isoniazid (ETI), ethionamide (ETI1), isoniazid (INH), isoniazid/sulfamethoxazole/trimethoprim/pyridoxine (IST), morinamide (MRN), p-aminosalicylic acid (PAS), pretomanid (PMD), protionamide (PTH), pyrazinamide (PZA), rifabutin (RIB), rifampicin (RIF), rifampicin/ethambutol/isoniazid (REI), rifampicin/isoniazid (RFI), rifampicin/pyrazinamide/ethambutol/isoniazid (RPEI), rifampicin/pyrazinamide/isoniazid (RPI), rifamycin (RFM), rifapentine (RFP), sodium aminosalicylate (SDA), streptomycin/isoniazid (STI), terizidone (TRZ), thioacetazone (TAT), thioacetazone/isoniazid (THI1), tiocarlide (TCR), and viomycin (VIO)

• betalactams() can select:
  amoxicillin (AMX), amoxicillin/clavulanic acid (AMC), amoxicillin/sulbactam (AXS), ampicillin (AMP), ampicillin/sulbactam (SAM), apalcillin (APL), aspoxicillin (APX), azidocillin (AZD), azlocillin (AZL), aztreonam (ATM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), bacampicillin (BAM), benzathine benzylpenicillin (BNB), benzathine phenoxymethylpenicillin (BNP), benzylpenicillin (PEN), benzylpenicillin screening test (PEN-S), biapenem (BIA), carbenicillin (CRB), carindacillin (CRN), carumonam (CAR), cefacetrile (CAC), cefaclor (CEC), cefadroxil (CFR), cefalexin (LEX), cefaloridine (RID), cefalotin (CEP), cefamandole (MAN), cefapirin (HAP), cefatrizine (CTZ), cefazedone (CZD), cefazolin (CZO), cefcapene (CCP), cefcapene pivoxil (CCX), cefdinir (CDR), cefditoren (DIT), cefditoren pivoxil (DIX), cefepime (FEP), cefepime/amikacin (CFA), cefepime/clavulanic acid (CPC), cefepime/enmetazobactam (FPE), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefepime/zidebactam (FPZ), cefetamet (CAT), cefetamet pivoxil (CPI), cefetecol (CCL), cefetrizole (CZL), cefiderocol (FDC), cefixime (CFM), cefmenoxime (CMX), cefmetazole (CMZ), cefodizime (DIZ), cefonicid (CID), cefoperazone (CFP), cefoperazone/sulbactam (CSL), ceforanide (CND), cefoselis (CSE), cefotaxime (CTX), cefotaxime screening test (CTX-S), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotetan (CTT), cefotiam (CTF), cefotiam hexetil (CHE), cefovecin (FOV), cefoxitin (FOX), cefoxitin screening test (FOX-S), cefozopran (ZOP), cefpimizole (CFZ), cefpiramide (CPM), cefpirome (CPO), cefpodoxime (CPD), cefpodoxime proxetil (CPX), cefpodoxime/clavulanic acid (CDC), cefprozil (CPR), cefquinome (CEQ), cefroxadine (CRD), cefsulodin (CFS), cefsumide (CSU), ceftaroline (CPT), ceftaroline/avibactam (CPA), ceftazidime (CAZ), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), cefteram (CEM), cefteram pivoxil (CPL), ceftezole (CTL), ceftibuten (CTB), ceftiofur (TIO), ceftizoxime (CZX), ceftizoxime alapivoxil (CZP), ceftobiprole (BPR), ceftobiprole medocaril (CFM1), ceftolozane/tazobactam (CZT), ceftriaxone (CRO), ceftriaxone/beta-lactamase inhibitor (CEB), cefuroxime (CXM), cefuroxime axetil (CXA), cephradine (CED), ciclacillin (CIC), clometocillin (CLM), cloxacillin (CLO), dicloxacillin (DIC), doripenem (DOR), epicillin (EPC), ertapenem (ETP), flucloxacillin (FLC), hetacillin (HET), imipenem (IPM), imipenem/EDTA (IPE), imipenem/relebactam (IMR), latamoxef (LTM), lenampicillin (LEN), loracarbef (LOR), mecillinam (MEC), meropenem (MEM), meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), metampicillin (MTM), meticillin (MET), mezlocillin (MEZ), mezlocillin/sulbactam (MSU), nafcillin (NAF), oxacillin (OXA), oxacillin screening test (OXA-S), panipenem (PAN), penamecillin (PNM), penicillin/novobiocin (PNO), penicillin/sulbactam (PSU), pheneticillin (PHE), phenoxymethylpenicillin (PHN), piperacillin (PIP), piperacillin/sulbactam (PIS), piperacillin/tazobactam (TZP), piridicillin (PRC), pivampicillin (PVM), pivmecillinam (PME), procaine benzylpenicillin (PRB), propicillin (PRP), razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), sarmoxicillin (SRX), sulbenicillin (SBC), sultamicillin (SLT6), talampicillin (TAL), tebipenem (TBP), temocillin (TEM), ticarcillin (TIC), ticarcillin/clavulanic acid (TCC), and tigemonam (TMN)

• betalactams_with_inhibitor() can select:
  amoxicillin/clavulanic acid (AMC), amoxicillin/sulbactam (AXS), ampicillin/sulbactam (SAM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), cefepime/amikacin (CFA), cefepime/clavulanic acid (CPC), cefepime/enmetazobactam (FPE), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefepime/zidebactam (FPZ), cefoperazone/sulbactam (CSL), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefpodoxime/clavulanic acid (CDC), ceftaroline/avibactam (CPA), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), ceftolozane/tazobactam (CZT), ceftriaxone/beta-lactamase inhibitor (CEB), imipenem/relebactam (IMR), meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), mezlocillin/sulbactam (MSU), penicillin/novobiocin (PNO), penicillin/sulbactam (PSU), piperacillin/sulbactam (PIS), piperacillin/tazobactam (TZP), and ticarcillin/clavulanic acid (TCC)

• carbapenems() can select:
  biapenem (BIA), doripenem (DOR), ertapenem (ETP), imipenem (IPM), imipenem/EDTA (IPE), imipenem/relebactam (IMR), meropenem (MEM), meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), panipenem (PAN), razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), and tebipenem (TBP)

• cephalosporins() can select:
  cefacetrile (CAC), cefaclor (CEC), cefadroxil (CFR), cefalexin (LEX), cefaloridine (RID), cefalotin (CEP), cefamandole (MAN), cefapirin (HAP), cefatrizine (CTZ), cefazedone (CZD), cefazolin (CZO), cefcapene (CCP), cefcapene pivoxil (CCX), cefdinir (CDR), cefditoren (DIT), cefditoren pivoxil (DIX), cefepime (FEP), cefepime/amikacin (CFA), cefepime/clavulanic acid (CPC), cefepime/enmetazobactam (FPE), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefepime/zidebactam (FPZ), cefetamet (CAT), cefetamet pivoxil (CPI), cefetecol (CCL), cefetrizole (CZL), cefiderocol (FDC), cefixime (CFM), cefmenoxime (CMX), cefmetazole (CMZ), cefodizime (DIZ), cefonicid (CID), cefoperazone (CFP), cefoperazone/sulbactam (CSL), ceforanide (CND), cefoselis (CSE), cefotaxime (CTX), cefotaxime screening test (CTX-S), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotetan (CTT), cefotiam (CTF), cefotiam hexetil (CHE), cefovecin (FOV), cefoxitin (FOX), cefoxitin screening test (FOX-S), cefozopran (ZOP), cefpimizole (CFZ), cefpiramide (CPM), cefpirome (CPO), cefpodoxime (CPD), cefpodoxime proxetil (CPX), cefpodoxime/clavulanic acid (CDC), cefprozil (CPR), cefquinome (CEQ), cefroxadine (CRD), cefsulodin (CFS), cefsumide (CSU), ceftaroline (CPT), ceftaroline/avibactam (CPA), ceftazidime (CAZ), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), cefteram (CEM), cefteram pivoxil (CPL), ceftezole (CTL), ceftibuten (CTB), ceftiofur (TIO), ceftizoxime (CZX), ceftizoxime alapivoxil (CZP), ceftobiprole (BPR), ceftobiprole medocaril (CFM1), ceftolozane/tazobactam (CZT), ceftriaxone (CRO), ceftriaxone/beta-lactamase inhibitor (CEB), cefuroxime (CXM), cefuroxime axetil (CXA), cephradine (CED), latamoxef (LTM), and loracarbef (LOR)

• cephalosporins_1st() can select:
  cefacetrile (CAC), cefadroxil (CFR), cefalexin (LEX), cefaloridine (RID), cefalotin (CEP), cefapirin (HAP), cefatrizine (CTZ), cefazedone (CZD), cefazolin (CZO), cefroxadine (CRD), ceftezole (CTL), and cephradine (CED)

• cephalosporins_2nd() can select:
  cefaclor (CEC), cefamandole (MAN), cefmetazole (CMZ), cefonicid (CID), ceforanide (CND), cefotetan (CTT), cefotiam (CTF), cefoxitin (FOX), cefoxitin screening test (FOX-S), cefprozil (CPR), cefuroxime (CXM), cefuroxime axetil (CXA), and loracarbef (LOR)

• cephalosporins_3rd() can select:
  cefcapene (CCP), cefcapene pivoxil (CCX), cefdinir (CDR), cefditoren (DIT), cefditoren pivoxil (DIX), cefetamet (CAT), cefetamet pivoxil (CPI), cefixime (CFM), cefmenoxime (CMX), cefodizime (DIZ), cefoperazone (CFP), cefoperazone/sulbactam (CSL), cefotaxime (CTX), cefotaxime screening test (CTX-S), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotiam hexetil (CHE), cefovecin (FOV), cefpimizole (CFZ), cefpiramide (CPM), cefpodoxime (CPD), cefpodoxime proxetil (CPX), cefpodoxime/clavulanic acid (CDC), cefsulodin (CFS), ceftazidime (CAZ), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), cefteram (CEM), cefteram pivoxil (CPL), ceftibuten (CTB), ceftiofur (TIO), ceftizoxime (CZX), ceftizoxime alapivoxil (CZP), ceftriaxone (CRO), ceftriaxone/beta-lactamase inhibitor (CEB), and latamoxef (LTM)

• cephalosporins_4th() can select:
  cefepime (FEP), cefepime/amikacin (CFA), cefepime/clavulanic acid (CPC), cefepime/enmetazobactam (FPE), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefepime/zidebactam (FPZ), cefetecol (CCL), cefoselis (CSE), cefozopran (ZOP), cefpirome (CPO), and cefquinome (CEQ)

• cephalosporins_5th() can select:
  ceftaroline (CPT), ceftaroline/avibactam (CPA), ceftobiprole (BPR), ceftobiprole medocaril (CFM1), and ceftolozane/tazobactam (CZT)

• fluoroquinolones() can select:
  besifloxacin (BES), ciprofloxacin (CIP), ciprofloxacin/metronidazole (CIM), ciprofloxacin/ornidazole (CIO), ciprofloxacin/tinidazole (CIT), clinafloxacin (CLX), danofloxacin (DAN), delafloxacin (DFX), difloxacin (DIF), enoxacin (ENX), enrofloxacin (ENR), finafloxacin (FIN), fleroxacin (FLE), garenoxacin (GRN), gatifloxacin (GAT), gemifloxacin (GEM), grepafloxacin (GRX), lascufloxacin (LSC), levofloxacin (LVX), levofloxacin/ornidazole (LEO), levonadifloxacin (LND), lomefloxacin (LOM), marbofloxacin (MAR), metioxate (MXT), miloxacin (MIL), moxifloxacin (MFX), nadifloxacin (NAD), nemonoxacin (NEM), nifuroquine (NIF), nitroxoline (NTR), norfloxacin (NOR), norfloxacin screening test (NOR-S), norfloxacin/metronidazole (NME), norfloxacin/tinidazole (NTI), ofloxacin (OFX), ofloxacin/ornidazole (OOR), orbifloxacin (ORB), pazufloxacin (PAZ), pefloxacin (PEF), pefloxacin screening test (PEF-S), pradofloxacin (PRA), premafloxacin (PRX), prulifloxacin (PRU), rufloxacin (RFL), sarafloxacin (SAR), sitafloxacin (SIT), sparfloxacin (SPX), temafloxacin (TMX), tilbroquinol (TBQ), tioxacin (TXC), tosufloxacin (TFX), and trovafloxacin (TVA)

• glycopeptides() can select:
  avoparcin (AVO), bleomycin (BLM), dalbavancin (DAL), norvancomycin (NVA), oritavancin (ORI), ramoplanin (RAM), teicoplanin (TEC), teicoplanin-macromethod (TCM), telavancin (TLV), vancomycin (VAN), and vancomycin-macromethod (VAM)

• isoxazolylpenicillins() can select:
  cloxacillin (CLO), dicloxacillin (DIC), flucloxacillin (FLC), meticillin (MET), oxacillin (OXA), and oxacillin screening test (OXA-S)

• lincosamides() can select:
  clindamycin (CLI), lincomycin (LIN), and pirlimycin (PRL)

• lipoglycopeptides() can select:
  dalbavancin (DAL), oritavancin (ORI), and telavancin (TLV)

• macrolides() can select:
  acetylmidecamycin (ACM), acetylspiramycin (ASP), azithromycin (AZM), clarithromycin (CLR), dirithromycin (DIR), erythromycin (ERY), flurithromycin (FLR1), gamithromycin (GAM), josamycin (JOS), kitasamycin (KIT), meleumycin (MEL), midecamycin (MID), miocamycin (MCM), nafithromycin (ZWK), oleandomycin (OLE), rokitamycin (ROK), roxithromycin (RXT), solithromycin (SOL), spiramycin (SPI), telithromycin (TLT), tildipirosin (TIP), tilmicosin (TIL), troleandomycin (TRL), tulathromycin (TUL), tylosin (TYL), and tylvalosin (TYL1)

• monobactams() can select:
  aztreonam (ATM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), carumonam (CAR), and tigemonam (TMN)

• nitrofurans() can select:
  furazidin (FUR), furazolidone (FRZ), nifurtoinol (NFR), nitrofurantoin (NIT), and nitrofurazone (NIZ)

• oxazolidinones() can select:
  cadazolid (CDZ), cycloserine (CYC), linezolid (LNZ), tedizolid (TZD), and thiacetazone (THA)

• penicillins() can select:
  amoxicillin (AMX), amoxicillin/clavulanic acid (AMC), amoxicillin/sulbactam (AXS), ampicillin (AMP), ampicillin/sulbactam (SAM), apalcillin (APL), aspoxicillin (APX), azidocillin (AZD), azlocillin (AZL), bacampicillin (BAM), benzathine benzylpenicillin (BNB), benzathine phenoxymethylpenicillin (BNP), benzylpenicillin (PEN), benzylpenicillin screening test (PEN-S), carbenicillin (CRB), carindacillin (CRN), ciclacillin (CIC), clometocillin (CLM), cloxacillin (CLO), dicloxacillin (DIC), epicillin (EPC), flucloxacillin (FLC), hetacillin (HET), lenampicillin (LEN), mecillinam (MEC), metampicillin (MTM), meticillin (MET), mezlocillin (MEZ), mezlocillin/sulbactam (MSU), nafcillin (NAF), oxacillin (OXA), oxacillin screening test (OXA-S), penamecillin (PNM), penicillin/novobiocin (PNO), penicillin/sulbactam (PSU), pheneticillin (PHE), phenoxymethylpenicillin (PHN), piperacillin (PIP), piperacillin/sulbactam (PIS), piperacillin/tazobactam (TZP), piridicillin (PRC), pivampicillin (PVM), pivmecillinam (PME), procaine benzylpenicillin (PRB), propicillin (PRP), sarmoxicillin (SRX), sulbenicillin (SBC), sultamicillin (SLT6), talampicillin (TAL), temocillin (TEM), ticarcillin (TIC), and ticarcillin/clavulanic acid (TCC)

• phenicols() can select:
  chloramphenicol (CHL), florfenicol (FLR), and thiamphenicol (THI)

• polymyxins() can select:
  colistin (COL), polymyxin B (PLB), and polymyxin B/polysorbate 80 (POP)

• quinolones() can select:
  besifloxacin (BES), cinoxacin (CIN), ciprofloxacin (CIP), ciprofloxacin/metronidazole (CIM), ciprofloxacin/ornidazole (CIO), ciprofloxacin/tinidazole (CIT), clinafloxacin (CLX), danofloxacin (DAN), delafloxacin (DFX), difloxacin (DIF), enoxacin (ENX), enrofloxacin (ENR), finafloxacin (FIN), fleroxacin (FLE), flumequine (FLM), garenoxacin (GRN), gatifloxacin (GAT), gemifloxacin (GEM), grepafloxacin (GRX), lascufloxacin (LSC), levofloxacin (LVX), levofloxacin/ornidazole (LEO), levonadifloxacin (LND), lomefloxacin (LOM), marbofloxacin (MAR), metioxate (MXT), miloxacin (MIL), moxifloxacin (MFX), nadifloxacin (NAD), nalidixic acid (NAL), nalidixic acid screening test (NAL-S), nemonoxacin (NEM), nifuroquine (NIF), nitroxoline (NTR), norfloxacin (NOR), norfloxacin screening test (NOR-S), norfloxacin/metronidazole (NME), norfloxacin/tinidazole (NTI), ofloxacin (OFX), ofloxacin/ornidazole (OOR), orbifloxacin (ORB), oxolinic acid (OXO), pazufloxacin (PAZ), pefloxacin (PEF), pefloxacin screening test (PEF-S), pipemidic acid (PPA), piromidic acid (PIR), pradofloxacin (PRA), premafloxacin (PRX), prulifloxacin (PRU), rosoxacin (ROS), rufloxacin (RFL), sarafloxacin (SAR), sitafloxacin (SIT), sparfloxacin (SPX), temafloxacin (TMX), tilbroquinol (TBQ), tioxacin (TXC), tosufloxacin (TFX), and trovafloxacin (TVA)

• rifamycins() can select:
  rifabutin (RIB), rifampicin (RIF), rifampicin/ethambutol/isoniazid (REI), rifampicin/isoniazid (RFI), rifampicin/pyrazinamide/ethambutol/isoniazid (RPEI), rifampicin/pyrazinamide/isoniazid (RPI), rifamycin (RFM), and rifapentine (RFP)

• streptogramins() can select:
  pristinamycin (PRI) and quinupristin/dalfopristin (QDA)

• sulfonamides() can select:
  brodimoprim (BDP), sulfadiazine (SDI), sulfadiazine/tetroxoprim (SLT), sulfadimethoxine (SUD), sulfadimidine (SDM), sulfafurazole (SLF), sulfaisodimidine (SLF1), sulfalene (SLF2), sulfamazone (SZO), sulfamerazine (SLF3), sulfamethizole (SLF4), sulfamethoxazole (SMX), sulfamethoxypyridazine (SLF5), sulfametomidine (SLF6), sulfametoxydiazine (SLF7), sulfamoxole (SLF8), sulfanilamide (SLF9), sulfaperin (SLF10), sulfaphenazole (SLF11), sulfapyridine (SLF12), sulfathiazole (SUT), and sulfathiourea (SLF13)

• tetracyclines() can select:
  cetocycline (CTO), chlortetracycline (CTE), clomocycline (CLM1), demeclocycline (DEM), doxycycline (DOX), eravacycline (ERV), lymecycline (LYM), metacycline (MTC), minocycline (MNO), omadacycline (OMC), oxytetracycline (OXY), penimepicycline (PNM1), rolitetracycline (RLT), sarecycline (SRC), tetracycline (TCY), tetracycline screening test (TCY-S), and tigecycline (TGC)

• trimethoprims() can select:
  brodimoprim (BDP), sulfadiazine (SDI), sulfadiazine/tetroxoprim (SLT), sulfadiazine/trimethoprim (SLT1), sulfadimethoxine (SUD), sulfadimidine (SDM), sulfadimidine/trimethoprim (SLT2), sulfafurazole (SLF), sulfaisodimidine (SLF1), sulfalene (SLF2), sulfamazone (SZO), sulfamerazine (SLF3), sulfamerazine/trimethoprim (SLT3), sulfamethizole (SLF4), sulfamethoxazole (SMX), sulfamethoxypyridazine (SLF5), sulfametomidine (SLF6), sulfametoxydiazine (SLF7), sulfametrole/trimethoprim (SLT4), sulfamoxole (SLF8), sulfamoxole/trimethoprim (SLT5), sulfanilamide (SLF9), sulfaperin (SLF10), sulfaphenazole (SLF11), sulfapyridine (SLF12), sulfathiazole (SUT), sulfathiourea (SLF13), trimethoprim (TMP), and trimethoprim/sulfamethoxazole (SXT)

• ureidopenicillins() can select:
  azlocillin (AZL), mezlocillin (MEZ), piperacillin (PIP), and piperacillin/tazobactam (TZP)

Download Our Reference Data

All reference data sets in the AMR package - including information on microorganisms, antimicrobials, and clinical breakpoints - are freely available for download in multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, and Stata.

For maximum compatibility, we also provide machine-readable, tab-separated plain text files suitable for use in any software, including laboratory information systems.

Visitour website for direct download links, or explore the actual files inour GitHub repository.

Examples

# `example_isolates` is a data set available in the AMR package.# See ?example_isolates.example_isolates# you can use the selectors separately to retrieve all possible antimicrobials:carbapenems()# Though they are primarily intended to use for selections and filters.# Examples sections below are split into 'dplyr', 'base R', and 'data.table':## Not run: # dplyr -------------------------------------------------------------------library(dplyr, warn.conflicts = FALSE)example_isolates %>% select(carbapenems())# select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB'example_isolates %>% select(mo, aminoglycosides())# you can combine selectors like you are used with tidyverse# e.g., for betalactams, but not the ones with an enzyme inhibitor:example_isolates %>% select(betalactams(), -betalactams_with_inhibitor())# select only antimicrobials with DDDs for oral treatmentexample_isolates %>% select(administrable_per_os())# get AMR for all aminoglycosides e.g., per ward:example_isolates %>%  group_by(ward) %>%  summarise(across(aminoglycosides(),                   resistance))# You can combine selectors with '&' to be more specific:example_isolates %>%  select(penicillins() & administrable_per_os())# get AMR for only drugs that matter - no intrinsic resistance:example_isolates %>%  filter(mo_genus() %in% c("Escherichia", "Klebsiella")) %>%  group_by(ward) %>%  summarise_at(not_intrinsic_resistant(),               resistance)# get susceptibility for antimicrobials whose name contains "trim":example_isolates %>%  filter(first_isolate()) %>%  group_by(ward) %>%  summarise(across(amr_selector(name %like% "trim"), susceptibility))# this will select columns 'IPM' (imipenem) and 'MEM' (meropenem):example_isolates %>%  select(carbapenems())# this will select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB':example_isolates %>%  select(mo, aminoglycosides())# any() and all() work in dplyr's filter() too:example_isolates %>%  filter(    any(aminoglycosides() == "R"),    all(cephalosporins_2nd() == "R")  )# also works with c():example_isolates %>%  filter(any(c(carbapenems(), aminoglycosides()) == "R"))# not setting any/all will automatically apply all():example_isolates %>%  filter(aminoglycosides() == "R")# this will select columns 'mo' and all antimycobacterial drugs ('RIF'):example_isolates %>%  select(mo, amr_class("mycobact"))# get bug/drug combinations for only glycopeptides in Gram-positives:example_isolates %>%  filter(mo_is_gram_positive()) %>%  select(mo, glycopeptides()) %>%  bug_drug_combinations() %>%  format()data.frame(  some_column = "some_value",  J01CA01 = "S") %>% # ATC code of ampicillin  select(penicillins()) # only the 'J01CA01' column will be selected# with recent versions of dplyr, this is all equal:x <- example_isolates[carbapenems() == "R", ]y <- example_isolates %>% filter(carbapenems() == "R")z <- example_isolates %>% filter(if_all(carbapenems(), ~ .x == "R"))identical(x, y) && identical(y, z)## End(Not run)# base R ------------------------------------------------------------------# select columns 'IPM' (imipenem) and 'MEM' (meropenem)example_isolates[, carbapenems()]# select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB'example_isolates[, c("mo", aminoglycosides())]# select only antimicrobials with DDDs for oral treatmentexample_isolates[, administrable_per_os()]# filter using any() or all()example_isolates[any(carbapenems() == "R"), ]subset(example_isolates, any(carbapenems() == "R"))# filter on any or all results in the carbapenem columns (i.e., IPM, MEM):example_isolates[any(carbapenems()), ]example_isolates[all(carbapenems()), ]# filter with multiple antimicrobial selectors using c()example_isolates[all(c(carbapenems(), aminoglycosides()) == "R"), ]# filter + select in one go: get penicillins in carbapenem-resistant strainsexample_isolates[any(carbapenems() == "R"), penicillins()]# You can combine selectors with '&' to be more specific. For example,# penicillins() would select benzylpenicillin ('peni G') and# administrable_per_os() would select erythromycin. Yet, when combined these# drugs are both omitted since benzylpenicillin is not administrable per os# and erythromycin is not a penicillin:example_isolates[, penicillins() & administrable_per_os()]# amr_selector() applies a filter in the `antimicrobials` data set and is thus# very flexible. For instance, to select antimicrobials with an oral DDD# of at least 1 gram:example_isolates[, amr_selector(oral_ddd > 1 & oral_units == "g")]# data.table --------------------------------------------------------------# data.table is supported as well, just use it in the same way as with# base R, but add `with = FALSE` if using a single AB selector.if (require("data.table")) {  dt <- as.data.table(example_isolates)  # this does not work, it returns column *names*  dt[, carbapenems()]}if (require("data.table")) {  # so `with = FALSE` is required  dt[, carbapenems(), with = FALSE]}# for multiple selections or AB selectors, `with = FALSE` is not needed:if (require("data.table")) {  dt[, c("mo", aminoglycosides())]}if (require("data.table")) {  dt[, c(carbapenems(), aminoglycosides())]}# row filters are also supported:if (require("data.table")) {  dt[any(carbapenems() == "S"), ]}if (require("data.table")) {  dt[any(carbapenems() == "S"), penicillins(), with = FALSE]}

Data Sets with 616 Antimicrobial Drugs

Description

Two data sets containing all antimicrobials and antivirals. Useas.ab() or one of theab_* functions to retrieve values from theantimicrobials data set. Three identifiers are included in this data set: an antimicrobial ID (ab, primarily used in this package) as defined by WHONET/EARS-Net, an ATC code (atc) as defined by the WHO, and a Compound ID (cid) as found in PubChem. Other properties in this data set are derived from one or more of these codes. Note that some drugs have multiple ATC codes.

Theantibiotics data set has been renamed toantimicrobials. The old name will be removed in a future version.

Usage

antimicrobialsantibioticsantivirals

Format

For theantimicrobials data set: atibble with 496 observations and 14 variables:

• ab
  antimicrobial ID as used in this package (such asAMC), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available.This is a unique identifier.

• cid
  Compound ID as found in PubChem.This is a unique identifier.

• name
  Official name as used by WHONET/EARS-Net or the WHO.This is a unique identifier.

• group
  A short and concise group name, based on WHONET and WHOCC definitions

• atc
  ATC codes (Anatomical Therapeutic Chemical) as defined by the WHOCC, likeJ01CR02 (last updated May 4th, 2025):

• atc_group1
  Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC, like"Macrolides, lincosamides and streptogramins"

• atc_group2
  Official chemical subgroup (4th level ATC code) as defined by the WHOCC, like"Macrolides"

• abbr
  List of abbreviations as used in many countries, also for antimicrobial susceptibility testing (AST)

• synonyms
  Synonyms (often trade names) of a drug, as found in PubChem based on their compound ID

ATC properties (last updated May 4th, 2025):

• oral_ddd
  Defined Daily Dose (DDD), oral treatment, currently available for 180 drugs

• oral_units
  Units oforal_ddd

• iv_ddd
  Defined Daily Dose (DDD), parenteral (intravenous) treatment, currently available for 153 drugs

• iv_units
  Units ofiv_ddd

LOINC:

• loinc
  All codes associated with the name of the antimicrobial drug from Logical Observation Identifiers Names and Codes (LOINC), Version 2.76 (18 September, 2023). Useab_loinc() to retrieve them quickly, seeab_property().

For theantivirals data set: atibble with 120 observations and 11 variables:

• av
  Antiviral ID as used in this package (such asACI), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available.This is a unique identifier. Combinations are codes that contain a+ to indicate this, such asATA+COBI for atazanavir/cobicistat.

• name
  Official name as used by WHONET/EARS-Net or the WHO.This is a unique identifier.

• atc
  ATC codes (Anatomical Therapeutic Chemical) as defined by the WHOCC, seeDetails

• cid
  Compound ID as found in PubChem.This is a unique identifier.

• atc_group
  Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC

• synonyms
  Synonyms (often trade names) of a drug, as found in PubChem based on their compound ID

• oral_ddd
  Defined Daily Dose (DDD), oral treatment

• oral_units
  Units oforal_ddd

• iv_ddd
  Defined Daily Dose (DDD), parenteral treatment

• iv_units
  Units ofiv_ddd

• loinc
  All codes associated with the name of the antiviral drug from Logical Observation Identifiers Names and Codes (LOINC), Version 2.76 (18 September, 2023). Useav_loinc() to retrieve them quickly, seeav_property().

An object of classdeprecated_amr_dataset (inherits fromtbl_df,tbl,data.frame) with 496 rows and 14 columns.

An object of classtbl_df (inherits fromtbl,data.frame) with 120 rows and 11 columns.

Details

Properties that are based on an ATC code are only available when an ATC is available. These properties are:atc_group1,atc_group2,oral_ddd,oral_units,iv_ddd andiv_units. Do note that ATC codes are not unique. For example, J01CR02 is officially the ATC code for "amoxicillin and beta-lactamase inhibitor". Consequently, these two items from theantimicrobials data set both return"J01CR02":

ab_atc("amoxicillin/clavulanic acid")ab_atc("amoxicillin/sulbactam")

Synonyms (i.e. trade names) were derived from the PubChem Compound ID (columncid) and are consequently only available where a CID is available.

Download Our Reference Data

All reference data sets in the AMR package - including information on microorganisms, antimicrobials, and clinical breakpoints - are freely available for download in multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, and Stata.

For maximum compatibility, we also provide machine-readable, tab-separated plain text files suitable for use in any software, including laboratory information systems.

Visitour website for direct download links, or explore the actual files inour GitHub repository.

WHOCC

This package containsall ~550 antibiotic, antimycotic and antiviral drugs and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC,https://atcddd.fhi.no) and the Pharmaceuticals Community Register of the European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm).

These have become the gold standard for international drug utilisation monitoring and research.

The WHOCC is located in Oslo at the Norwegian Institute of Public Health and funded by the Norwegian government. The European Commission is the executive of the European Union and promotes its general interest.

NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. Seehttps://atcddd.fhi.no/copyright_disclaimer/.

Source

• WHO Collaborating Centre for Drug Statistics Methodology, Guidelines for ATC classification and DDD assignment, Oslo Accessed fromhttps://atcddd.fhi.no/atc_ddd_index/ on May 4th, 2025.

• Logical Observation Identifiers Names and Codes (LOINC), Version 2.76 (18 September, 2023). Accessed fromhttps://loinc.org on October 19th, 2023.

• European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER:https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm

See Also

microorganisms,intrinsic_resistant

Examples

antimicrobialsantivirals

Transform Input to an Antibiotic ID

Description

Use this function to determine the antimicrobial drug code of one or more antimicrobials. The data setantimicrobials will be searched for abbreviations, official names and synonyms (brand names).

Usage

as.ab(x, flag_multiple_results = TRUE, language = get_AMR_locale(),  info = interactive(), ...)is.ab(x)ab_reset_session()

Arguments

Details

All entries in theantimicrobials data set have three different identifiers: a human readable EARS-Net code (columnab, used by ECDC and WHONET), an ATC code (columnatc, used by WHO), and a CID code (columncid, Compound ID, used by PubChem). The data set contains more than 5,000 official brand names from many different countries, as found in PubChem. Not that some drugs contain multiple ATC codes.

All these properties will be searched for the user input. Theas.ab() can correct for different forms of misspelling:

• Wrong spelling of drug names (such as "tobramicin" or "gentamycin"), which corrects for most audible similarities such as f/ph, x/ks, c/z/s, t/th, etc.

• Too few or too many vowels or consonants

• Switching two characters (such as "mreopenem", often the case in clinical data, when doctors typed too fast)

• Digitalised paper records, leaving artefacts like 0/o/O (zero and O's), B/8, n/r, etc.

Use theab_* functions to get properties based on the returned antibiotic ID, seeExamples.

Note: theas.ab() andab_* functions may use very long regular expression to match brand names of antimicrobial drugs. This may fail on some systems.

You can add your own manual codes to be considered byas.ab() and allab_* functions, seeadd_custom_antimicrobials().

Value

Acharactervector with additional classab

Source

World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology:https://atcddd.fhi.no/atc_ddd_index/

European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER:https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm

WHOCC

This package containsall ~550 antibiotic, antimycotic and antiviral drugs and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC,https://atcddd.fhi.no) and the Pharmaceuticals Community Register of the European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm).

These have become the gold standard for international drug utilisation monitoring and research.

The WHOCC is located in Oslo at the Norwegian Institute of Public Health and funded by the Norwegian government. The European Commission is the executive of the European Union and promotes its general interest.

NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. Seehttps://atcddd.fhi.no/copyright_disclaimer/.

Download Our Reference Data

All reference data sets in the AMR package - including information on microorganisms, antimicrobials, and clinical breakpoints - are freely available for download in multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, and Stata.

For maximum compatibility, we also provide machine-readable, tab-separated plain text files suitable for use in any software, including laboratory information systems.

Visitour website for direct download links, or explore the actual files inour GitHub repository.

See Also

• antimicrobials for thedata.frame that is being used to determine ATCs

• ab_from_text() for a function to retrieve antimicrobial drugs from clinical text (from health care records)

Examples

# these examples all return "ERY", the ID of erythromycin:as.ab("J01FA01")as.ab("J 01 FA 01")as.ab("Erythromycin")as.ab("eryt")as.ab("ERYT")as.ab("ERY")as.ab("eritromicine") # spelled wrong, yet worksas.ab("Erythrocin") # trade name# spelling from different languages and dyslexia are no problemab_atc("ceftriaxon")ab_atc("cephtriaxone") # small spelling errorab_atc("cephthriaxone") # or a bit more severeab_atc("seephthriaaksone") # and even this works# use ab_* functions to get a specific properties (see ?ab_property);# they use as.ab() internally:ab_name("J01FA01")ab_name("eryt")if (require("dplyr")) {  # you can quickly rename 'sir' columns using set_ab_names() with dplyr:  example_isolates %>%    set_ab_names(where(is.sir), property = "atc")}

Transform Input to an Antiviral Drug ID

Description

Use this function to determine the antiviral drug code of one or more antiviral drugs. The data setantivirals will be searched for abbreviations, official names and synonyms (brand names).

Usage

as.av(x, flag_multiple_results = TRUE, info = interactive(), ...)is.av(x)

Arguments

Details

All entries in theantivirals data set have three different identifiers: a human readable EARS-Net code (columnab, used by ECDC and WHONET), an ATC code (columnatc, used by WHO), and a CID code (columncid, Compound ID, used by PubChem). The data set contains more than 5,000 official brand names from many different countries, as found in PubChem. Not that some drugs contain multiple ATC codes.

All these properties will be searched for the user input. Theas.av() can correct for different forms of misspelling:

• Wrong spelling of drug names (such as "acyclovir"), which corrects for most audible similarities such as f/ph, x/ks, c/z/s, t/th, etc.

• Too few or too many vowels or consonants

• Switching two characters (such as "aycclovir", often the case in clinical data, when doctors typed too fast)

• Digitalised paper records, leaving artefacts like 0/o/O (zero and O's), B/8, n/r, etc.

Use theav_* functions to get properties based on the returned antiviral drug ID, seeExamples.

Note: theas.av() andav_* functions may use very long regular expression to match brand names of antimicrobial drugs. This may fail on some systems.

Value

Acharactervector with additional classab

Source

World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology:https://atcddd.fhi.no/atc_ddd_index/

European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER:https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm

WHOCC

This package containsall ~550 antibiotic, antimycotic and antiviral drugs and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC,https://atcddd.fhi.no) and the Pharmaceuticals Community Register of the European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm).

These have become the gold standard for international drug utilisation monitoring and research.

The WHOCC is located in Oslo at the Norwegian Institute of Public Health and funded by the Norwegian government. The European Commission is the executive of the European Union and promotes its general interest.

NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. Seehttps://atcddd.fhi.no/copyright_disclaimer/.

Download Our Reference Data

All reference data sets in the AMR package - including information on microorganisms, antimicrobials, and clinical breakpoints - are freely available for download in multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, and Stata.

For maximum compatibility, we also provide machine-readable, tab-separated plain text files suitable for use in any software, including laboratory information systems.

Visitour website for direct download links, or explore the actual files inour GitHub repository.

See Also

• antivirals for thedata.frame that is being used to determine ATCs

• av_from_text() for a function to retrieve antimicrobial drugs from clinical text (from health care records)

Examples

# these examples all return "ACI", the ID of aciclovir:as.av("J05AB01")as.av("J 05 AB 01")as.av("Aciclovir")as.av("aciclo")as.av("   aciclo 123")as.av("ACICL")as.av("ACI")as.av("Virorax") # trade nameas.av("Zovirax") # trade nameas.av("acyklofir") # severe spelling error, yet works# use av_* functions to get a specific properties (see ?av_property);# they use as.av() internally:av_name("J05AB01")av_name("acicl")

Transform Input to Disk Diffusion Diameters

Description

This transforms a vector to a new classdisk, which is a disk diffusion growth zone size (around an antibiotic disk) in millimetres between 0 and 50.

Usage

as.disk(x, na.rm = FALSE)NA_disk_is.disk(x)

Arguments

Format

An object of classdisk (inherits frominteger) of length 1.

Details

Interpret disk values as SIR values withas.sir(). It supports guidelines from EUCAST and CLSI.

Disk diffusion growth zone sizes must be between 0 and 50 millimetres. Values higher than 50 but lower than 100 will be maximised to 50. All others input values outside the 0-50 range will returnNA.

NA_disk_ is a missing value of the newdisk class.

Value

Aninteger with additional classdisk

See Also

as.sir()

Examples

# transform existing disk zones to the `disk` class (using base R)df <- data.frame(  microorganism = "Escherichia coli",  AMP = 20,  CIP = 14,  GEN = 18,  TOB = 16)df[, 2:5] <- lapply(df[, 2:5], as.disk)str(df)# transforming is easier with dplyr:if (require("dplyr")) {  df %>% mutate(across(AMP:TOB, as.disk))}# interpret disk values, see ?as.siras.sir(  x = as.disk(18),  mo = "Strep pneu", # `mo` will be coerced with as.mo()  ab = "ampicillin", # and `ab` with as.ab()  guideline = "EUCAST")# interpret whole data set, pretend to be all from urinary tract infections:as.sir(df, uti = TRUE)

Transform Input to Minimum Inhibitory Concentrations (MIC)

Description

This transforms vectors to a new classmic, which treats the input as decimal numbers, while maintaining operators (such as ">=") and only allowing valid MIC values known to the field of (medical) microbiology.

Usage

as.mic(x, na.rm = FALSE, keep_operators = "all")is.mic(x)NA_mic_rescale_mic(x, mic_range, keep_operators = "edges", as.mic = TRUE)mic_p50(x, na.rm = FALSE, ...)mic_p90(x, na.rm = FALSE, ...)## S3 method for class 'mic'droplevels(x, as.mic = FALSE, ...)

Arguments

Details

To interpret MIC values as SIR values, useas.sir() on MIC values. It supports guidelines from EUCAST (2011-2025) and CLSI (2011-2025).

This class for MIC values is a quite a special data type: formally it is an orderedfactor with valid MIC values asfactor levels (to make sure only valid MIC values are retained), but for any mathematical operation it acts as decimal numbers:

x <- random_mic(10)x#> Class 'mic'#>  [1] 16     1      8      8      64     >=128  0.0625 32     32     16is.factor(x)#> [1] TRUEx[1] * 2#> [1] 32median(x)#> [1] 26

This makes it possible to maintain operators that often come with MIC values, such ">=" and "<=", even when filtering usingnumeric values in data analysis, e.g.:

x[x > 4]#> Class 'mic'#> [1] 16    8     8     64    >=128 32    32    16df <- data.frame(x, hospital = "A")subset(df, x > 4) # or with dplyr: df %>% filter(x > 4)#>        x hospital#> 1     16        A#> 5     64        A#> 6  >=128        A#> 8     32        A#> 9     32        A#> 10    16        A

All so-calledgroup generic functions are implemented for the MIC class (such as!,!=,<,>=,exp(),log2()). Some mathematical functions are also implemented (such asquantile(),median(),fivenum()). Sincesd() andvar() are non-generic functions, these could not be extended. Usemad() as an alternative, or use e.g.sd(as.numeric(x)) wherex is your vector of MIC values.

Usingas.double() oras.numeric() on MIC values will remove the operators and return a numeric vector. Donot useas.integer() on MIC values as by theR convention onfactors, it will return the index of the factor levels (which is often useless for regular users).

The functionis.mic() detects if the input contains classmic. If the input is adata.frame orlist, it iterates over all columns/items and returns alogical vector.

Usedroplevels() to drop unused levels. At default, it will return a plain factor. Usedroplevels(..., as.mic = TRUE) to maintain themic class.

Withrescale_mic(), existing MIC ranges can be limited to a defined range of MIC values. This can be useful to better compare MIC distributions.

Forggplot2, use one of thescale_*_mic() functions to plot MIC values. They allows custom MIC ranges and to plot intermediate log2 levels for missing MIC values.

NA_mic_ is a missing value of the newmic class, analogous to e.g. baseR'sNA_character_.

Usemic_p50() andmic_p90() to get the 50th and 90th percentile of MIC values. They return 'normal'numeric values.

Value

Orderedfactor with additional classmic, that in mathematical operations acts as anumeric vector. Bear in mind that the outcome of any mathematical operation on MICs will return anumeric value.

See Also

as.sir()

Examples

mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16"))mic_datais.mic(mic_data)# this can also coerce combined MIC/SIR values:as.mic("<=0.002; S")# mathematical processing treats MICs as numeric valuesfivenum(mic_data)quantile(mic_data)all(mic_data < 512)# rescale MICs using rescale_mic()rescale_mic(mic_data, mic_range = c(4, 16))# interpret MIC valuesas.sir(  x = as.mic(2),  mo = as.mo("Streptococcus pneumoniae"),  ab = "AMX",  guideline = "EUCAST")as.sir(  x = as.mic(c(0.01, 2, 4, 8)),  mo = as.mo("Streptococcus pneumoniae"),  ab = "AMX",  guideline = "EUCAST")# plot MIC values, see ?plotplot(mic_data)plot(mic_data, mo = "E. coli", ab = "cipro")if (require("ggplot2")) {  autoplot(mic_data, mo = "E. coli", ab = "cipro")}if (require("ggplot2")) {  autoplot(mic_data, mo = "E. coli", ab = "cipro", language = "nl") # Dutch}

Transform Arbitrary Input to Valid Microbial Taxonomy

Description

Use this function to get a valid microorganism code (mo) based on arbitrary user input. Determination is done using intelligent rules and the complete taxonomic tree of the kingdoms Animalia, Archaea, Bacteria, Chromista, and Protozoa, and most microbial species from the kingdom Fungi (seeSource). The input can be almost anything: a full name (like"Staphylococcus aureus"), an abbreviated name (such as"S. aureus"), an abbreviation known in the field (such as"MRSA"), or just a genus. SeeExamples.

Usage

as.mo(x, Becker = FALSE, Lancefield = FALSE,  minimum_matching_score = NULL,  keep_synonyms = getOption("AMR_keep_synonyms", FALSE),  reference_df = get_mo_source(),  ignore_pattern = getOption("AMR_ignore_pattern", NULL),  cleaning_regex = getOption("AMR_cleaning_regex", mo_cleaning_regex()),  only_fungi = getOption("AMR_only_fungi", FALSE),  language = get_AMR_locale(), info = interactive(), ...)is.mo(x)mo_uncertainties()mo_renamed()mo_failures()mo_reset_session()mo_cleaning_regex()

Arguments

Details

A microorganism (MO) code from this package (class:mo) is human-readable and typically looks like these examples:

  Code               Full name  ---------------    --------------------------------------  B_KLBSL            Klebsiella  B_KLBSL_PNMN       Klebsiella pneumoniae  B_KLBSL_PNMN_RHNS  Klebsiella pneumoniae rhinoscleromatis  |   |    |    |  |   |    |    |  |   |    |    \---> subspecies, a 3-5 letter acronym  |   |    \----> species, a 3-6 letter acronym  |   \----> genus, a 4-8 letter acronym  \----> kingdom: A (Archaea), AN (Animalia), B (Bacteria),                  C (Chromista), F (Fungi), PL (Plantae),                  P (Protozoa)

Values that cannot be coerced will be considered 'unknown' and will return the MO codeUNKNOWN with a warning.

Use themo_* functions to get properties based on the returned code, seeExamples.

Theas.mo() function uses a novel and scientifically validated (doi:10.18637/jss.v104.i03) matching score algorithm (seeMatching Score for Microorganisms below) to match input against theavailable microbial taxonomy in this package. This implicates that e.g."E. coli" (a microorganism highly prevalent in humans) will return the microbial ID ofEscherichia coli and notEntamoeba coli (a microorganism less prevalent in humans), although the latter would alphabetically come first.

Coping with Uncertain Results

Results of non-exact taxonomic input are based on theirmatching score. The lowest allowed score can be set with theminimum_matching_score argument. At default this will be determined based on the character length of the input, thetaxonomic kingdom, and thehuman pathogenicity of the taxonomic outcome. If values are matched with uncertainty, a message will be shown to suggest the user to inspect the results withmo_uncertainties(), which returns adata.frame with all specifications.

To increase the quality of matching, thecleaning_regex argument is used to clean the input. This must be aregular expression that matches parts of the input that should be removed before the input is matched against theavailable microbial taxonomy. It will be matched Perl-compatible and case-insensitive. The default value ofcleaning_regex is the outcome of the helper functionmo_cleaning_regex().

There are three helper functions that can be run after using theas.mo() function:

• Usemo_uncertainties() to get adata.frame that prints in a pretty format with all taxonomic names that were guessed. The output contains the matching score for all matches (seeMatching Score for Microorganisms below).

• Usemo_failures() to get acharactervector with all values that could not be coerced to a valid value.

• Usemo_renamed() to get adata.frame with all values that could be coerced based on old, previously accepted taxonomic names.

For Mycologists

Thematching score algorithm gives precedence to bacteria over fungi. If you are only analysing fungi, be sure to useonly_fungi = TRUE, or better yet, add this to your code and run it once every session:

options(AMR_only_fungi = TRUE)

This will make sure that no bacteria or other 'non-fungi' will be returned byas.mo(), or any of themo_* functions.

Coagulase-negative and Coagulase-positive Staphylococci

WithBecker = TRUE, the following staphylococci will be converted to their corresponding coagulase group:

• Coagulase-negative:S. americanisciuri,S. argensis,S. arlettae,S. auricularis,S. borealis,S. brunensis,S. caeli,S. caledonicus,S. canis,S. capitis,S. capitis capitis,S. capitis urealyticus,S. capitis ureolyticus,S. caprae,S. carnosus,S. carnosus carnosus,S. carnosus utilis,S. casei,S. caseolyticus,S. chromogenes,S. cohnii,S. cohnii cohnii,S. cohnii urealyticum,S. cohnii urealyticus,S. condimenti,S. croceilyticus,S. debuckii,S. devriesei,S. durrellii,S. edaphicus,S. epidermidis,S. equorum,S. equorum equorum,S. equorum linens,S. felis,S. fleurettii,S. gallinarum,S. haemolyticus,S. hominis,S. hominis hominis,S. hominis novobiosepticus,S. jettensis,S. kloosii,S. lentus,S. lloydii,S. lugdunensis,S. marylandisciuri,S. massiliensis,S. microti,S. muscae,S. nepalensis,S. pasteuri,S. petrasii,S. petrasii croceilyticus,S. petrasii jettensis,S. petrasii petrasii,S. petrasii pragensis,S. pettenkoferi,S. piscifermentans,S. pragensis,S. pseudoxylosus,S. pulvereri,S. ratti,S. rostri,S. saccharolyticus,S. saprophyticus,S. saprophyticus bovis,S. saprophyticus saprophyticus,S. schleiferi,S. schleiferi schleiferi,S. sciuri,S. sciuri carnaticus,S. sciuri lentus,S. sciuri rodentium,S. sciuri sciuri,S. shinii,S. simulans,S. stepanovicii,S. succinus,S. succinus casei,S. succinus succinus,S. taiwanensis,S. urealyticus,S. ureilyticus,S. veratri,S. vitulinus,S. vitulus,S. warneri, andS. xylosus

• Coagulase-positive:S. agnetis,S. argenteus,S. coagulans,S. cornubiensis,S. delphini,S. hyicus,S. hyicus chromogenes,S. hyicus hyicus,S. intermedius,S. lutrae,S. pseudintermedius,S. roterodami,S. schleiferi coagulans,S. schweitzeri,S. simiae, andS. singaporensis

This is based on:

• Becker Ket al. (2014).Coagulase-Negative Staphylococci.Clin Microbiol Rev. 27(4): 870-926;doi:10.1128/CMR.00109-13

• Becker Ket al. (2019).Implications of identifying the recently defined members of theS. aureus complex,S. argenteus andS. schweitzeri: A position paper of members of the ESCMID Study Group for staphylococci and Staphylococcal Diseases (ESGS).Clin Microbiol Infect;doi:10.1016/j.cmi.2019.02.028

• Becker Ket al. (2020).Emergence of coagulase-negative staphylococci.Expert Rev Anti Infect Ther. 18(4):349-366;doi:10.1080/14787210.2020.1730813

For newly named staphylococcal species, such asS. brunensis (2024) andS. shinii (2023), we looked up the scientific reference to make sure the species are considered for the correct coagulase group.

Lancefield Groups in Streptococci

WithLancefield = TRUE, the following streptococci will be converted to their corresponding Lancefield group:

• Streptococcus Group A:S. pyogenes

• Streptococcus Group B:S. agalactiae

• Streptococcus Group C:S. dysgalactiae,S. dysgalactiae dysgalactiae,S. dysgalactiae equisimilis,S. equi,S. equi equi,S. equi ruminatorum, andS. equi zooepidemicus

• Streptococcus Group F:S. anginosus,S. anginosus anginosus,S. anginosus whileyi,S. constellatus,S. constellatus constellatus,S. constellatus pharyngis,S. constellatus viborgensis, andS. intermedius

• Streptococcus Group G:S. canis,S. dysgalactiae,S. dysgalactiae dysgalactiae, andS. dysgalactiae equisimilis

• Streptococcus Group H:S. sanguinis

• Streptococcus Group K:S. salivarius,S. salivarius salivarius, andS. salivarius thermophilus

• Streptococcus Group L:S. dysgalactiae,S. dysgalactiae dysgalactiae, andS. dysgalactiae equisimilis

This is based on:

• Lancefield RC (1933).A serological differentiation of human and other groups of hemolytic streptococci.J Exp Med. 57(4): 571-95;doi:10.1084/jem.57.4.571

Value

Acharactervector with additional classmo

Source

• Berends MSet al. (2022).AMR: An R Package for Working with Antimicrobial Resistance Data.Journal of Statistical Software, 104(3), 1-31;doi:10.18637/jss.v104.i03

• Parte, ACet al. (2020).List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ. International Journal of Systematic and Evolutionary Microbiology, 70, 5607-5612;doi:10.1099/ijsem.0.004332. Accessed fromhttps://lpsn.dsmz.de on June 24th, 2024.

• Vincent, Ret al (2013).MycoBank gearing up for new horizons. IMA Fungus, 4(2), 371-9;doi:10.5598/imafungus.2013.04.02.16. Accessed fromhttps://www.mycobank.org on June 24th, 2024.

• GBIF Secretariat (2023). GBIF Backbone Taxonomy. Checklist datasetdoi:10.15468/39omei. Accessed fromhttps://www.gbif.org on June 24th, 2024.

• Reimer, LCet al. (2022).BacDive in 2022: the knowledge base for standardized bacterial and archaeal data. Nucleic Acids Res., 50(D1):D741-D74;doi:10.1093/nar/gkab961. Accessed fromhttps://bacdive.dsmz.de on July 16th, 2024.

• Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS). US Edition of SNOMED CT from 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL:https://www.cdc.gov/phin/php/phinvads/

• Bartlett Aet al. (2022).A comprehensive list of bacterial pathogens infecting humansMicrobiology 168:001269;doi:10.1099/mic.0.001269

Matching Score for Microorganisms

With ambiguous user input inas.mo() and all themo_* functions, the returned results are chosen based on their matching score usingmo_matching_score(). This matching scorem, is calculated as:

m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}

where:

• x is the user input;

• n is a taxonomic name (genus, species, and subspecies);

• l_n is the length ofn;

• lev is theLevenshtein distance function (counting any insertion as 1, and any deletion or substitution as 2) that is needed to changex inton;

• p_n is the human pathogenic prevalence group ofn, as described below;

• k_n is the taxonomic kingdom ofn, set as Bacteria = 1, Fungi = 1.25, Protozoa = 1.5, Chromista = 1.75, Archaea = 2, others = 3.

The grouping into human pathogenic prevalencep is based on recent work from Bartlettet al. (2022,doi:10.1099/mic.0.001269) who extensively studied medical-scientific literature to categorise all bacterial species into these groups:

• Established, if a taxonomic species has infected at least three persons in three or more references. These records haveprevalence = 1.15 in themicroorganisms data set;

• Putative, if a taxonomic species has fewer than three known cases. These records haveprevalence = 1.25 in themicroorganisms data set.

Furthermore,

• Genera from the World Health Organization's (WHO) Priority Pathogen List haveprevalence = 1.0 in themicroorganisms data set;

• Any genus present in theestablished list also hasprevalence = 1.15 in themicroorganisms data set;

• Any other genus present in theputative list hasprevalence = 1.25 in themicroorganisms data set;

• Any other species or subspecies of which the genus is present in the two aforementioned groups, hasprevalence = 1.5 in themicroorganisms data set;

• Anynon-bacterial genus, species or subspecies of which the genus is present in the following list, hasprevalence = 1.25 in themicroorganisms data set:Absidia,Acanthamoeba,Acremonium,Actinomucor,Aedes,Alternaria,Amoeba,Ancylostoma,Angiostrongylus,Anisakis,Anopheles,Apophysomyces,Arthroderma,Aspergillus,Aureobasidium,Basidiobolus,Beauveria,Bipolaris,Blastobotrys,Blastocystis,Blastomyces,Candida,Capillaria,Chaetomium,Chilomastix,Chrysonilia,Chrysosporium,Cladophialophora,Cladosporium,Clavispora,Coccidioides,Cokeromyces,Conidiobolus,Coniochaeta,Contracaecum,Cordylobia,Cryptococcus,Cryptosporidium,Cunninghamella,Curvularia,Cyberlindnera,Debaryozyma,Demodex,Dermatobia,Dientamoeba,Diphyllobothrium,Dirofilaria,Echinostoma,Entamoeba,Enterobius,Epidermophyton,Exidia,Exophiala,Exserohilum,Fasciola,Fonsecaea,Fusarium,Geotrichum,Giardia,Graphium,Haloarcula,Halobacterium,Halococcus,Hansenula,Hendersonula,Heterophyes,Histomonas,Histoplasma,Hortaea,Hymenolepis,Hypomyces,Hysterothylacium,Kloeckera,Kluyveromyces,Kodamaea,Lacazia,Leishmania,Lichtheimia,Lodderomyces,Lomentospora,Madurella,Malassezia,Malbranchea,Metagonimus,Meyerozyma,Microsporidium,Microsporum,Millerozyma,Mortierella,Mucor,Mycocentrospora,Nannizzia,Necator,Nectria,Ochroconis,Oesophagostomum,Oidiodendron,Opisthorchis,Paecilomyces,Paracoccidioides,Pediculus,Penicillium,Phaeoacremonium,Phaeomoniella,Phialophora,Phlebotomus,Phoma,Pichia,Piedraia,Pithomyces,Pityrosporum,Pneumocystis,Pseudallescheria,Pseudoscopulariopsis,Pseudoterranova,Pulex,Purpureocillium,Quambalaria,Rhinocladiella,Rhizomucor,Rhizopus,Rhodotorula,Saccharomyces,Saksenaea,Saprochaete,Sarcoptes,Scedosporium,Schistosoma,Schizosaccharomyces,Scolecobasidium,Scopulariopsis,Scytalidium,Spirometra,Sporobolomyces,Sporopachydermia,Sporothrix,Sporotrichum,Stachybotrys,Strongyloides,Syncephalastrum,Syngamus,Taenia,Talaromyces,Teleomorph,Toxocara,Trichinella,Trichobilharzia,Trichoderma,Trichomonas,Trichophyton,Trichosporon,Trichostrongylus,Trichuris,Tritirachium,Trombicula,Trypanosoma,Tunga,Ulocladium,Ustilago,Verticillium,Wallemia,Wangiella,Wickerhamomyces,Wuchereria,Yarrowia, orZygosaccharomyces;

• All other records haveprevalence = 2.0 in themicroorganisms data set.

When calculating the matching score, all characters inx andn are ignored that are other than A-Z, a-z, 0-9, spaces and parentheses.

All matches are sorted descending on their matching score and for all user input values, the top match will be returned. This will lead to the effect that e.g.,"E. coli" will return the microbial ID ofEscherichia coli (m = 0.688, a highly prevalent microorganism found in humans) and notEntamoeba coli (m = 0.381, a less prevalent microorganism in humans), although the latter would alphabetically come first.

Download Our Reference Data

All reference data sets in the AMR package - including information on microorganisms, antimicrobials, and clinical breakpoints - are freely available for download in multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, and Stata.

For maximum compatibility, we also provide machine-readable, tab-separated plain text files suitable for use in any software, including laboratory information systems.

Visitour website for direct download links, or explore the actual files inour GitHub repository.

See Also

microorganisms for thedata.frame that is being used to determine ID's.

Themo_* functions (such asmo_genus(),mo_gramstain()) to get properties based on the returned code.

Examples

# These examples all return "B_STPHY_AURS", the ID of S. aureus:as.mo(c(  "sau", # WHONET code  "stau",  "STAU",  "staaur",  "S. aureus",  "S aureus",  "Sthafilokkockus aureus", # handles incorrect spelling  "Staphylococcus aureus (MRSA)",  "MRSA", # Methicillin Resistant S. aureus  "VISA", # Vancomycin Intermediate S. aureus  "VRSA", # Vancomycin Resistant S. aureus  115329001 # SNOMED CT code))# Dyslexia is no problem - these all work:as.mo(c(  "Ureaplasma urealyticum",  "Ureaplasma urealyticus",  "Ureaplasmium urealytica",  "Ureaplazma urealitycium"))# input will get cleaned up with the input given in the `cleaning_regex` argument,# which defaults to `mo_cleaning_regex()`:cat(mo_cleaning_regex(), "\n")as.mo("Streptococcus group A")as.mo("S. epidermidis") # will remain species: B_STPHY_EPDRas.mo("S. epidermidis", Becker = TRUE) # will not remain species: B_STPHY_CONSas.mo("S. pyogenes") # will remain species: B_STRPT_PYGNas.mo("S. pyogenes", Lancefield = TRUE) # will not remain species: B_STRPT_GRPA# All mo_* functions use as.mo() internally too (see ?mo_property):mo_genus("E. coli")mo_gramstain("ESCO")mo_is_intrinsic_resistant("ESCCOL", ab = "vanco")

Interpret MIC and Disk Diffusion as SIR, or Clean Existing SIR Data

Description

Clean up existing SIR values, or interpret minimum inhibitory concentration (MIC) values and disk diffusion diameters according to EUCAST or CLSI.as.sir() transforms the input to a new classsir, which is an orderedfactor containing the levelsS,SDD,I,R,NI.

Breakpoints are currently implemented from EUCAST 2011-2025 and CLSI 2011-2025, seeDetails. All breakpoints used for interpretation are available in ourclinical_breakpoints data set.

Usage

as.sir(x, ...)NA_sir_is.sir(x)is_sir_eligible(x, threshold = 0.05)## Default S3 method:as.sir(x, S = "^(S|U)+$", I = "^(I)+$", R = "^(R)+$",  NI = "^(N|NI|V)+$", SDD = "^(SDD|D|H)+$", info = interactive(), ...)## S3 method for class 'mic'as.sir(x, mo = NULL, ab = deparse(substitute(x)),  guideline = getOption("AMR_guideline", "EUCAST"), uti = NULL,  capped_mic_handling = getOption("AMR_capped_mic_handling", "standard"),  add_intrinsic_resistance = FALSE,  reference_data = AMR::clinical_breakpoints,  substitute_missing_r_breakpoint = getOption("AMR_substitute_missing_r_breakpoint",  FALSE), include_screening = getOption("AMR_include_screening", FALSE),  include_PKPD = getOption("AMR_include_PKPD", TRUE),  breakpoint_type = getOption("AMR_breakpoint_type", "human"), host = NULL,  language = get_AMR_locale(), verbose = FALSE, info = interactive(),  conserve_capped_values = NULL, ...)## S3 method for class 'disk'as.sir(x, mo = NULL, ab = deparse(substitute(x)),  guideline = getOption("AMR_guideline", "EUCAST"), uti = NULL,  add_intrinsic_resistance = FALSE,  reference_data = AMR::clinical_breakpoints,  substitute_missing_r_breakpoint = getOption("AMR_substitute_missing_r_breakpoint",  FALSE), include_screening = getOption("AMR_include_screening", FALSE),  include_PKPD = getOption("AMR_include_PKPD", TRUE),  breakpoint_type = getOption("AMR_breakpoint_type", "human"), host = NULL,  language = get_AMR_locale(), verbose = FALSE, info = interactive(),  ...)## S3 method for class 'data.frame'as.sir(x, ..., col_mo = NULL,  guideline = getOption("AMR_guideline", "EUCAST"), uti = NULL,  capped_mic_handling = getOption("AMR_capped_mic_handling", "standard"),  add_intrinsic_resistance = FALSE,  reference_data = AMR::clinical_breakpoints,  substitute_missing_r_breakpoint = getOption("AMR_substitute_missing_r_breakpoint",  FALSE), include_screening = getOption("AMR_include_screening", FALSE),  include_PKPD = getOption("AMR_include_PKPD", TRUE),  breakpoint_type = getOption("AMR_breakpoint_type", "human"), host = NULL,  language = get_AMR_locale(), verbose = FALSE, info = interactive(),  parallel = FALSE, max_cores = -1, conserve_capped_values = NULL)sir_interpretation_history(clean = FALSE)

Arguments

Details

Note: The clinical breakpoints in this package were validated through, and imported from,WHONET. The public use of thisAMR package has been endorsed by both CLSI and EUCAST. Seeclinical_breakpoints for more information.

How it Works

Theas.sir() function can work in four ways:

1. Forcleaning raw / untransformed data. The data will be cleaned to only contain valid values, namely:S for susceptible,I for intermediate or 'susceptible, increased exposure',R for resistant,NI for non-interpretable, andSDD for susceptible dose-dependent. Each of these can be set using aregular expression. Furthermore,as.sir() will try its best to clean with some intelligence. For example, mixed values with SIR interpretations and MIC values such as"<0.25; S" will be coerced to"S". Combined interpretations for multiple test methods (as seen in laboratory records) such as"S; S" will be coerced to"S", but a value like"S; I" will returnNA with a warning that the input is invalid.

2. Forinterpreting minimum inhibitory concentration (MIC) values according to EUCAST or CLSI. You must clean your MIC values first usingas.mic(), that also gives your columns the new data classmic. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using themo argument.

   • Example to apply usingdplyr:

     your_data %>% mutate_if(is.mic, as.sir)your_data %>% mutate(across(where(is.mic), as.sir))your_data %>% mutate_if(is.mic, as.sir, ab = "column_with_antibiotics", mo = "column_with_microorganisms")your_data %>% mutate_if(is.mic, as.sir, ab = c("cipro", "ampicillin", ...), mo = c("E. coli", "K. pneumoniae", ...))# for veterinary breakpoints, also set `host`:your_data %>% mutate_if(is.mic, as.sir, host = "column_with_animal_species", guideline = "CLSI")# fast processing with parallel computing:as.sir(your_data, ..., parallel = TRUE)

   • Operators like "<=" will be stripped before interpretation. When usingcapped_mic_handling = "conservative", an MIC value of e.g. ">2" will always return "R", even if the breakpoint according to the chosen guideline is ">=4". This is to prevent that capped values from raw laboratory data would not be treated conservatively. The default behaviour (capped_mic_handling = "standard") considers ">2" to be lower than ">=4" and might in this case return "S" or "I".

   • Note: When using CLSI as the guideline, MIC values must be log2-based doubling dilutions. Values not in this format, will be automatically rounded up to the nearest log2 level as CLSI instructs, and a warning will be thrown.

3. Forinterpreting disk diffusion diameters according to EUCAST or CLSI. You must clean your disk zones first usingas.disk(), that also gives your columns the new data classdisk. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using themo argument.

   • Example to apply usingdplyr:

     your_data %>% mutate_if(is.disk, as.sir)your_data %>% mutate(across(where(is.disk), as.sir))your_data %>% mutate_if(is.disk, as.sir, ab = "column_with_antibiotics", mo = "column_with_microorganisms")your_data %>% mutate_if(is.disk, as.sir, ab = c("cipro", "ampicillin", ...), mo = c("E. coli", "K. pneumoniae", ...))# for veterinary breakpoints, also set `host`:your_data %>% mutate_if(is.disk, as.sir, host = "column_with_animal_species", guideline = "CLSI")# fast processing with parallel computing:as.sir(your_data, ..., parallel = TRUE)

4. Forinterpreting a complete data set, with automatic determination of MIC values, disk diffusion diameters, microorganism names or codes, and antimicrobial test results. This is done very simply by runningas.sir(your_data).

For points 2, 3 and 4: Usesir_interpretation_history() to retrieve adata.frame with all results of all previousas.sir() calls. It also contains notes about interpretation, and the exact input and output values.

Supported Guidelines

For interpreting MIC values as well as disk diffusion diameters, currently implemented guidelines are:

• Forclinical microbiology: EUCAST 2011-2025 and CLSI 2011-2025;

• Forveterinary microbiology: EUCAST 2021-2025 and CLSI 2019-2025;

• ForECOFFs (Epidemiological Cut-off Values): EUCAST 2020-2025 and CLSI 2022-2025.

Theguideline argument must be set to e.g.,"EUCAST 2025" or"CLSI 2025". By simply using"EUCAST" (the default) or"CLSI" as input, the latest included version of that guideline will automatically be selected. Importantly, using a column name of your data instead, allows for straightforward interpretation of historical data that must be analysed in the context of, for example, different years.

You can set your own data set using thereference_data argument. Theguideline argument will then be ignored.

It is also possible to set the default guideline with the package optionAMR_guideline (e.g. in your.Rprofile file), such as:

  options(AMR_guideline = "CLSI")  options(AMR_guideline = "CLSI 2018")  options(AMR_guideline = "EUCAST 2020")  # or to reset:  options(AMR_guideline = NULL)

Working with Veterinary Breakpoints

When using veterinary breakpoints (i.e., settingbreakpoint_type = "animal"), a column with animal species must be available or set manually using thehost argument. The column must contain names like "dogs", "cats", "cattle", "swine", "horses", "poultry", or "aquatic". Other animal names like "goats", "rabbits", or "monkeys" are also recognised but may not be available in all guidelines. Matching is case-insensitive and accepts Latin-based synonyms (e.g., "bovine" for cattle and "canine" for dogs).

Regarding choice of veterinary guidelines, these might be the best options to set before analysis:

  options(AMR_guideline = "CLSI")  options(AMR_breakpoint_type = "animal")

After Interpretation

After usingas.sir(), you can use theeucast_rules() defined by EUCAST to (1) apply inferred susceptibility and resistance based on results of other antimicrobials and (2) apply intrinsic resistance based on taxonomic properties of a microorganism.

To determine which isolates are multi-drug resistant, be sure to runmdro() (which applies the MDR/PDR/XDR guideline from 2012 at default) on a data set that contains S/I/R values. Read more aboutinterpreting multidrug-resistant organisms here.

Other

The functionis.sir() detects if the input contains classsir. If the input is adata.frame orlist, it iterates over all columns/items and returns alogical vector.

The base R functionas.double() can be used to retrieve quantitative values from asir object:"S" = 1,"I"/"SDD" = 2,"R" = 3. All other values are renderedNA.Note: Do not useas.integer(), since that (because of how R works internally) will return the factor level indices, and not these aforementioned quantitative values.

The functionis_sir_eligible() returnsTRUE when a column contains at most 5% potentially invalid antimicrobial interpretations, andFALSE otherwise. The threshold of 5% can be set with thethreshold argument. If the input is adata.frame, it iterates over all columns and returns alogical vector.

NA_sir_ is a missing value of the newsir class, analogous to e.g. baseR'sNA_character_.

Value

Orderedfactor with new classsir

Interpretation of SIR

In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R (https://www.eucast.org/newsiandr).

This AMR package follows insight; usesusceptibility() (equal toproportion_SI()) to determine antimicrobial susceptibility andcount_susceptible() (equal tocount_SI()) to count susceptible isolates.

Download Our Reference Data

All reference data sets in the AMR package - including information on microorganisms, antimicrobials, and clinical breakpoints - are freely available for download in multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, and Stata.

For maximum compatibility, we also provide machine-readable, tab-separated plain text files suitable for use in any software, including laboratory information systems.

Visitour website for direct download links, or explore the actual files inour GitHub repository.

Source

For interpretations of minimum inhibitory concentration (MIC) values and disk diffusion diameters:

• CLSI M39: Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 2011-2025,Clinical and Laboratory Standards Institute (CLSI).https://clsi.org/standards/products/microbiology/documents/m39/.

• CLSI M100: Performance Standard for Antimicrobial Susceptibility Testing, 2011-2025,Clinical and Laboratory Standards Institute (CLSI).https://clsi.org/standards/products/microbiology/documents/m100/.

• CLSI VET01: Performance Standards for Antimicrobial Disk and Dilution Susceptibility Tests for Bacteria Isolated From Animals, 2019-2025,Clinical and Laboratory Standards Institute (CLSI).https://clsi.org/standards/products/veterinary-medicine/documents/vet01/.

• EUCAST Breakpoint tables for interpretation of MICs and zone diameters, 2011-2025,European Committee on Antimicrobial Susceptibility Testing (EUCAST).https://www.eucast.org/clinical_breakpoints.

• WHONET as a source for machine-reading the clinical breakpoints (read more here), 1989-2025,WHO Collaborating Centre for Surveillance of Antimicrobial Resistance.https://whonet.org/.

See Also

as.mic(),as.disk(),as.mo()

Examples

example_isolatessummary(example_isolates[, 1:10]) # see all SIR results at a glance# create some example data sets, with combined MIC values and disk zonesdf_wide <- data.frame(  microorganism = "Escherichia coli",  amoxicillin = as.mic(8),  cipro = as.mic(0.256),  tobra = as.disk(16),  genta = as.disk(18),  ERY = "R")df_long <- data.frame(  bacteria = rep("Escherichia coli", 4),  antibiotic = c("amoxicillin", "cipro", "tobra", "genta"),  mics = as.mic(c(0.01, 1, 4, 8)),  disks = as.disk(c(6, 10, 14, 18)),  guideline = c("EUCAST 2021", "EUCAST 2022", "EUCAST 2023", "EUCAST 2024"))# and clean previous SIR interpretation logsx <- sir_interpretation_history(clean = TRUE)# For INTERPRETING disk diffusion and MIC values -----------------------# most basic application:as.sir(df_wide)# return a 'logbook' about the results:sir_interpretation_history()# using parallel computing, which is available in base R:as.sir(df_wide, parallel = TRUE, info = TRUE)## Using dplyr -------------------------------------------------if (require("dplyr")) {  # approaches that all work without additional arguments:  df_wide %>% mutate_if(is.mic, as.sir)  df_wide %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)  df_wide %>% mutate(across(where(is.mic), as.sir))  df_wide %>% mutate_at(vars(amoxicillin:tobra), as.sir)  df_wide %>% mutate(across(amoxicillin:tobra, as.sir))  df_wide %>% mutate(across(aminopenicillins(), as.sir))  # approaches that all work with additional arguments:  df_long %>%    # given a certain data type, e.g. MIC values    mutate_if(is.mic, as.sir,      mo = "bacteria",      ab = "antibiotic",      guideline = "guideline"    )  df_long %>%    mutate(across(      where(is.mic),      function(x) {        as.sir(x,          mo = "bacteria",          ab = "antibiotic",          guideline = "CLSI"        )      }    ))  df_wide %>%    # given certain columns, e.g. from 'cipro' to 'genta'    mutate_at(vars(cipro:genta), as.sir,      mo = "bacteria",      guideline = "CLSI"    )  df_wide %>%    mutate(across(      cipro:genta,      function(x) {        as.sir(x,          mo = "bacteria",          guideline = "CLSI"        )      }    ))  # for veterinary breakpoints, add 'host':  df_long$animal_species <- c("cats", "dogs", "horses", "cattle")  df_long %>%    # given a certain data type, e.g. MIC values    mutate_if(is.mic, as.sir,      mo = "bacteria",      ab = "antibiotic",      host = "animal_species",      guideline = "CLSI"    )  df_long %>%    mutate(across(      where(is.mic),      function(x) {        as.sir(x,          mo = "bacteria",          ab = "antibiotic",          host = "animal_species",          guideline = "CLSI"        )      }    ))  df_wide %>%    mutate_at(vars(cipro:genta), as.sir,      mo = "bacteria",      ab = "antibiotic",      host = "animal_species",      guideline = "CLSI"    )  df_wide %>%    mutate(across(      cipro:genta,      function(x) {        as.sir(x,          mo = "bacteria",          host = "animal_species",          guideline = "CLSI"        )      }    ))  # to include information about urinary tract infections (UTI)  data.frame(    mo = "E. coli",    nitrofuratoin = c("<= 2", 32),    from_the_bladder = c(TRUE, FALSE)  ) %>%    as.sir(uti = "from_the_bladder")  data.frame(    mo = "E. coli",    nitrofuratoin = c("<= 2", 32),    specimen = c("urine", "blood")  ) %>%    as.sir() # automatically determines urine isolates  df_wide %>%    mutate_at(vars(cipro:genta), as.sir, mo = "E. coli", uti = TRUE)}## Using base R ------------------------------------------------# for single valuesas.sir(  x = as.mic(2),  mo = as.mo("S. pneumoniae"),  ab = "AMP",  guideline = "EUCAST")as.sir(  x = as.disk(18),  mo = "Strep pneu", # `mo` will be coerced with as.mo()  ab = "ampicillin", # and `ab` with as.ab()  guideline = "EUCAST")# For CLEANING existing SIR values -------------------------------------as.sir(c("S", "SDD", "I", "R", "NI", "A", "B", "C"))as.sir("<= 0.002; S") # will return "S"sir_data <- as.sir(c(rep("S", 474), rep("I", 36), rep("R", 370)))is.sir(sir_data)plot(sir_data) # for percentagesbarplot(sir_data) # for frequencies# as common in R, you can use as.integer() to return factor indices:as.integer(as.sir(c("S", "SDD", "I", "R", "NI", NA)))# but for computational use, as.double() will return 1 for S, 2 for I/SDD, and 3 for R:as.double(as.sir(c("S", "SDD", "I", "R", "NI", NA)))# the dplyr wayif (require("dplyr")) {  example_isolates %>%    mutate_at(vars(PEN:RIF), as.sir)  # same:  example_isolates %>%    as.sir(PEN:RIF)  # fastest way to transform all columns with already valid AMR results to class `sir`:  example_isolates %>%    mutate_if(is_sir_eligible, as.sir)  # since dplyr 1.0.0, this can also be the more impractical:  # example_isolates %>%  #   mutate(across(where(is_sir_eligible), as.sir))}

Get ATC Properties from WHOCC Website

Description

Gets data from the WHOCC website to determine properties of an Anatomical Therapeutic Chemical (ATC) (e.g. an antimicrobial), such as the name, defined daily dose (DDD) or standard unit.

Usage

atc_online_property(atc_code, property, administration = "O",  url = "https://atcddd.fhi.no/atc_ddd_index/?code=%s&showdescription=no",  url_vet = "https://atcddd.fhi.no/atcvet/atcvet_index/?code=%s&showdescription=no")atc_online_groups(atc_code, ...)atc_online_ddd(atc_code, ...)atc_online_ddd_units(atc_code, ...)

Arguments

Details

Options for argumentadministration:

• "Implant" = Implant

• "Inhal" = Inhalation

• "Instill" = Instillation

• "N" = nasal

• "O" = oral

• "P" = parenteral

• "R" = rectal

• "SL" = sublingual/buccal

• "TD" = transdermal

• "V" = vaginal

Abbreviations of return values when usingproperty = "U" (unit):

• "g" = gram

• "mg" = milligram

• "mcg" = microgram

• "U" = unit

• "TU" = thousand units

• "MU" = million units

• "mmol" = millimole

• "ml" = millilitre (e.g. eyedrops)

N.B. This function requires an internet connection and only works if the following packages are installed:curl,rvest,xml2.

Source

https://atcddd.fhi.no/atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/

Examples

if (requireNamespace("curl") && requireNamespace("rvest") && requireNamespace("xml2")) {  # oral DDD (Defined Daily Dose) of amoxicillin  atc_online_property("J01CA04", "DDD", "O")  atc_online_ddd(ab_atc("amox"))  # parenteral DDD (Defined Daily Dose) of amoxicillin  atc_online_property("J01CA04", "DDD", "P")  atc_online_property("J01CA04", property = "groups") # search hierarchical groups of amoxicillin}

Retrieve Antiviral Drug Names and Doses from Clinical Text

Description

Use this function on e.g. clinical texts from health care records. It returns alist with all antiviral drugs, doses and forms of administration found in the texts.

Usage

av_from_text(text, type = c("drug", "dose", "administration"),  collapse = NULL, translate_av = FALSE, thorough_search = NULL,  info = interactive(), ...)

Arguments

Details

This function is also internally used byas.av(), although it then only searches for the first drug name and will throw a note if more drug names could have been returned. Note: theas.av() function may use very long regular expression to match brand names of antiviral drugs. This may fail on some systems.

Argumenttype

At default, the function will search for antiviral drug names. All text elements will be searched for official names, ATC codes and brand names. As it usesas.av() internally, it will correct for misspelling.

Withtype = "dose" (or similar, like "dosing", "doses"), all text elements will be searched fornumeric values that are higher than 100 and do not resemble years. The output will benumeric. It supports any unit (g, mg, IE, etc.) and multiple values in one clinical text, seeExamples.

Withtype = "administration" (or abbreviations, like "admin", "adm"), all text elements will be searched for a form of drug administration. It supports the following forms (including common abbreviations): buccal, implant, inhalation, instillation, intravenous, nasal, oral, parenteral, rectal, sublingual, transdermal and vaginal. Abbreviations for oral (such as 'po', 'per os') will become "oral", all values for intravenous (such as 'iv', 'intraven') will become "iv". It supports multiple values in one clinical text, seeExamples.

Argumentcollapse

Without usingcollapse, this function will return alist. This can be convenient to use e.g. inside amutate()):
df %>% mutate(avx = av_from_text(clinical_text))

The returned AV codes can be transformed to official names, groups, etc. with allav_* functions such asav_name() andav_group(), or by using thetranslate_av argument.

With usingcollapse, this function will return acharacter:
df %>% mutate(avx = av_from_text(clinical_text, collapse = "|"))

Value

Alist, or acharacter ifcollapse is notNULL

Examples

av_from_text("28/03/2020 valaciclovir po tid")av_from_text("28/03/2020 valaciclovir po tid", type = "admin")

Get Properties of an Antiviral Drug

Description

Use these functions to return a specific property of an antiviral drug from theantivirals data set. All input values will be evaluated internally withas.av().

Usage

av_name(x, language = get_AMR_locale(), tolower = FALSE, ...)av_cid(x, ...)av_synonyms(x, ...)av_tradenames(x, ...)av_group(x, language = get_AMR_locale(), ...)av_atc(x, ...)av_loinc(x, ...)av_ddd(x, administration = "oral", ...)av_ddd_units(x, administration = "oral", ...)av_info(x, language = get_AMR_locale(), ...)av_url(x, open = FALSE, ...)av_property(x, property = "name", language = get_AMR_locale(), ...)

Arguments

Details

All outputwill be translated where possible.

The functionav_url() will return the direct URL to the official WHO website. A warning will be returned if the required ATC code is not available.

Value

• Aninteger in case ofav_cid()

• A namedlist in case ofav_info() and multipleav_atc()/av_synonyms()/av_tradenames()

• Adouble in case ofav_ddd()

• Acharacter in all other cases

Source

World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology:https://atcddd.fhi.no/atc_ddd_index/

European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER:https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm

Download Our Reference Data

All reference data sets in the AMR package - including information on microorganisms, antimicrobials, and clinical breakpoints - are freely available for download in multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, and Stata.

For maximum compatibility, we also provide machine-readable, tab-separated plain text files suitable for use in any software, including laboratory information systems.

Visitour website for direct download links, or explore the actual files inour GitHub repository.

See Also

antivirals

Examples

# all properties:av_name("ACI")av_atc("ACI")av_cid("ACI")av_synonyms("ACI")av_tradenames("ACI")av_group("ACI")av_url("ACI")# lowercase transformationav_name(x = c("ACI", "VALA"))av_name(x = c("ACI", "VALA"), tolower = TRUE)# defined daily doses (DDD)av_ddd("ACI", "oral")av_ddd_units("ACI", "oral")av_ddd("ACI", "iv")av_ddd_units("ACI", "iv")av_info("ACI") # all properties as a list# all av_* functions use as.av() internally, so you can go from 'any' to 'any':av_atc("ACI")av_group("J05AB01")av_loinc("abacavir")av_name("29113-8")av_name(135398513)av_name("J05AB01")

Check Availability of Columns

Description

Easy check for data availability of all columns in a data set. This makes it easy to get an idea of which antimicrobial combinations can be used for calculation with e.g.susceptibility() andresistance().

Usage

availability(tbl, width = NULL)

Arguments

Details

The function returns adata.frame with columns"resistant" and"visual_resistance". The values in that columns are calculated withresistance().

Value

data.frame with column names oftbl as row names

Examples

availability(example_isolates)if (require("dplyr")) {  example_isolates %>%    filter(mo == as.mo("Escherichia coli")) %>%    select_if(is.sir) %>%    availability()}

Determine Bug-Drug Combinations

Description

Determine antimicrobial resistance (AMR) of all bug-drug combinations in your data set where at least 30 (default) isolates are available per species. Useformat() on the result to prettify it to a publishable/printable format, seeExamples.

Usage

bug_drug_combinations(x, col_mo = NULL, FUN = mo_shortname,  include_n_rows = FALSE, ...)## S3 method for class 'bug_drug_combinations'format(x, translate_ab = "name (ab, atc)",  language = get_AMR_locale(), minimum = 30, combine_SI = TRUE,  add_ab_group = TRUE, remove_intrinsic_resistant = FALSE,  decimal.mark = getOption("OutDec"), big.mark = ifelse(decimal.mark ==  ",", ".", ","), ...)

Arguments

Details

The functionformat() calculates the resistance per bug-drug combination and returns a table ready for reporting/publishing. Usecombine_SI = TRUE (default) to test R vs. S+I andcombine_SI = FALSE to test R+I vs. S. This table can also directly be used in R Markdown / Quarto without the need for e.g.knitr::kable().

Value

The functionbug_drug_combinations() returns adata.frame with columns "mo", "ab", "S", "SDD", "I", "R", and "total".

Examples

# example_isolates is a data set available in the AMR package.# run ?example_isolates for more info.example_isolatesx <- bug_drug_combinations(example_isolates)head(x)format(x, translate_ab = "name (atc)")# Use FUN to change to transformation of microorganism codesbug_drug_combinations(example_isolates,  FUN = mo_gramstain)bug_drug_combinations(example_isolates,  FUN = function(x) {    ifelse(x == as.mo("Escherichia coli"),      "E. coli",      "Others"    )  })

Data Set with Clinical Breakpoints for SIR Interpretation

Description

Data set containing clinical breakpoints to interpret MIC and disk diffusion to SIR values, according to international guidelines. This dataset contain breakpoints for humans, 7 different animal groups, and ECOFFs.

These breakpoints are currently implemented:

• Forclinical microbiology: EUCAST 2011-2025 and CLSI 2011-2025;

• Forveterinary microbiology: EUCAST 2021-2025 and CLSI 2019-2025;

• ForECOFFs (Epidemiological Cut-off Values): EUCAST 2020-2025 and CLSI 2022-2025.

Useas.sir() to transform MICs or disks measurements to SIR values.

Usage

clinical_breakpoints

Format

Atibble with 40 217 observations and 14 variables:

• guideline
  Name of the guideline

• type
  Breakpoint type, either "ECOFF", "animal", or "human"

• host
  Host of infectious agent. This is mostly useful for veterinary breakpoints and is either "ECOFF", "aquatic", "cats", "cattle", "dogs", "horse", "human", "poultry", or "swine"

• method
  Testing method, either "DISK" or "MIC"

• site
  Body site for which the breakpoint must be applied, e.g. "Oral" or "Respiratory"

• mo
  Microbial ID, seeas.mo()

• rank_index
  Taxonomic rank index ofmo from 1 (subspecies/infraspecies) to 5 (unknown microorganism)

• ab
  Antimicrobial code as used by this package, EARS-Net and WHONET, seeas.ab()

• ref_tbl
  Info about where the guideline rule can be found

• disk_dose
  Dose of the used disk diffusion method

• breakpoint_S
  Lowest MIC value or highest number of millimetres that leads to "S"

• breakpoint_R
  Highest MIC value or lowest number of millimetres that leads to "R", can beNA

• uti
  Alogical value (TRUE/FALSE) to indicate whether the rule applies to a urinary tract infection (UTI)

• is_SDD
  Alogical value (TRUE/FALSE) to indicate whether the intermediate range between "S" and "R" should be interpreted as "SDD", instead of "I". This currently applies to 48 breakpoints.

Details

Different Types of Breakpoints

Supported types of breakpoints are ECOFF, animal, and human. ECOFF (Epidemiological cut-off) values are used in antimicrobial susceptibility testing to differentiate between wild-type and non-wild-type strains of bacteria or fungi.

The default is"human", which can also be set with the package optionAMR_breakpoint_type. Useas.sir(..., breakpoint_type = ...) to interpret raw data using a specific breakpoint type, e.g.as.sir(..., breakpoint_type = "ECOFF") to use ECOFFs.

Imported From WHONET

Clinical breakpoints in this package were validated through and imported fromWHONET, a free desktop Windows application developed and supported by the WHO Collaborating Centre for Surveillance of Antimicrobial Resistance. More can be read ontheir website. The developers of WHONET and thisAMR package have been in contact about sharing their work. We highly appreciate their great development on the WHONET software.

Our import and reproduction script can be found here:https://github.com/msberends/AMR/blob/main/data-raw/_reproduction_scripts/reproduction_of_clinical_breakpoints.R.

Response From CLSI and EUCAST

The CEO of CLSI and the chairman of EUCAST have endorsed the work and public use of thisAMR package (and consequently the use of their breakpoints) in June 2023, when future development of distributing clinical breakpoints was discussed in a meeting between CLSI, EUCAST, WHO, developers of WHONET software, and developers of thisAMR package.

Download Note

ThisAMR package (and the WHONET software as well) contains rather complex internal methods to apply the guidelines. For example, some breakpoints must be applied on certain species groups (which are in case of this package available through themicroorganisms.groups data set). It is important that this is considered when implementing the breakpoints for own use.

Download Our Reference Data

All reference data sets in the AMR package - including information on microorganisms, antimicrobials, and clinical breakpoints - are freely available for download in multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, and Stata.

For maximum compatibility, we also provide machine-readable, tab-separated plain text files suitable for use in any software, including laboratory information systems.

Visitour website for direct download links, or explore the actual files inour GitHub repository.

See Also

intrinsic_resistant

Examples

clinical_breakpoints

Count Available Isolates

Description

These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used insummarise() from thedplyr package and also support grouped variables, seeExamples.

count_resistant() should be used to count resistant isolates,count_susceptible() should be used to count susceptible isolates.

Usage

count_resistant(..., only_all_tested = FALSE)count_susceptible(..., only_all_tested = FALSE)count_S(..., only_all_tested = FALSE)count_SI(..., only_all_tested = FALSE)count_I(..., only_all_tested = FALSE)count_IR(..., only_all_tested = FALSE)count_R(..., only_all_tested = FALSE)count_all(..., only_all_tested = FALSE)n_sir(..., only_all_tested = FALSE)count_df(data, translate_ab = "name", language = get_AMR_locale(),  combine_SI = TRUE)

Arguments

Details

These functions are meant to count isolates. Use theresistance()/susceptibility() functions to calculate microbial resistance/susceptibility.

The functioncount_resistant() is equal to the functioncount_R(). The functioncount_susceptible() is equal to the functioncount_SI().

The functionn_sir() is an alias ofcount_all(). They can be used to count all available isolates, i.e. where all input antimicrobials have an available result (S, I or R). Their use is equal ton_distinct(). Their function is equal tocount_susceptible(...) + count_resistant(...).

The functioncount_df() takes any variable fromdata that has ansir class (created withas.sir()) and counts the number of S's, I's and R's. It also supports grouped variables. The functionsir_df() works exactly likecount_df(), but adds the percentage of S, I and R.

Value

Aninteger

Interpretation of SIR

In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R (https://www.eucast.org/newsiandr).

This AMR package follows insight; usesusceptibility() (equal toproportion_SI()) to determine antimicrobial susceptibility andcount_susceptible() (equal tocount_SI()) to count susceptible isolates.

Combination Therapy

When using more than one variable for... (= combination therapy), useonly_all_tested to only count isolates that are tested for all antimicrobials/variables that you test them for. See this example for two antimicrobials, Drug A and Drug B, about howsusceptibility() works to calculate the %SI:

--------------------------------------------------------------------                    only_all_tested = FALSE  only_all_tested = TRUE                    -----------------------  ----------------------- Drug A    Drug B   considered   considered  considered   considered                    susceptible    tested    susceptible    tested--------  --------  -----------  ----------  -----------  ---------- S or I    S or I        X            X           X            X   R       S or I        X            X           X            X  <NA>     S or I        X            X           -            - S or I      R           X            X           X            X   R         R           -            X           -            X  <NA>       R           -            -           -            - S or I     <NA>         X            X           -            -   R        <NA>         -            -           -            -  <NA>      <NA>         -            -           -            ---------------------------------------------------------------------

Please note that, in combination therapies, foronly_all_tested = TRUE applies that:

    count_S()    +   count_I()    +   count_R()    = count_all()  proportion_S() + proportion_I() + proportion_R() = 1

and that, in combination therapies, foronly_all_tested = FALSE applies that:

    count_S()    +   count_I()    +   count_R()    >= count_all()  proportion_S() + proportion_I() + proportion_R() >= 1

Usingonly_all_tested has no impact when only using one antibiotic as input.

See Also

proportion_* to calculate microbial resistance and susceptibility.

Examples

# example_isolates is a data set available in the AMR package.# run ?example_isolates for more info.# base R ------------------------------------------------------------count_resistant(example_isolates$AMX) # counts "R"count_susceptible(example_isolates$AMX) # counts "S" and "I"count_all(example_isolates$AMX) # counts "S", "I" and "R"# be more specificcount_S(example_isolates$AMX)count_SI(example_isolates$AMX)count_I(example_isolates$AMX)count_IR(example_isolates$AMX)count_R(example_isolates$AMX)# Count all available isolatescount_all(example_isolates$AMX)n_sir(example_isolates$AMX)# n_sir() is an alias of count_all().# Since it counts all available isolates, you can# calculate back to count e.g. susceptible isolates.# These results are the same:count_susceptible(example_isolates$AMX)susceptibility(example_isolates$AMX) * n_sir(example_isolates$AMX)# dplyr -------------------------------------------------------------if (require("dplyr")) {  example_isolates %>%    group_by(ward) %>%    summarise(      R = count_R(CIP),      I = count_I(CIP),      S = count_S(CIP),      n1 = count_all(CIP), # the actual total; sum of all three      n2 = n_sir(CIP), # same - analogous to n_distinct      total = n()    ) # NOT the number of tested isolates!  # Number of available isolates for a whole antibiotic class  # (i.e., in this data set columns GEN, TOB, AMK, KAN)  example_isolates %>%    group_by(ward) %>%    summarise(across(aminoglycosides(), n_sir))  # Count co-resistance between amoxicillin/clav acid and gentamicin,  # so we can see that combination therapy does a lot more than mono therapy.  # Please mind that `susceptibility()` calculates percentages right away instead.  example_isolates %>% count_susceptible(AMC) # 1433  example_isolates %>% count_all(AMC) # 1879  example_isolates %>% count_susceptible(GEN) # 1399  example_isolates %>% count_all(GEN) # 1855  example_isolates %>% count_susceptible(AMC, GEN) # 1764  example_isolates %>% count_all(AMC, GEN) # 1936  # Get number of S+I vs. R immediately of selected columns  example_isolates %>%    select(AMX, CIP) %>%    count_df(translate = FALSE)  # It also supports grouping variables  example_isolates %>%    select(ward, AMX, CIP) %>%    group_by(ward) %>%    count_df(translate = FALSE)}

Define Custom EUCAST Rules

Description

Define custom EUCAST rules for your organisation or specific analysis and use the output of this function ineucast_rules().

Usage

custom_eucast_rules(...)

Arguments

Details

Some organisations have their own adoption of EUCAST rules. This function can be used to define custom EUCAST rules to be used in theeucast_rules() function.

Basics

If you are familiar with thecase_when() function of thedplyr package, you will recognise the input method to set your own rules. Rules must be set using whatR considers to be the 'formula notation'. The rule itself is writtenbefore the tilde (~) and the consequence of the rule is writtenafter the tilde:

x <- custom_eucast_rules(TZP == "S" ~ aminopenicillins == "S",                         TZP == "R" ~ aminopenicillins == "R")

These are two custom EUCAST rules: if TZP (piperacillin/tazobactam) is "S", all aminopenicillins (ampicillin and amoxicillin) must be made "S", and if TZP is "R", aminopenicillins must be made "R". These rules can also be printed to the console, so it is immediately clear how they work:

x#> A set of custom EUCAST rules:#>#>   1. If TZP is "S" then set to  S :#>      amoxicillin (AMX), ampicillin (AMP)#>#>   2. If TZP is "R" then set to  R :#>      amoxicillin (AMX), ampicillin (AMP)

The rules (the partbefore the tilde, in above exampleTZP == "S" andTZP == "R") must be evaluable in your data set: it should be able to run as a filter in your data set without errors. This means for the above example that the columnTZP must exist. We will create a sample data set and test the rules set:

df <- data.frame(mo = c("Escherichia coli", "Klebsiella pneumoniae"),                 TZP = as.sir("R"),                 ampi = as.sir("S"),                 cipro = as.sir("S"))df#>                      mo TZP ampi cipro#> 1      Escherichia coli   R    S     S#> 2 Klebsiella pneumoniae   R    S     Seucast_rules(df,             rules = "custom",             custom_rules = x,             info = FALSE,             overwrite = TRUE)#>                      mo TZP ampi cipro#> 1      Escherichia coli   R    R     S#> 2 Klebsiella pneumoniae   R    R     S

Using taxonomic properties in rules

There is one exception in columns used for the rules: all column names of themicroorganisms data set can also be used, but do not have to exist in the data set. These column names are: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "oxygen_tolerance", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "mycobank", "mycobank_parent", "mycobank_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence", and "snomed". Thus, this next example will work as well, despite the fact that thedf data set does not contain a columngenus:

y <- custom_eucast_rules(  TZP == "S" & genus == "Klebsiella" ~ aminopenicillins == "S",  TZP == "R" & genus == "Klebsiella" ~ aminopenicillins == "R")eucast_rules(df,             rules = "custom",             custom_rules = y,             info = FALSE,             overwrite = TRUE)#>                      mo TZP ampi cipro#> 1      Escherichia coli   R    S     S#> 2 Klebsiella pneumoniae   R    R     S

Sharing rules among multiple users

The rules set (they object in this case) could be exported to a shared file location usingsaveRDS() if you collaborate with multiple users. The custom rules set could then be imported usingreadRDS().

Usage of multiple antimicrobials and antimicrobial group names

You can define antimicrobial groups instead of single antimicrobials for the rule consequence, which is the partafter the tilde (~). In the examples above, the antimicrobial groupaminopenicillins includes both ampicillin and amoxicillin.

Rules can also be applied to multiple antimicrobials and antimicrobial groups simultaneously. Use thec() function to combine multiple antimicrobials. For instance, the following example sets all aminopenicillins and ureidopenicillins to "R" if column TZP (piperacillin/tazobactam) is "R":

x <- custom_eucast_rules(TZP == "R" ~ c(aminopenicillins, ureidopenicillins) == "R")x#> A set of custom EUCAST rules:#>#>   1. If TZP is "R" then set to "R":#>      amoxicillin (AMX), ampicillin (AMP), azlocillin (AZL), mezlocillin (MEZ), piperacillin (PIP), piperacillin/tazobactam (TZP)

These 35 antimicrobial groups are allowed in the rules (case-insensitive) and can be used in any combination:

• aminoglycosides
  (amikacin, amikacin/fosfomycin, apramycin, arbekacin, astromicin, bekanamycin, dibekacin, framycetin, gentamicin, gentamicin-high, habekacin, hygromycin, isepamicin, kanamycin, kanamycin-high, kanamycin/cephalexin, micronomicin, neomycin, netilmicin, pentisomicin, plazomicin, propikacin, ribostamycin, sisomicin, streptoduocin, streptomycin, streptomycin-high, tobramycin, and tobramycin-high)

• aminopenicillins
  (amoxicillin and ampicillin)

• antifungals
  (amorolfine, amphotericin B, amphotericin B-high, anidulafungin, butoconazole, caspofungin, ciclopirox, clotrimazole, econazole, fluconazole, flucytosine, fosfluconazole, griseofulvin, hachimycin, ibrexafungerp, isavuconazole, isoconazole, itraconazole, ketoconazole, manogepix, micafungin, miconazole, nystatin, oteseconazole, pimaricin, posaconazole, rezafungin, ribociclib, sulconazole, terbinafine, terconazole, and voriconazole)

• antimycobacterials
  (4-aminosalicylic acid, calcium aminosalicylate, capreomycin, clofazimine, delamanid, enviomycin, ethambutol, ethambutol/isoniazid, ethionamide, isoniazid, isoniazid/sulfamethoxazole/trimethoprim/pyridoxine, morinamide, p-aminosalicylic acid, pretomanid, protionamide, pyrazinamide, rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, rifapentine, sodium aminosalicylate, streptomycin/isoniazid, terizidone, thioacetazone, thioacetazone/isoniazid, tiocarlide, and viomycin)

• betalactams
  (amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, benzylpenicillin screening test, biapenem, carbenicillin, carindacillin, carumonam, cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/amikacin, cefepime/clavulanic acid, cefepime/enmetazobactam, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefiderocol, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime screening test, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening test, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, ciclacillin, clometocillin, cloxacillin, dicloxacillin, doripenem, epicillin, ertapenem, flucloxacillin, hetacillin, imipenem, imipenem/EDTA, imipenem/relebactam, latamoxef, lenampicillin, loracarbef, mecillinam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nafcillin, oxacillin, oxacillin screening test, panipenem, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, razupenem, ritipenem, ritipenem acoxil, sarmoxicillin, sulbenicillin, sultamicillin, talampicillin, tebipenem, temocillin, ticarcillin, ticarcillin/clavulanic acid, and tigemonam)

• betalactams_with_inhibitor
  (amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin/sulbactam, aztreonam/avibactam, aztreonam/nacubactam, cefepime/amikacin, cefepime/clavulanic acid, cefepime/enmetazobactam, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefoperazone/sulbactam, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefpodoxime/clavulanic acid, ceftaroline/avibactam, ceftazidime/avibactam, ceftazidime/clavulanic acid, ceftolozane/tazobactam, ceftriaxone/beta-lactamase inhibitor, imipenem/relebactam, meropenem/nacubactam, meropenem/vaborbactam, mezlocillin/sulbactam, penicillin/novobiocin, penicillin/sulbactam, piperacillin/sulbactam, piperacillin/tazobactam, and ticarcillin/clavulanic acid)

• carbapenems
  (biapenem, doripenem, ertapenem, imipenem, imipenem/EDTA, imipenem/relebactam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, panipenem, razupenem, ritipenem, ritipenem acoxil, and tebipenem)

• cephalosporins
  (cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/amikacin, cefepime/clavulanic acid, cefepime/enmetazobactam, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefiderocol, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime screening test, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening test, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, latamoxef, and loracarbef)

• cephalosporins_1st
  (cefacetrile, cefadroxil, cefalexin, cefaloridine, cefalotin, cefapirin, cefatrizine, cefazedone, cefazolin, cefroxadine, ceftezole, and cephradine)

• cephalosporins_2nd
  (cefaclor, cefamandole, cefmetazole, cefonicid, ceforanide, cefotetan, cefotiam, cefoxitin, cefoxitin screening test, cefprozil, cefuroxime, cefuroxime axetil, and loracarbef)

• cephalosporins_3rd
  (cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefetamet, cefetamet pivoxil, cefixime, cefmenoxime, cefodizime, cefoperazone, cefoperazone/sulbactam, cefotaxime, cefotaxime screening test, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotiam hexetil, cefovecin, cefpimizole, cefpiramide, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefsulodin, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, and latamoxef)

• cephalosporins_4th
  (cefepime, cefepime/amikacin, cefepime/clavulanic acid, cefepime/enmetazobactam, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefetecol, cefoselis, cefozopran, cefpirome, and cefquinome)

• cephalosporins_5th
  (ceftaroline, ceftaroline/avibactam, ceftobiprole, ceftobiprole medocaril, and ceftolozane/tazobactam)

• cephalosporins_except_caz
  (cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/amikacin, cefepime/clavulanic acid, cefepime/enmetazobactam, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefiderocol, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime screening test, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening test, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, latamoxef, and loracarbef)

• fluoroquinolones
  (besifloxacin, ciprofloxacin, ciprofloxacin/metronidazole, ciprofloxacin/ornidazole, ciprofloxacin/tinidazole, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levofloxacin/ornidazole, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nemonoxacin, nifuroquine, nitroxoline, norfloxacin, norfloxacin screening test, norfloxacin/metronidazole, norfloxacin/tinidazole, ofloxacin, ofloxacin/ornidazole, orbifloxacin, pazufloxacin, pefloxacin, pefloxacin screening test, pradofloxacin, premafloxacin, prulifloxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin, and trovafloxacin)

• glycopeptides
  (avoparcin, bleomycin, dalbavancin, norvancomycin, oritavancin, ramoplanin, teicoplanin, teicoplanin-macromethod, telavancin, vancomycin, and vancomycin-macromethod)

• glycopeptides_except_lipo
  (avoparcin, bleomycin, norvancomycin, ramoplanin, teicoplanin, teicoplanin-macromethod, vancomycin, and vancomycin-macromethod)

• isoxazolylpenicillins
  (cloxacillin, dicloxacillin, flucloxacillin, meticillin, oxacillin, and oxacillin screening test)

• lincosamides
  (clindamycin, lincomycin, and pirlimycin)

• lipoglycopeptides
  (dalbavancin, oritavancin, and telavancin)

• macrolides
  (acetylmidecamycin, acetylspiramycin, azithromycin, clarithromycin, dirithromycin, erythromycin, flurithromycin, gamithromycin, josamycin, kitasamycin, meleumycin, midecamycin, miocamycin, nafithromycin, oleandomycin, rokitamycin, roxithromycin, solithromycin, spiramycin, telithromycin, tildipirosin, tilmicosin, troleandomycin, tulathromycin, tylosin, and tylvalosin)

• monobactams
  (aztreonam, aztreonam/avibactam, aztreonam/nacubactam, carumonam, and tigemonam)

• nitrofurans
  (furazidin, furazolidone, nifurtoinol, nitrofurantoin, and nitrofurazone)

• oxazolidinones
  (cadazolid, cycloserine, linezolid, tedizolid, and thiacetazone)

• penicillins
  (amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, azidocillin, azlocillin, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, benzylpenicillin screening test, carbenicillin, carindacillin, ciclacillin, clometocillin, cloxacillin, dicloxacillin, epicillin, flucloxacillin, hetacillin, lenampicillin, mecillinam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nafcillin, oxacillin, oxacillin screening test, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, sarmoxicillin, sulbenicillin, sultamicillin, talampicillin, temocillin, ticarcillin, and ticarcillin/clavulanic acid)

• phenicols
  (chloramphenicol, florfenicol, and thiamphenicol)

• polymyxins
  (colistin, polymyxin B, and polymyxin B/polysorbate 80)

• quinolones
  (besifloxacin, cinoxacin, ciprofloxacin, ciprofloxacin/metronidazole, ciprofloxacin/ornidazole, ciprofloxacin/tinidazole, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, flumequine, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levofloxacin/ornidazole, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nalidixic acid, nalidixic acid screening test, nemonoxacin, nifuroquine, nitroxoline, norfloxacin, norfloxacin screening test, norfloxacin/metronidazole, norfloxacin/tinidazole, ofloxacin, ofloxacin/ornidazole, orbifloxacin, oxolinic acid, pazufloxacin, pefloxacin, pefloxacin screening test, pipemidic acid, piromidic acid, pradofloxacin, premafloxacin, prulifloxacin, rosoxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin, and trovafloxacin)

• rifamycins
  (rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, and rifapentine)

• streptogramins
  (pristinamycin and quinupristin/dalfopristin)

• sulfonamides
  (brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim, sulfadimethoxine, sulfadimidine, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfamoxole, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, and sulfathiourea)

• tetracyclines
  (cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline, tetracycline screening test, and tigecycline)

• tetracyclines_except_tgc
  (cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline, and tetracycline screening test)

• trimethoprims
  (brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim, sulfadiazine/trimethoprim, sulfadimethoxine, sulfadimidine, sulfadimidine/trimethoprim, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamerazine/trimethoprim, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfametrole/trimethoprim, sulfamoxole, sulfamoxole/trimethoprim, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, sulfathiourea, trimethoprim, and trimethoprim/sulfamethoxazole)

• ureidopenicillins
  (azlocillin, mezlocillin, piperacillin, and piperacillin/tazobactam)

Value

Alist containing the custom rules

Examples

x <- custom_eucast_rules(  AMC == "R" & genus == "Klebsiella" ~ aminopenicillins == "R",  AMC == "I" & genus == "Klebsiella" ~ aminopenicillins == "I")x# run the custom rule set (verbose = TRUE will return a logbook instead of the data set):eucast_rules(example_isolates,  rules = "custom",  custom_rules = x,  info = FALSE,  overwrite = TRUE,  verbose = TRUE)# combine rule setsx2 <- c(  x,  custom_eucast_rules(TZP == "R" ~ carbapenems == "R"))x2

Define Custom MDRO Guideline

Description

Define custom a MDRO guideline for your organisation or specific analysis and use the output of this function inmdro().

Usage

custom_mdro_guideline(..., as_factor = TRUE)## S3 method for class 'custom_mdro_guideline'c(x, ..., as_factor = NULL)

Arguments

Details

Using a custom MDRO guideline is of importance if you have custom rules to determine MDROs in your hospital, e.g., rules that are dependent on ward, state of contact isolation or other variables in your data.

Basics

If you are familiar with thecase_when() function of thedplyr package, you will recognise the input method to set your own rules. Rules must be set using whatR considers to be the 'formula notation'. The rule itself is writtenbefore the tilde (~) and the consequence of the rule is writtenafter the tilde:

custom <- custom_mdro_guideline(CIP == "R" & age > 60 ~ "Elderly Type A",                                ERY == "R" & age > 60 ~ "Elderly Type B")

If a row/an isolate matches the first rule, the value after the first~ (in this case'Elderly Type A') will be set as MDRO value. Otherwise, the second rule will be tried and so on. The number of rules is unlimited.

You can print the rules set in the console for an overview. Colours will help reading it if your console supports colours.

custom#> A set of custom MDRO rules:#>   1. If CIP is R and age is higher than 60 then: Elderly Type A#>   2. If ERY is R and age is higher than 60 then: Elderly Type B#>   3. Otherwise: Negative#> Unmatched rows will return NA.#> Results will be of class 'factor', with ordered levels: Negative < Elderly Type A < Elderly Type B

The outcome of the function can be used for theguideline argument in themdro() function:

x <- mdro(example_isolates, guideline = custom)#> Determining MDROs based on custom rules, resulting in factor levels: Negative < Elderly Type A < Elderly Type B.#> - Custom MDRO rule 1: CIP == "R" & age > 60 (198 rows matched)#> - Custom MDRO rule 2: ERY == "R" & age > 60 (732 rows matched)#> => Found 930 custom defined MDROs out of 2000 isolates (46.5%)table(x)#> x#>       Negative  Elderly Type A  Elderly Type B#>           1070             198             732

Rules can also be combined with other custom rules by usingc():

x <- mdro(example_isolates,          guideline = c(custom,                        custom_mdro_guideline(ERY == "R" & age > 50 ~ "Elderly Type C")))#> Determining MDROs based on custom rules, resulting in factor levels: Negative < Elderly Type A < Elderly Type B < Elderly Type C.#> - Custom MDRO rule 1: CIP == "R" & age > 60 (198 rows matched)#> - Custom MDRO rule 2: ERY == "R" & age > 60 (732 rows matched)#> - Custom MDRO rule 3: ERY == "R" & age > 50 (109 rows matched)#> => Found 1039 custom defined MDROs out of 2000 isolates (52.0%)table(x)#> x#>       Negative  Elderly Type A  Elderly Type B  Elderly Type C#>            961             198             732             109

Sharing rules among multiple users

The rules set (thecustom object in this case) could be exported to a shared file location usingsaveRDS() if you collaborate with multiple users. The custom rules set could then be imported usingreadRDS().

Usage of multiple antimicrobials and antimicrobial group names

You can define antimicrobial groups instead of single antimicrobials for the rule itself, which is the partbefore the tilde (~). Useany() orall() to specify the scope of the antimicrobial group:

custom_mdro_guideline(  AMX == "R"                       ~ "My MDRO #1",  any(cephalosporins_2nd() == "R") ~ "My MDRO #2",  all(glycopeptides() == "R")      ~ "My MDRO #3")

All 35 antimicrobial selectors are supported for use in the rules:

• aminoglycosides() can select:
  amikacin, amikacin/fosfomycin, apramycin, arbekacin, astromicin, bekanamycin, dibekacin, framycetin, gentamicin, gentamicin-high, habekacin, hygromycin, isepamicin, kanamycin, kanamycin-high, kanamycin/cephalexin, micronomicin, neomycin, netilmicin, pentisomicin, plazomicin, propikacin, ribostamycin, sisomicin, streptoduocin, streptomycin, streptomycin-high, tobramycin, and tobramycin-high

• aminopenicillins() can select:
  amoxicillin and ampicillin

• antifungals() can select:
  amorolfine, amphotericin B, amphotericin B-high, anidulafungin, butoconazole, caspofungin, ciclopirox, clotrimazole, econazole, fluconazole, flucytosine, fosfluconazole, griseofulvin, hachimycin, ibrexafungerp, isavuconazole, isoconazole, itraconazole, ketoconazole, manogepix, micafungin, miconazole, nystatin, oteseconazole, pimaricin, posaconazole, rezafungin, ribociclib, sulconazole, terbinafine, terconazole, and voriconazole

• antimycobacterials() can select:
  4-aminosalicylic acid, calcium aminosalicylate, capreomycin, clofazimine, delamanid, enviomycin, ethambutol, ethambutol/isoniazid, ethionamide, isoniazid, isoniazid/sulfamethoxazole/trimethoprim/pyridoxine, morinamide, p-aminosalicylic acid, pretomanid, protionamide, pyrazinamide, rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, rifapentine, sodium aminosalicylate, streptomycin/isoniazid, terizidone, thioacetazone, thioacetazone/isoniazid, tiocarlide, and viomycin

• betalactams() can select:
  amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, benzylpenicillin screening test, biapenem, carbenicillin, carindacillin, carumonam, cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/amikacin, cefepime/clavulanic acid, cefepime/enmetazobactam, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefiderocol, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime screening test, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening test, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, ciclacillin, clometocillin, cloxacillin, dicloxacillin, doripenem, epicillin, ertapenem, flucloxacillin, hetacillin, imipenem, imipenem/EDTA, imipenem/relebactam, latamoxef, lenampicillin, loracarbef, mecillinam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nafcillin, oxacillin, oxacillin screening test, panipenem, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, razupenem, ritipenem, ritipenem acoxil, sarmoxicillin, sulbenicillin, sultamicillin, talampicillin, tebipenem, temocillin, ticarcillin, ticarcillin/clavulanic acid, and tigemonam

• betalactams_with_inhibitor() can select:
  amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin/sulbactam, aztreonam/avibactam, aztreonam/nacubactam, cefepime/amikacin, cefepime/clavulanic acid, cefepime/enmetazobactam, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefoperazone/sulbactam, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefpodoxime/clavulanic acid, ceftaroline/avibactam, ceftazidime/avibactam, ceftazidime/clavulanic acid, ceftolozane/tazobactam, ceftriaxone/beta-lactamase inhibitor, imipenem/relebactam, meropenem/nacubactam, meropenem/vaborbactam, mezlocillin/sulbactam, penicillin/novobiocin, penicillin/sulbactam, piperacillin/sulbactam, piperacillin/tazobactam, and ticarcillin/clavulanic acid

• carbapenems() can select:
  biapenem, doripenem, ertapenem, imipenem, imipenem/EDTA, imipenem/relebactam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, panipenem, razupenem, ritipenem, ritipenem acoxil, and tebipenem

• cephalosporins() can select:
  cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/amikacin, cefepime/clavulanic acid, cefepime/enmetazobactam, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefiderocol, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime screening test, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening test, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, latamoxef, and loracarbef

• cephalosporins_1st() can select:
  cefacetrile, cefadroxil, cefalexin, cefaloridine, cefalotin, cefapirin, cefatrizine, cefazedone, cefazolin, cefroxadine, ceftezole, and cephradine

• cephalosporins_2nd() can select:
  cefaclor, cefamandole, cefmetazole, cefonicid, ceforanide, cefotetan, cefotiam, cefoxitin, cefoxitin screening test, cefprozil, cefuroxime, cefuroxime axetil, and loracarbef

• cephalosporins_3rd() can select:
  cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefetamet, cefetamet pivoxil, cefixime, cefmenoxime, cefodizime, cefoperazone, cefoperazone/sulbactam, cefotaxime, cefotaxime screening test, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotiam hexetil, cefovecin, cefpimizole, cefpiramide, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefsulodin, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, and latamoxef

• cephalosporins_4th() can select:
  cefepime, cefepime/amikacin, cefepime/clavulanic acid, cefepime/enmetazobactam, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefetecol, cefoselis, cefozopran, cefpirome, and cefquinome

• cephalosporins_5th() can select:
  ceftaroline, ceftaroline/avibactam, ceftobiprole, ceftobiprole medocaril, and ceftolozane/tazobactam

• fluoroquinolones() can select:
  besifloxacin, ciprofloxacin, ciprofloxacin/metronidazole, ciprofloxacin/ornidazole, ciprofloxacin/tinidazole, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levofloxacin/ornidazole, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nemonoxacin, nifuroquine, nitroxoline, norfloxacin, norfloxacin screening test, norfloxacin/metronidazole, norfloxacin/tinidazole, ofloxacin, ofloxacin/ornidazole, orbifloxacin, pazufloxacin, pefloxacin, pefloxacin screening test, pradofloxacin, premafloxacin, prulifloxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin, and trovafloxacin

• glycopeptides() can select:
  avoparcin, bleomycin, dalbavancin, norvancomycin, oritavancin, ramoplanin, teicoplanin, teicoplanin-macromethod, telavancin, vancomycin, and vancomycin-macromethod

• isoxazolylpenicillins() can select:
  cloxacillin, dicloxacillin, flucloxacillin, meticillin, oxacillin, and oxacillin screening test

• lincosamides() can select:
  clindamycin, lincomycin, and pirlimycin

• lipoglycopeptides() can select:
  dalbavancin, oritavancin, and telavancin

• macrolides() can select:
  acetylmidecamycin, acetylspiramycin, azithromycin, clarithromycin, dirithromycin, erythromycin, flurithromycin, gamithromycin, josamycin, kitasamycin, meleumycin, midecamycin, miocamycin, nafithromycin, oleandomycin, rokitamycin, roxithromycin, solithromycin, spiramycin, telithromycin, tildipirosin, tilmicosin, troleandomycin, tulathromycin, tylosin, and tylvalosin

• monobactams() can select:
  aztreonam, aztreonam/avibactam, aztreonam/nacubactam, carumonam, and tigemonam

• nitrofurans() can select:
  furazidin, furazolidone, nifurtoinol, nitrofurantoin, and nitrofurazone

• oxazolidinones() can select:
  cadazolid, cycloserine, linezolid, tedizolid, and thiacetazone

• penicillins() can select:
  amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, azidocillin, azlocillin, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, benzylpenicillin screening test, carbenicillin, carindacillin, ciclacillin, clometocillin, cloxacillin, dicloxacillin, epicillin, flucloxacillin, hetacillin, lenampicillin, mecillinam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nafcillin, oxacillin, oxacillin screening test, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, sarmoxicillin, sulbenicillin, sultamicillin, talampicillin, temocillin, ticarcillin, and ticarcillin/clavulanic acid

• phenicols() can select:
  chloramphenicol, florfenicol, and thiamphenicol

• polymyxins() can select:
  colistin, polymyxin B, and polymyxin B/polysorbate 80

• quinolones() can select:
  besifloxacin, cinoxacin, ciprofloxacin, ciprofloxacin/metronidazole, ciprofloxacin/ornidazole, ciprofloxacin/tinidazole, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, flumequine, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levofloxacin/ornidazole, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nalidixic acid, nalidixic acid screening test, nemonoxacin, nifuroquine, nitroxoline, norfloxacin, norfloxacin screening test, norfloxacin/metronidazole, norfloxacin/tinidazole, ofloxacin, ofloxacin/ornidazole, orbifloxacin, oxolinic acid, pazufloxacin, pefloxacin, pefloxacin screening test, pipemidic acid, piromidic acid, pradofloxacin, premafloxacin, prulifloxacin, rosoxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin, and trovafloxacin

• rifamycins() can select:
  rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, and rifapentine

• streptogramins() can select:
  pristinamycin and quinupristin/dalfopristin

• sulfonamides() can select:
  brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim, sulfadimethoxine, sulfadimidine, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfamoxole, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, and sulfathiourea

• tetracyclines() can select:
  cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline, tetracycline screening test, and tigecycline

• trimethoprims() can select:
  brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim, sulfadiazine/trimethoprim, sulfadimethoxine, sulfadimidine, sulfadimidine/trimethoprim, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamerazine/trimethoprim, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfametrole/trimethoprim, sulfamoxole, sulfamoxole/trimethoprim, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, sulfathiourea, trimethoprim, and trimethoprim/sulfamethoxazole

• ureidopenicillins() can select:
  azlocillin, mezlocillin, piperacillin, and piperacillin/tazobactam

Value

Alist containing the custom rules

Examples

x <- custom_mdro_guideline(  CIP == "R" & age > 60 ~ "Elderly Type A",  ERY == "R" & age > 60 ~ "Elderly Type B")x# run the custom rule set (verbose = TRUE will return a logbook instead of the data set):out <- mdro(example_isolates, guideline = x)table(out)out <- mdro(example_isolates, guideline = x, verbose = TRUE)head(out)# you can create custom guidelines using selectors (see ?antimicrobial_selectors)my_guideline <- custom_mdro_guideline(  AMX == "R" ~ "Custom MDRO 1",  all(cephalosporins_2nd() == "R") ~ "Custom MDRO 2")my_guidelineout <- mdro(example_isolates, guideline = my_guideline)table(out)

Data Set with Treatment Dosages as Defined by EUCAST

Description

EUCAST breakpoints used in this package are based on the dosages in this data set. They can be retrieved witheucast_dosage().

Usage

dosage

Format

Atibble with 759 observations and 9 variables:

• ab
  Antimicrobial ID as used in this package (such asAMC), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available

• name
  Official name of the antimicrobial drug as used by WHONET/EARS-Net or the WHO

• type
  Type of the dosage, either "high_dosage", "standard_dosage", or "uncomplicated_uti"

• dose
  Dose, such as "2 g" or "25 mg/kg"

• dose_times
  Number of times a dose must be administered

• administration
  Route of administration, either "", "im", "iv", or "oral"

• notes
  Additional dosage notes

• original_txt
  Original text in the PDF file of EUCAST

• eucast_version
  Version number of the EUCAST Clinical Breakpoints guideline to which these dosages apply, either 15, 14, 13.1, 12, or 11

Download Our Reference Data

All reference data sets in the AMR package - including information on microorganisms, antimicrobials, and clinical breakpoints - are freely available for download in multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, and Stata.

For maximum compatibility, we also provide machine-readable, tab-separated plain text files suitable for use in any software, including laboratory information systems.

Visitour website for direct download links, or explore the actual files inour GitHub repository.

Examples

dosage

Apply EUCAST Rules

Description

Apply rules from clinical breakpoints notes and expected resistant phenotypes as defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST,https://www.eucast.org), seeSource. Useeucast_dosage() to get adata.frame with advised dosages of a certain bug-drug combination, which is based on thedosage data set.

To improve the interpretation of the antibiogram before EUCAST rules are applied, some non-EUCAST rules can applied at default, seeDetails.

Usage

eucast_rules(x, col_mo = NULL, info = interactive(),  rules = getOption("AMR_eucastrules", default = c("breakpoints",  "expected_phenotypes")), verbose = FALSE, version_breakpoints = 15,  version_expected_phenotypes = 1.2, version_expertrules = 3.3,  ampc_cephalosporin_resistance = NA, only_sir_columns = any(is.sir(x)),  custom_rules = NULL, overwrite = FALSE, ...)eucast_dosage(ab, administration = "iv", version_breakpoints = 15)

Arguments

Details

Note: This function does not translate MIC values to SIR values. Useas.sir() for that.
Note: When ampicillin (AMP, J01CA01) is not available but amoxicillin (AMX, J01CA04) is, the latter will be used for all rules where there is a dependency on ampicillin. These drugs are interchangeable when it comes to expression of antimicrobial resistance.

The file containing all EUCAST rules is located here:https://github.com/msberends/AMR/blob/main/data-raw/eucast_rules.tsv.Note: Old taxonomic names are replaced with the current taxonomy where applicable. For example,Ochrobactrum anthropi was renamed toBrucella anthropi in 2020; the original EUCAST rules v3.1 and v3.2 did not yet contain this new taxonomic name. TheAMR package contains the full microbial taxonomy updated until June 24th, 2024, seemicroorganisms.

Custom Rules

Custom rules can be created usingcustom_eucast_rules(), e.g.:

x <- custom_eucast_rules(AMC == "R" & genus == "Klebsiella" ~ aminopenicillins == "R",                         AMC == "I" & genus == "Klebsiella" ~ aminopenicillins == "I")eucast_rules(example_isolates, rules = "custom", custom_rules = x)

'Other' Rules

Before further processing, two non-EUCAST rules about drug combinations can be applied to improve the efficacy of the EUCAST rules, and the reliability of your data (analysis). These rules are:

1. A drugwith enzyme inhibitor will be set to S if the same drugwithout enzyme inhibitor is S

2. A drugwithout enzyme inhibitor will be set to R if the same drugwith enzyme inhibitor is R

Important examples include amoxicillin and amoxicillin/clavulanic acid, and trimethoprim and trimethoprim/sulfamethoxazole. Needless to say, for these rules to work, both drugs must be available in the data set.

Since these rules are not officially approved by EUCAST, they are not applied at default. To use these rules, include"other" to therules argument, or useeucast_rules(..., rules = "all"). You can also set the package optionAMR_eucastrules, i.e. runoptions(AMR_eucastrules = "all").

Value

The input ofx, possibly with edited values of antimicrobials. Or, ifverbose = TRUE, adata.frame with all original and new values of the affected bug-drug combinations.

Download Our Reference Data

All reference data sets in the AMR package - including information on microorganisms, antimicrobials, and clinical breakpoints - are freely available for download in multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, and Stata.

For maximum compatibility, we also provide machine-readable, tab-separated plain text files suitable for use in any software, including laboratory information systems.

Visitour website for direct download links, or explore the actual files inour GitHub repository.

Source

• EUCAST Expert Rules. Version 2.0, 2012.
  Leclercq et al.EUCAST expert rules in antimicrobial susceptibility testing.Clin Microbiol Infect. 2013;19(2):141-60;doi:10.1111/j.1469-0691.2011.03703.x

• EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes Tables. Version 3.1, 2016.(link)

• EUCAST Intrinsic Resistance and Unusual Phenotypes. Version 3.2, 2020.(link)

• EUCAST Intrinsic Resistance and Unusual Phenotypes. Version 3.3, 2021.(link)

• EUCAST Breakpoint tables for interpretation of MICs and zone diameters. Version 9.0, 2019.(link)

• EUCAST Breakpoint tables for interpretation of MICs and zone diameters. Version 10.0, 2020.(link)

• EUCAST Breakpoint tables for interpretation of MICs and zone diameters. Version 11.0, 2021.(link)

• EUCAST Breakpoint tables for interpretation of MICs and zone diameters. Version 12.0, 2022.(link)

Examples

a <- data.frame(  mo = c(    "Staphylococcus aureus",    "Enterococcus faecalis",    "Escherichia coli",    "Klebsiella pneumoniae",    "Pseudomonas aeruginosa"  ),  VAN = "-", # Vancomycin  AMX = "-", # Amoxicillin  COL = "-", # Colistin  CAZ = "-", # Ceftazidime  CXM = "-", # Cefuroxime  PEN = "S", # Benzylpenicillin  FOX = "S", # Cefoxitin  stringsAsFactors = FALSE)head(a)# apply EUCAST rules: some results wil be changedb <- eucast_rules(a, overwrite = TRUE)head(b)# do not apply EUCAST rules, but rather get a data.frame# containing all details about the transformations:c <- eucast_rules(a, overwrite = TRUE, verbose = TRUE)head(c)# Dosage guidelines:eucast_dosage(c("tobra", "genta", "cipro"), "iv")eucast_dosage(c("tobra", "genta", "cipro"), "iv", version_breakpoints = 10)

Data Set with 2 000 Example Isolates

Description

A data set containing 2 000 microbial isolates with their full antibiograms. This data set contains randomised fictitious data, but reflects reality and can be used to practise AMR data analysis. For examples, please readthe tutorial on our website.

Usage

example_isolates

Format

Atibble with 2 000 observations and 46 variables:

• date
  Date of receipt at the laboratory

• patient
  ID of the patient

• age
  Age of the patient

• gender
  Gender of the patient, either "F" or "M"

• ward
  Ward type where the patient was admitted, either "Clinical", "ICU", or "Outpatient"

• mo
  ID of microorganism created withas.mo(), see also themicroorganisms data set

• PEN:RIF
  40 different antimicrobials with classsir (seeas.sir()); these column names occur in theantimicrobials data set and can be translated withset_ab_names() orab_name()

Download Our Reference Data

All reference data sets in the AMR package - including information on microorganisms, antimicrobials, and clinical breakpoints - are freely available for download in multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, and Stata.

For maximum compatibility, we also provide machine-readable, tab-separated plain text files suitable for use in any software, including laboratory information systems.

Visitour website for direct download links, or explore the actual files inour GitHub repository.

Examples

example_isolates

Data Set with Unclean Data

Description

A data set containing 3 000 microbial isolates that are not cleaned up and consequently not ready for AMR data analysis. This data set can be used for practice.

Usage

example_isolates_unclean

Format

Atibble with 3 000 observations and 8 variables:

• patient_id
  ID of the patient

• date
  date of receipt at the laboratory

• hospital
  ID of the hospital, from A to C

• bacteria
  info about microorganism that can be transformed withas.mo(), see alsomicroorganisms

• AMX:GEN
  4 different antimicrobials that have to be transformed withas.sir()

Download Our Reference Data

All reference data sets in the AMR package - including information on microorganisms, antimicrobials, and clinical breakpoints - are freely available for download in multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, and Stata.

For maximum compatibility, we also provide machine-readable, tab-separated plain text files suitable for use in any software, including laboratory information systems.

Visitour website for direct download links, or explore the actual files inour GitHub repository.

Examples

example_isolates_unclean

Export Data Set as NCBI BioSample Antibiogram

Description

Export Data Set as NCBI BioSample Antibiogram

Usage

export_ncbi_biosample(x, filename = paste0("biosample_", format(Sys.time(),  "%Y-%m-%d-%H%M%S"), ".xlsx"), type = "pathogen MIC",  columns = where(is.mic), save_as_xlsx = TRUE)

Arguments

Determine First Isolates

Description

Determine first isolates of all microorganisms of every patient per episode and (if needed) per specimen type. These functions support all four methods as summarised by Hindleret al. in 2007 (doi:10.1086/511864). To determine patient episodes not necessarily based on microorganisms, useis_new_episode() that also supports grouping with thedplyr package.

Usage

first_isolate(x = NULL, col_date = NULL, col_patient_id = NULL,  col_mo = NULL, col_testcode = NULL, col_specimen = NULL,  col_icu = NULL, col_keyantimicrobials = NULL, episode_days = 365,  testcodes_exclude = NULL, icu_exclude = FALSE, specimen_group = NULL,  type = "points", method = c("phenotype-based", "episode-based",  "patient-based", "isolate-based"), ignore_I = TRUE, points_threshold = 2,  info = interactive(), include_unknown = FALSE,  include_untested_sir = TRUE, ...)filter_first_isolate(x = NULL, col_date = NULL, col_patient_id = NULL,  col_mo = NULL, episode_days = 365, method = c("phenotype-based",  "episode-based", "patient-based", "isolate-based"), ...)

Arguments

Details

The methodology implemented in these functions is strictly based on the recommendations outlined inCLSI Guideline M39 and the research overview by Hindleret al. (2007,doi:10.1086/511864).

To conduct epidemiological analyses on antimicrobial resistance data, only so-called first isolates should be included to prevent overestimation and underestimation of antimicrobial resistance. Different methods can be used to do so, see below.

These functions are context-aware. This means that thex argument can be left blank if used inside adata.frame call, seeExamples.

Thefirst_isolate() function is a wrapper around theis_new_episode() function, but more efficient for data sets containing microorganism codes or names.

All isolates with a microbial ID ofNA will be excluded as first isolate.

Different methods

According to previously-mentioned sources, there are different methods (algorithms) to select first isolates with increasing reliability: isolate-based, patient-based, episode-based and phenotype-based. All methods select on a combination of the taxonomic genus and species (not subspecies).

All mentioned methods are covered in thefirst_isolate() function:

Isolate-based

Minimum variables required: Microorganism identifier

This method does not require any selection, as all isolates should be included. It does, however, respect all arguments set in thefirst_isolate() function. For example, the default setting forinclude_unknown (FALSE) will omit selection of rows without a microbial ID.

Patient-based

Minimum variables required: Microorganism identifier, Patient identifier

This method includes every genus-species combination per patient once. This method makes sure that no duplicate isolates are selected from the same patient. This method is preferred to e.g. identify the first MRSA finding of each patient to determine the incidence. Conversely, in a large longitudinal data set, this could mean that isolates areexcluded that were found years after the initial isolate.

Episode-based

Minimum variables required: Microorganism identifier, Patient identifier, Date

To include every genus-species combination per patient episode once, set theepisode_days to a sensible number of days. Depending on the type of analysis, this could be e.g., 14, 30, 60 or 365. Short episodes are common for analysing specific hospital or ward data or ICU cases, long episodes are common for analysing regional and national data.

This is the most common method to correct for duplicate isolates. Patients are categorised into episodes based on their ID and dates (e.g., the date of specimen receipt or laboratory result). While this is a common method, it does not take into account antimicrobial test results. This means that e.g. a methicillin-resistantStaphylococcus aureus (MRSA) isolate cannot be differentiated from a wildtypeStaphylococcus aureus isolate.

Phenotype-based

Minimum variables required: Microorganism identifier, Patient identifier, Date, Antimicrobial test results

This is a more reliable method, since it alsoweighs the antibiogram (antimicrobial test results) yielding so-called 'first weighted isolates'. There are two different methods to weigh the antibiogram:

1. Usingtype = "points" and argumentpoints_threshold (default)

   This method weighsall antimicrobial drugs available in the data set. Any difference from I to S or R (or vice versa) counts as0.5 points, a difference from S to R (or vice versa) counts as1 point. When the sum of points exceedspoints_threshold, which defaults to2, an isolate will be selected as a first weighted isolate.

   All antimicrobials are internally selected using theall_antimicrobials() function. The output of this function does not need to be passed to thefirst_isolate() function.

2. Usingtype = "keyantimicrobials" and argumentignore_I

   This method only weighs specific antimicrobial drugs, calledkey antimicrobials. Any difference from S to R (or vice versa) in these key antimicrobials will select an isolate as a first weighted isolate. Withignore_I = FALSE, also differences from I to S or R (or vice versa) will lead to this.

   Key antimicrobials are internally selected using thekey_antimicrobials() function, but can also be added manually as a variable to the data and set in thecol_keyantimicrobials argument. Another option is to pass the output of thekey_antimicrobials() function directly to thecol_keyantimicrobials argument.

The default method is phenotype-based (usingtype = "points") and episode-based (usingepisode_days = 365). This makes sure that every genus-species combination is selected per patient once per year, while taking into account all antimicrobial test results. If no antimicrobial test results are available in the data set, only the episode-based method is applied at default.

Value

Alogical vector

Source

Methodology of these functions is strictly based on:

• M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition, 2022,Clinical and Laboratory Standards Institute (CLSI).https://clsi.org/standards/products/microbiology/documents/m39/.

• Hindler JF and Stelling J (2007).Analysis and Presentation of Cumulative Antibiograms: A New Consensus Guideline from the Clinical and Laboratory Standards Institute. Clinical Infectious Diseases, 44(6), 867-873.doi:10.1086/511864

See Also

key_antimicrobials()

Examples

# `example_isolates` is a data set available in the AMR package.# See ?example_isolates.example_isolates[first_isolate(info = TRUE), ]# get all first Gram-negativesexample_isolates[which(first_isolate(info = FALSE) & mo_is_gram_negative()), ]if (require("dplyr")) {  # filter on first isolates using dplyr:  example_isolates %>%    filter(first_isolate(info = TRUE))}if (require("dplyr")) {  # short-hand version:  example_isolates %>%    filter_first_isolate(info = FALSE)}if (require("dplyr")) {  # flag the first isolates per group:  example_isolates %>%    group_by(ward) %>%    mutate(first = first_isolate(info = TRUE)) %>%    select(ward, date, patient, mo, first)}

G-test for Count Data

Description

g.test() performs chi-squared contingency table tests and goodness-of-fit tests, just likechisq.test() but is more reliable (1). AG-test can be used to see whether the number of observations in each category fits a theoretical expectation (called aG-test of goodness-of-fit), or to see whether the proportions of one variable are different for different values of the other variable (called aG-test of independence).

Usage

g.test(x, y = NULL, p = rep(1/length(x), length(x)), rescale.p = FALSE)

Arguments

Details

Ifx is amatrix with one row or column, or ifx is a vector andy is not given, then agoodness-of-fit test is performed (x is treated as a one-dimensional contingency table). The entries ofx must be non-negative integers. In this case, the hypothesis tested is whether the population probabilities equal those inp, or are all equal ifp is not given.

Ifx is amatrix with at least two rows and columns, it is taken as a two-dimensional contingency table: the entries ofx must be non-negative integers. Otherwise,x andy must be vectors or factors of the same length; cases with missing values are removed, the objects are coerced to factors, and the contingency table is computed from these. Then Pearson's chi-squared test is performed of the null hypothesis that the joint distribution of the cell counts in a 2-dimensional contingency table is the product of the row and column marginals.

The p-value is computed from the asymptotic chi-squared distribution of the test statistic.

In the contingency table case simulation is done by random sampling from the set of all contingency tables with given marginals, and works only if the marginals are strictly positive. Note that this is not the usual sampling situation assumed for a chi-squared test (such as theG-test) but rather that for Fisher's exact test.

In the goodness-of-fit case simulation is done by random sampling from the discrete distribution specified byp, each sample being of sizen = sum(x). This simulation is done inR and may be slow.

G-test Of Goodness-of-Fit (Likelihood Ratio Test)

Use theG-test of goodness-of-fit when you have one nominal variable with two or more values (such as male and female, or red, pink and white flowers). You compare the observed counts of numbers of observations in each category with the expected counts, which you calculate using some kind of theoretical expectation (such as a 1:1 sex ratio or a 1:2:1 ratio in a genetic cross).

If the expected number of observations in any category is too small, theG-test may give inaccurate results, and you should use an exact test instead (fisher.test()).

TheG-test of goodness-of-fit is an alternative to the chi-square test of goodness-of-fit (chisq.test()); each of these tests has some advantages and some disadvantages, and the results of the two tests are usually very similar.

G-test of Independence

Use theG-test of independence when you have two nominal variables, each with two or more possible values. You want to know whether the proportions for one variable are different among values of the other variable.

It is also possible to do aG-test of independence with more than two nominal variables. For example, Jackson et al. (2013) also had data for children under 3, so you could do an analysis of old vs. young, thigh vs. arm, and reaction vs. no reaction, all analyzed together.

Fisher's exact test (fisher.test()) is anexact test, where theG-test is still only anapproximation. For any 2x2 table, Fisher's Exact test may be slower but will still run in seconds, even if the sum of your observations is multiple millions.

TheG-test of independence is an alternative to the chi-square test of independence (chisq.test()), and they will give approximately the same results.

How the Test Works

Unlike the exact test of goodness-of-fit (fisher.test()), theG-test does not directly calculate the probability of obtaining the observed results or something more extreme. Instead, like almost all statistical tests, theG-test has an intermediate step; it uses the data to calculate a test statistic that measures how far the observed data are from the null expectation. You then use a mathematical relationship, in this case the chi-square distribution, to estimate the probability of obtaining that value of the test statistic.

TheG-test uses the log of the ratio of two likelihoods as the test statistic, which is why it is also called a likelihood ratio test or log-likelihood ratio test. The formula to calculate aG-statistic is:

G = 2 * sum(x * log(x / E))

whereE are the expected values. Since this is chi-square distributed, the p value can be calculated inR with:

p <- stats::pchisq(G, df, lower.tail = FALSE)

wheredf are the degrees of freedom.

If there are more than two categories and you want to find out which ones are significantly different from their null expectation, you can use the same method of testing each category vs. the sum of all categories, with the Bonferroni correction. You useG-tests for each category, of course.

Value

A list with class"htest" containing the followingcomponents:

Source

The code for this function is identical to that ofchisq.test(), except that:

• The calculation of the statistic was changed to2 * sum(x * log(x / E))

• Yates' continuity correction was removed as it does not apply to aG-test

• The possibility to simulate p values withsimulate.p.value was removed

References

1. McDonald, J.H. 2014.Handbook of Biological Statistics (3rd ed.). Sparky House Publishing, Baltimore, Maryland.

See Also

chisq.test()

Examples

# = EXAMPLE 1 =# Shivrain et al. (2006) crossed clearfield rice (which are resistant# to the herbicide imazethapyr) with red rice (which are susceptible to# imazethapyr). They then crossed the hybrid offspring and examined the# F2 generation, where they found 772 resistant plants, 1611 moderately# resistant plants, and 737 susceptible plants. If resistance is controlled# by a single gene with two co-dominant alleles, you would expect a 1:2:1# ratio.x <- c(772, 1611, 737)g.test(x, p = c(1, 2, 1) / 4)# There is no significant difference from a 1:2:1 ratio.# Meaning: resistance controlled by a single gene with two co-dominant# alleles, is plausible.# = EXAMPLE 2 =# Red crossbills (Loxia curvirostra) have the tip of the upper bill either# right or left of the lower bill, which helps them extract seeds from pine# cones. Some have hypothesized that frequency-dependent selection would# keep the number of right and left-billed birds at a 1:1 ratio. Groth (1992)# observed 1752 right-billed and 1895 left-billed crossbills.x <- c(1752, 1895)g.test(x)# There is a significant difference from a 1:1 ratio.# Meaning: there are significantly more left-billed birds.

Determine Clinical or Epidemic Episodes

Description

These functions determine which items in a vector can be considered (the start of) a new episode. This can be used to determine clinical episodes for any epidemiological analysis. Theget_episode() function returns the index number of the episode per group, while theis_new_episode() function returnsTRUE for every newget_episode() index. Both absolute and relative episode determination are supported.

Usage

get_episode(x, episode_days = NULL, case_free_days = NULL, ...)is_new_episode(x, episode_days = NULL, case_free_days = NULL, ...)

Arguments

Details

Episodes can be determined in two ways: absolute and relative.

1. Absolute

   This method usesepisode_days to define an episode length in days, after which a new episode will start. A common use case in AMR data analysis is microbial epidemiology: episodes ofS. aureus bacteraemia in ICU patients for example. The episode length could then be 30 days, so that newS. aureus isolates after an ICU episode of 30 days will be considered a different (or new) episode.

   Thus, this method countssince the start of the previous episode.

2. Relative

   This method usescase_free_days to quantify the duration of case-free days (the inter-epidemic interval), after which a new episode will start. A common use case is infectious disease epidemiology: episodes of norovirus outbreaks in a hospital for example. The case-free period could then be 14 days, so that new norovirus cases after that time will be considered a different (or new) episode.

   Thus, this methods countssince the last case in the previous episode.

In a table:

** This marks the start of a new episode, because 8 January 2023 is more than 7 days since the start of the previous episode (1 January 2023).
*** This marks the start of a new episode, because 21 January 2023 is more than 7 days since the last case in the previous episode (8 January 2023).

Eitherepisode_days orcase_free_days must be provided in the function.

Difference betweenget_episode() andis_new_episode()

Theget_episode() function returns the index number of the episode, so all cases/patients/isolates in the first episode will have the number 1, all cases/patients/isolates in the second episode will have the number 2, etc.

Theis_new_episode() function on the other hand, returnsTRUE for every newget_episode() index.

To specify, when settingepisode_days = 365 (using method 1 as explained above), this is how the two functions differ:

Other

Thefirst_isolate() function is a wrapper around theis_new_episode() function, but is more efficient for data sets containing microorganism codes or names and allows for different isolate selection methods.

Thedplyr package is not required for these functions to work, but these episode functions do supportvariable grouping and work conveniently insidedplyr verbs such asfilter(),mutate() andsummarise().

Value

• get_episode(): aninteger vector

• is_new_episode(): alogical vector

See Also

first_isolate()

Examples

# difference between absolute and relative determination of episodes:x <- data.frame(dates = as.Date(c(  "2021-01-01",  "2021-01-02",  "2021-01-05",  "2021-01-08",  "2021-02-21",  "2021-02-22",  "2021-02-23",  "2021-02-24",  "2021-03-01",  "2021-03-01")))x$absolute <- get_episode(x$dates, episode_days = 7)x$relative <- get_episode(x$dates, case_free_days = 7)x# `example_isolates` is a data set available in the AMR package.# See ?example_isolatesdf <- example_isolates[sample(seq_len(2000), size = 100), ]get_episode(df$date, episode_days = 60) # indicesis_new_episode(df$date, episode_days = 60) # TRUE/FALSE# filter on results from the third 60-day episode only, using base Rdf[which(get_episode(df$date, 60) == 3), ]# the functions also work for less than a day, e.g. to include one per hour:get_episode(  c(    Sys.time(),    Sys.time() + 60 * 60  ),  episode_days = 1 / 24)if (require("dplyr")) {  # is_new_episode() can also be used in dplyr verbs to determine patient  # episodes based on any (combination of) grouping variables:  df %>%    mutate(condition = sample(      x = c("A", "B", "C"),      size = 100,      replace = TRUE    )) %>%    group_by(patient, condition) %>%    mutate(new_episode = is_new_episode(date, 365)) %>%    select(patient, date, condition, new_episode) %>%    arrange(patient, condition, date)}if (require("dplyr")) {  df %>%    group_by(ward, patient) %>%    transmute(date,      patient,      new_index = get_episode(date, 60),      new_logical = is_new_episode(date, 60)    ) %>%    arrange(patient, ward, date)}if (require("dplyr")) {  df %>%    group_by(ward) %>%    summarise(      n_patients = n_distinct(patient),      n_episodes_365 = sum(is_new_episode(date, episode_days = 365)),      n_episodes_60 = sum(is_new_episode(date, episode_days = 60)),      n_episodes_30 = sum(is_new_episode(date, episode_days = 30))    )}# grouping on patients and microorganisms leads to the same# results as first_isolate() when using 'episode-based':if (require("dplyr")) {  x <- df %>%    filter_first_isolate(      include_unknown = TRUE,      method = "episode-based"    )  y <- df %>%    group_by(patient, mo) %>%    filter(is_new_episode(date, 365)) %>%    ungroup()  identical(x, y)}# but is_new_episode() has a lot more flexibility than first_isolate(),# since you can now group on anything that seems relevant:if (require("dplyr")) {  df %>%    group_by(patient, mo, ward) %>%    mutate(flag_episode = is_new_episode(date, 365)) %>%    select(group_vars(.), flag_episode)}

PCA Biplot withggplot2

Description

Produces aggplot2 variant of a so-calledbiplot for PCA (principal component analysis), but is more flexible and more appealing than the baseRbiplot() function.

Usage

ggplot_pca(x, choices = 1:2, scale = 1, pc.biplot = TRUE,  labels = NULL, labels_textsize = 3, labels_text_placement = 1.5,  groups = NULL, ellipse = TRUE, ellipse_prob = 0.68,  ellipse_size = 0.5, ellipse_alpha = 0.5, points_size = 2,  points_alpha = 0.25, arrows = TRUE, arrows_colour = "darkblue",  arrows_size = 0.5, arrows_textsize = 3, arrows_textangled = TRUE,  arrows_alpha = 0.75, base_textsize = 10, ...)

Arguments

Details

The colours for labels and points can be changed by adding another scale layer for colour, such asscale_colour_viridis_d() andscale_colour_brewer().

Source

Theggplot_pca() function is based on theggbiplot() function from theggbiplot package by Vince Vu, as found on GitHub:https://github.com/vqv/ggbiplot (retrieved: 2 March 2020, their latest commit:7325e88; 12 February 2015).

As per their GPL-2 licence that demands documentation of code changes, the changes made based on the source code were:

1. Rewritten code to remove the dependency on packagesplyr,scales andgrid

2. Parametrised more options, like arrow and ellipse settings

3. Hardened all input possibilities by defining the exact type of user input for every argument

4. Added total amount of explained variance as a caption in the plot

5. Cleaned all syntax based on thelintr package, fixed grammatical errors and added integrity checks

6. Updated documentation

Examples

# `example_isolates` is a data set available in the AMR package.# See ?example_isolates.if (require("dplyr")) {  # calculate the resistance per group first  resistance_data <- example_isolates %>%    group_by(      order = mo_order(mo), # group on anything, like order      genus = mo_genus(mo)    ) %>% #   and genus as we do here;    filter(n() >= 30) %>% # filter on only 30 results per group    summarise_if(is.sir, resistance) # then get resistance of all drugs  # now conduct PCA for certain antimicrobial drugs  pca_result <- resistance_data %>%    pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)  summary(pca_result)  # old base R plotting method:  biplot(pca_result, main = "Base R biplot")  # new ggplot2 plotting method using this package:  if (require("ggplot2")) {    ggplot_pca(pca_result) +      labs(title = "ggplot2 biplot")  }  if (require("ggplot2")) {    # still extendible with any ggplot2 function    ggplot_pca(pca_result) +      scale_colour_viridis_d() +      labs(title = "ggplot2 biplot")  }}

AMR Plots withggplot2

Description

Use these functions to create bar plots for AMR data analysis. All functions rely onggplot2 functions.

Usage

ggplot_sir(data, position = NULL, x = "antibiotic",  fill = "interpretation", facet = NULL, breaks = seq(0, 1, 0.1),  limits = NULL, translate_ab = "name", combine_SI = TRUE,  minimum = 30, language = get_AMR_locale(), nrow = NULL, colours = c(S  = "#3CAEA3", SDD = "#8FD6C4", SI = "#3CAEA3", I = "#F6D55C", IR = "#ED553B",  R = "#ED553B"), datalabels = TRUE, datalabels.size = 2.5,  datalabels.colour = "grey15", title = NULL, subtitle = NULL,  caption = NULL, x.title = "Antimicrobial", y.title = "Proportion", ...)geom_sir(position = NULL, x = c("antibiotic", "interpretation"),  fill = "interpretation", translate_ab = "name", minimum = 30,  language = get_AMR_locale(), combine_SI = TRUE, ...)

Arguments

Details

At default, the names of antimicrobials will be shown on the plots usingab_name(). This can be set with thetranslate_ab argument. Seecount_df().

geom_sir() will take any variable from the data that has ansir class (created withas.sir()) usingsir_df() and will plot bars with the percentage S, I, and R. The default behaviour is to have the bars stacked and to have the different antimicrobials on the x axis.

Additional functions include:

• facet_sir() creates 2d plots (at default based on S/I/R) usingggplot2::facet_wrap().

• scale_y_percent() transforms the y axis to a 0 to 100% range usingggplot2::scale_y_continuous().

• scale_sir_colours() sets colours to the bars (green for S, yellow for I, and red for R). with multilingual support. The default colours are colour-blind friendly, while maintaining the convention that e.g. 'susceptible' should be green and 'resistant' should be red.

• theme_sir() is aggplot2 theme with minimal distraction.

• labels_sir_count() print datalabels on the bars with percentage and amount of isolates usingggplot2::geom_text().

ggplot_sir() is a wrapper around all above functions that uses data as first input. This makes it possible to use this function after a pipe (⁠%>%⁠). SeeExamples.

Examples

if (require("ggplot2") && require("dplyr")) {  # get antimicrobial results for drugs against a UTI:  ggplot(example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)) +    geom_sir()}if (require("ggplot2") && require("dplyr")) {  # prettify the plot using some additional functions:  df <- example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)  ggplot(df) +    geom_sir() +    scale_y_percent() +    scale_sir_colours(aesthetics = "fill") +    labels_sir_count() +    theme_sir()}if (require("ggplot2") && require("dplyr")) {  # or better yet, simplify this using the wrapper function - a single command:  example_isolates %>%    select(AMX, NIT, FOS, TMP, CIP) %>%    ggplot_sir()}if (require("ggplot2") && require("dplyr")) {  # get only proportions and no counts:  example_isolates %>%    select(AMX, NIT, FOS, TMP, CIP) %>%    ggplot_sir(datalabels = FALSE)}if (require("ggplot2") && require("dplyr")) {  # add other ggplot2 arguments as you like:  example_isolates %>%    select(AMX, NIT, FOS, TMP, CIP) %>%    ggplot_sir(      width = 0.5,      colour = "black",      size = 1,      linetype = 2,      alpha = 0.25    )}if (require("ggplot2") && require("dplyr")) {  # you can alter the colours with colour names:  example_isolates %>%    select(AMX) %>%    ggplot_sir(colours = c(SI = "yellow"))}if (require("ggplot2") && require("dplyr")) {  # but you can also use the built-in colour-blind friendly colours for  # your plots, where "S" is green, "I" is yellow and "R" is red:  data.frame(    x = c("Value1", "Value2", "Value3"),    y = c(1, 2, 3),    z = c("Value4", "Value5", "Value6")  ) %>%    ggplot() +    geom_col(aes(x = x, y = y, fill = z)) +    scale_sir_colours(      aesthetics = "fill",      Value4 = "S", Value5 = "I", Value6 = "R"    )}if (require("ggplot2") && require("dplyr")) {  # resistance of ciprofloxacine per age group  example_isolates %>%    mutate(first_isolate = first_isolate()) %>%    filter(      first_isolate == TRUE,      mo == as.mo("Escherichia coli")    ) %>%    # age_groups() is also a function in this AMR package:    group_by(age_group = age_groups(age)) %>%    select(age_group, CIP) %>%    ggplot_sir(x = "age_group")}if (require("ggplot2") && require("dplyr")) {  # a shorter version which also adjusts data label colours:  example_isolates %>%    select(AMX, NIT, FOS, TMP, CIP) %>%    ggplot_sir(colours = FALSE)}if (require("ggplot2") && require("dplyr")) {  # it also supports groups (don't forget to use the group var on `x` or `facet`):  example_isolates %>%    filter(mo_is_gram_negative(), ward != "Outpatient") %>%    # select only UTI-specific drugs    select(ward, AMX, NIT, FOS, TMP, CIP) %>%    group_by(ward) %>%    ggplot_sir(      x = "ward",      facet = "antibiotic",      nrow = 1,      title = "AMR of Anti-UTI Drugs Per Ward",      x.title = "Ward",      datalabels = FALSE    )}

Guess Antibiotic Column

Description

This tries to find a column name in a data set based on information from theantimicrobials data set. Also supports WHONET abbreviations.

Usage

guess_ab_col(x = NULL, search_string = NULL, verbose = FALSE,  only_sir_columns = FALSE)

Arguments

Details

You can look for an antibiotic (trade) name or abbreviation and it will searchx and theantimicrobials data set for any column containing a name or code of that antibiotic.

Value

A column name ofx, orNULL when no result is found.

Examples

df <- data.frame(  amox = "S",  tetr = "R")guess_ab_col(df, "amoxicillin")guess_ab_col(df, "J01AA07") # ATC code of tetracyclineguess_ab_col(df, "J01AA07", verbose = TRUE)# WHONET codesdf <- data.frame(  AMP_ND10 = "R",  AMC_ED20 = "S")guess_ab_col(df, "ampicillin")guess_ab_col(df, "J01CR02")guess_ab_col(df, "augmentin")

Data Set Denoting Bacterial Intrinsic Resistance

Description

Data set containing 'EUCAST Expected Resistant Phenotypes' ofall bug-drug combinations between themicroorganisms andantimicrobials data sets.

Usage

intrinsic_resistant

Format

Atibble with 271 905 observations and 2 variables:

• mo
  Microorganism ID which occurs inmicroorganisms$mo. Names can be retrieved usingmo_name().

• ab
  Antimicrobial ID which occurs inantimicrobials$ab. Names can be retrieved usingab_name().

Details

This data set is currently based on'EUCAST Expected Resistant Phenotypes' v1.2 (2023).

This data set is internally used by:

• not_intrinsic_resistant() (anantimicrobial selector)

• mo_is_intrinsic_resistant()

Download Our Reference Data

All reference data sets in the AMR package - including information on microorganisms, antimicrobials, and clinical breakpoints - are freely available for download in multiple formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, and Stata.

For maximum compatibility, we also provide machine-readable, tab-separated plain text files suitable for use in any software, including laboratory information systems.

Visitour website for direct download links, or explore the actual files inour GitHub repository.

Examples

intrinsic_resistant

Italicise Taxonomic Families, Genera, Species, Subspecies

Description

According to the binomial nomenclature, the lowest four taxonomic levels (family, genus, species, subspecies) should be printed in italics. This function finds taxonomic names within strings and makes them italic.

Usage

italicise_taxonomy(string, type = c("markdown", "ansi", "html"))italicize_taxonomy(string, type = c("markdown", "ansi", "html"))

Arguments

Details

This function finds the taxonomic names and makes them italic based on themicroorganisms data set.

The taxonomic names can be italicised using markdown (the default) by adding* before and after the taxonomic names, or⁠<i>⁠ and⁠</i>⁠ when using html. When using 'ansi', ANSI colours will be added using⁠\033[3m⁠ before and⁠\033[23m⁠ after the taxonomic names. If multiple ANSI colours are not available, no conversion will occur.

This function also supports abbreviation of the genus if it is followed by a species, such as "E. coli" and "K. pneumoniae ozaenae".

Examples

italicise_taxonomy("An overview of Staphylococcus aureus isolates")italicise_taxonomy("An overview of S. aureus isolates")cat(italicise_taxonomy("An overview of S. aureus isolates", type = "ansi"))

Joinmicroorganisms to a Data Set

Description

Join the data setmicroorganisms easily to an existing data set or to acharacter vector.

Usage

inner_join_microorganisms(x, by = NULL, suffix = c("2", ""), ...)left_join_microorganisms(x, by = NULL, suffix = c("2", ""), ...)right_join_microorganisms(x, by = NULL, suffix = c("2", ""), ...)full_join_microorganisms(x, by = NULL, suffix = c("2", ""), ...)semi_join_microorganisms(x, by = NULL, ...)anti_join_microorganisms(x, by = NULL, ...)

Arguments

Details

Note: As opposed to thejoin() functions ofdplyr,character vectors are supported and at default existing columns will get a suffix"2" and the newly joined columns will not get a suffix.

If thedplyr package is installed, their join functions will be used. Otherwise, the much slowermerge() andinteraction() functions from baseR will be used.

Value

adata.frame

Examples

left_join_microorganisms(as.mo("K. pneumoniae"))left_join_microorganisms("B_KLBSL_PNMN")df <- data.frame(  date = seq(    from = as.Date("2018-01-01"),    to = as.Date("2018-01-07"),    by = 1  ),  bacteria = as.mo(c(    "S. aureus", "MRSA", "MSSA", "STAAUR",    "E. coli", "E. coli", "E. coli"  )),  stringsAsFactors = FALSE)colnames(df)df_joined <- left_join_microorganisms(df, "bacteria")colnames(df_joined)if (require("dplyr")) {  example_isolates %>%    left_join_microorganisms() %>%    colnames()}

(Key) Antimicrobials for First Weighted Isolates

Description

These functions can be used to determine first weighted isolates by considering the phenotype for isolate selection (seefirst_isolate()). Using a phenotype-based method to determine first isolates is more reliable than methods that disregard phenotypes.

Usage

key_antimicrobials(x = NULL, col_mo = NULL, universal = c("ampicillin",  "amoxicillin/clavulanic acid", "cefuroxime", "piperacillin/tazobactam",  "ciprofloxacin", "trimethoprim/sulfamethoxazole"),  gram_negative = c("gentamicin", "tobramycin", "colistin", "cefotaxime",  "ceftazidime", "meropenem"), gram_positive = c("vancomycin", "teicoplanin",  "tetracycline", "erythromycin", "oxacillin", "rifampin"),  antifungal = c("anidulafungin", "caspofungin", "fluconazole", "miconazole",  "nystatin", "voriconazole"), only_sir_columns = any(is.sir(x)), ...)all_antimicrobials(x = NULL, only_sir_columns = any(is.sir(x)), ...)antimicrobials_equal(y, z, type = c("points", "keyantimicrobials"),  ignore_I = TRUE, points_threshold = 2, ...)

Arguments

Details

Thekey_antimicrobials() andall_antimicrobials() functions are context-aware. This means that thex argument can be left blank if used inside adata.frame call, seeExamples.

The functionkey_antimicrobials() returns acharacter vector with 12 antimicrobial results for every isolate. The functionall_antimicrobials() returns acharacter vector with all antimicrobial drug results for every isolate. These vectors can then be compared usingantimicrobials_equal(), to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot (".") bykey_antimicrobials() and ignored byantimicrobials_equal().

Please see thefirst_isolate() function how these important functions enable the 'phenotype-based' method for determination of first isolates.

The default antimicrobial drugs used forall rows (set inuniversal) are:

• Ampicillin

• Amoxicillin/clavulanic acid

• Cefuroxime

• Ciprofloxacin

• Piperacillin/tazobactam

• Trimethoprim/sulfamethoxazole

The default antimicrobial drugs used forGram-negative bacteria (set ingram_negative) are:

• Cefotaxime

• Ceftazidime

• Colistin

• Gentamicin

• Meropenem

• Tobramycin

The default antimicrobial drugs used forGram-positive bacteria (set ingram_positive) are:

• Erythromycin

• Oxacillin

• Rifampin

• Teicoplanin

• Tetracycline

• Vancomycin

The default antimicrobial drugs used forfungi (set inantifungal) are:

• Anidulafungin

• Caspofungin

• Fluconazole

• Miconazole

• Nystatin

• Voriconazole

See Also

first_isolate()

Examples

# `example_isolates` is a data set available in the AMR package.# See ?example_isolates.# output of the `key_antimicrobials()` function could be like this:strainA <- "SSSRR.S.R..S"strainB <- "SSSIRSSSRSSS"# those strings can be compared with:antimicrobials_equal(strainA, strainB, type = "keyantimicrobials")# TRUE, because I is ignored (as well as missing values)antimicrobials_equal(strainA, strainB, type = "keyantimicrobials", ignore_I = FALSE)# FALSE, because I is not ignored and so the 4th [character] differsif (require("dplyr")) {  # set key antimicrobials to a new variable  my_patients <- example_isolates %>%    mutate(keyab = key_antimicrobials(antifungal = NULL)) %>% # no need to define `x`    mutate(      # now calculate first isolates      first_regular = first_isolate(col_keyantimicrobials = FALSE),      # and first WEIGHTED isolates      first_weighted = first_isolate(col_keyantimicrobials = "keyab")    )  # Check the difference in this data set, 'weighted' results in more isolates:  sum(my_patients$first_regular, na.rm = TRUE)  sum(my_patients$first_weighted, na.rm = TRUE)}

Kurtosis of the Sample

Description

Kurtosis is a measure of the "tailedness" of the probability distribution of a real-valued random variable. A normal distribution has a kurtosis of 3 and a excess kurtosis of 0.

Usage

kurtosis(x, na.rm = FALSE, excess = FALSE)## Default S3 method:kurtosis(x, na.rm = FALSE, excess = FALSE)## S3 method for class 'matrix'kurtosis(x, na.rm = FALSE, excess = FALSE)## S3 method for class 'data.frame'kurtosis(x, na.rm = FALSE, excess = FALSE)

Arguments

See Also

skewness()

Examples

kurtosis(rnorm(10000))kurtosis(rnorm(10000), excess = TRUE)

Vectorised Pattern Matching with Keyboard Shortcut

Description

Convenient wrapper aroundgrepl() to match a pattern:x %like% pattern. It always returns alogical vector and is always case-insensitive (usex %like_case% pattern for case-sensitive matching). Also,pattern can be as long asx to compare items of each index in both vectors, or they both can have the same length to iterate over all cases.

Usage

like(x, pattern, ignore.case = TRUE)x %like% patternx %unlike% patternx %like_case% patternx %unlike_case% pattern

Arguments

Details

Theselike() and⁠%like%⁠/⁠%unlike%⁠ functions:

• Are case-insensitive (use⁠%like_case%⁠/⁠%unlike_case%⁠ for case-sensitive matching)

• Support multiple patterns

• Check ifpattern is a valid regular expression and setsfixed = TRUE if not, to greatly improve speed (vectorised overpattern)

• Always use compatibility with Perl unlessfixed = TRUE, to greatly improve speed

Using RStudio? The⁠%like%⁠/⁠%unlike%⁠ functions can also be directly inserted in your code from the Addins menu and can have its own keyboard shortcut likeShift+Ctrl+L orShift+Cmd+L (see menuTools >⁠Modify Keyboard Shortcuts...⁠). If you keep pressing your shortcut, the inserted text will be iterated over⁠%like%⁠ ->⁠%unlike%⁠ ->⁠%like_case%⁠ ->⁠%unlike_case%⁠.

Value

Alogical vector

Source

Idea from thelike function from thedata.table package, although altered as explained inDetails.

See Also

grepl()

Examples

# data.table has a more limited version of %like%, so unload it:try(detach("package:data.table", unload = TRUE), silent = TRUE)a <- "This is a test"b <- "TEST"a %like% bb %like% a# also supports multiple patternsa <- c("Test case", "Something different", "Yet another thing")b <- c("case", "diff", "yet")a %like% ba %unlike% ba[1] %like% ba %like% b[1]# get isolates whose name start with 'Entero' (case-insensitive)example_isolates[which(mo_name() %like% "^entero"), ]if (require("dplyr")) {  example_isolates %>%    filter(mo_name() %like% "^ent")}

Determine Multidrug-Resistant Organisms (MDRO)

Description

Determine which isolates are multidrug-resistant organisms (MDRO) according to international, national, or custom guidelines.

Usage

mdro(x = NULL, guideline = "CMI 2012", col_mo = NULL, esbl = NA,  carbapenemase = NA, mecA = NA, mecC = NA, vanA = NA, vanB = NA,  info = interactive(), pct_required_classes = 0.5, combine_SI = TRUE,  verbose = FALSE, only_sir_columns = any(is.sir(x)), ...)brmo(x = NULL, only_sir_columns = any(is.sir(x)), ...)mrgn(x = NULL, only_sir_columns = any(is.sir(x)), verbose = FALSE, ...)mdr_tb(x = NULL, only_sir_columns = any(is.sir(x)), verbose = FALSE, ...)mdr_cmi2012(x = NULL, only_sir_columns = any(is.sir(x)), verbose = FALSE,  ...)eucast_exceptional_phenotypes(x = NULL, only_sir_columns = any(is.sir(x)),  verbose = FALSE, ...)

Arguments

Details

These functions are context-aware. This means that thex argument can be left blank if used inside adata.frame call, seeExamples.

For thepct_required_classes argument, values above 1 will be divided by 100. This is to support both fractions (0.75 or3/4) and percentages (75).

Note: Every test that involves the Enterobacteriaceae family, will internally be performed using its newly namedorder Enterobacterales, since the Enterobacteriaceae family has been taxonomically reclassified by Adeoluet al. in 2016. Before that, Enterobacteriaceae was the only family under the Enterobacteriales (with an i) order. All species under the old Enterobacteriaceae family are still under the new Enterobacterales (without an i) order, but divided into multiple families. The way tests are performed now by thismdro() function makes sure that results from before 2016 and after 2016 are identical.

Supported International / National Guidelines

Please suggest to implement guidelines byletting us know.

Currently supported guidelines are (case-insensitive):

• guideline = "CMI 2012" (default)

  Magiorakos AP, Srinivasan Aet al. "Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance." Clinical Microbiology and Infection (2012) (doi:10.1111/j.1469-0691.2011.03570.x)

• guideline = "EUCAST 3.3" (or simplyguideline = "EUCAST")

  The European international guideline - EUCAST Expert Rules Version 3.3 "Intrinsic Resistance and Unusual Phenotypes" (link)

  Also:

  • guideline = "EUCAST 3.2"

    The former European international guideline - EUCAST Expert Rules Version 3.2 "Intrinsic Resistance and Unusual Phenotypes" (link)

  • guideline = "EUCAST 3.1"

    The former European international guideline - EUCAST Expert Rules Version 3.1 "Intrinsic Resistance and Exceptional Phenotypes Tables" (link)

• guideline = "TB"

  The international guideline for multi-drug resistant tuberculosis - World Health Organization "Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis" (link)

• guideline = "MRGN"

  The German national guideline - Mueller et al. (2015) Antimicrobial Resistance and Infection Control 4:7;doi:10.1186/s13756-015-0047-6

• guideline = "BRMO 2024" (or simplyguideline = "BRMO")

  The Dutch national guideline - Samenwerkingverband Richtlijnen Infectiepreventie (SRI) (2024) "Bijzonder Resistente Micro-Organismen (BRMO)" (link)

  Also:

  • guideline = "BRMO 2017"

    The former Dutch national guideline - Werkgroep Infectiepreventie (WIP), RIVM, last revision as of 2017: "Bijzonder Resistente Micro-Organismen (BRMO)"

Using Custom Guidelines

Using a custom MDRO guideline is of importance if you have custom rules to determine MDROs in your hospital, e.g., rules that are dependent on ward, state of contact isolation or other variables in your data.

Custom guidelines can be set with thecustom_mdro_guideline() function.

Value

• Ifverbose is set toTRUE:
  Adata.frame containing columnsrow_number,microorganism,MDRO,reason,all_nonsusceptible_columns,guideline

• CMI 2012 paper - functionmdr_cmi2012() ormdro():
  Orderedfactor with levelsNegative <Multi-drug-resistant (MDR) <⁠Extensively drug-resistant (XDR)⁠ <Pandrug-resistant (PDR)

• TB guideline - functionmdr_tb() ormdro(..., guideline = "TB"):
  Orderedfactor with levelsNegative <Mono-resistant <Poly-resistant <Multi-drug-resistant <⁠Extensively drug-resistant⁠

• German guideline - functionmrgn() ormdro(..., guideline = "MRGN"):
  Orderedfactor with levelsNegative <⁠3MRGN⁠ <⁠4MRGN⁠

• Everything else, except for custom guidelines:
  Orderedfactor with levelsNegative <⁠Positive, unconfirmed⁠ <Positive. The value"Positive, unconfirmed" means that, according to the guideline, it is not entirely sure if the isolate is multi-drug resistant and this should be confirmed with additional (e.g. genotypic) tests

Interpretation of SIR

In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R (https://www.eucast.org/newsiandr).

This AMR package follows insight; usesusceptibility() (equal toproportion_SI()) to determine antimicrobial susceptibility andcount_susceptible() (equal tocount_SI()) to count susceptible isolates.

See Also

custom_mdro_guideline()

Examples

out <- mdro(example_isolates)str(out)table(out)out <- mdro(example_isolates, guideline = "EUCAST 3.3")table(out)if (require("dplyr")) {  # no need to define `x` when used inside dplyr verbs:  example_isolates %>%    mutate(MDRO = mdro()) %>%    count(MDRO)}

Calculate the Mean AMR Distance

Description

Calculates a normalised mean for antimicrobial resistance between multiple observations, to help to identify similar isolates without comparing antibiograms by hand.

Usage

mean_amr_distance(x, ...)## S3 method for class 'sir'mean_amr_distance(x, ..., combine_SI = TRUE)## S3 method for class 'data.frame'mean_amr_distance(x, ..., combine_SI = TRUE)amr_distance_from_row(amr_distance, row)

Arguments

Details

The mean AMR distance is effectivelythe Z-score; a normalised numeric value to compare AMR test results which can help to identify similar isolates, without comparing antibiograms by hand.

MIC values (seeas.mic()) are transformed withlog2() first; their distance is thus calculated as(log2(x) - mean(log2(x))) / sd(log2(x)).

SIR values (seeas.sir()) are transformed using"S" = 1,"I" = 2, and"R" = 3. Ifcombine_SI isTRUE (default), the"I" will be considered to be 1.

For data sets, the mean AMR distance will be calculated per column, after which the mean per row will be returned, seeExamples.

Useamr_distance_from_row() to subtract distances from the distance of one row, seeExamples.

Interpretation

Isolates with distances less than 0.01 difference from each other should be considered similar. Differences lower than 0.025 should be considered suspicious.

Examples

sir <- random_sir(10)sirmean_amr_distance(sir)mic <- random_mic(10)micmean_amr_distance(mic)# equal to the Z-score of their log2:(log2(mic) - mean(log2(mic))) / sd(log2(mic))disk <- random_disk(10)diskmean_amr_distance(disk)y <- data.frame(  id = LETTERS[1:10],  amox = random_sir(10, ab = "amox", mo = "Escherichia coli"),  cipr = random_disk(10, ab = "cipr", mo = "Escherichia coli"),  gent = random_mic(10, ab = "gent", mo = "Escherichia coli"),  tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli"))ymean_amr_distance(y)y$amr_distance <- mean_amr_distance(y, is.mic(y))y[order(y$amr_distance), ]if (require("dplyr")) {  y %>%    mutate(      amr_distance = mean_amr_distance(y),      check_id_C = amr_distance_from_row(amr_distance, id == "C")    ) %>%    arrange(check_id_C)}if (require("dplyr")) {  # support for groups  example_isolates %>%    filter(mo_genus() == "Enterococcus" & mo_species() != "") %>%    select(mo, TCY, carbapenems()) %>%    group_by(mo) %>%    mutate(dist = mean_amr_distance(.)) %>%    arrange(mo, dist)}

Data Set with 78 679 Taxonomic Records of Microorganisms

Description

A data set containing the full microbial taxonomy (last updated: June 24th, 2024) of six kingdoms. This data set is the backbone of thisAMR package. MO codes can be looked up usingas.mo() and microorganism properties can be looked up using any of themo_* functions.

This data set is carefully crafted, yet made 100% reproducible from public and authoritative taxonomic sources (usingthis script), namely:List of Prokaryotic names with Standing in Nomenclature (LPSN) for bacteria,MycoBank for fungi, andGlobal Biodiversity Information Facility (GBIF) for all others taxons.

Usage

microorganisms

Format

Atibble with 78 679 observations and 26 variables:

• mo
  ID of microorganism as used by this package.This is a unique identifier.

• fullname
  Full name, like"Escherichia coli". For the taxonomic ranks genus, species and subspecies, this is the 'pasted' text of genus, species, and subspecies. For all taxonomic ranks higher than genus, this is the name of the taxon.This is a unique identifier.

• status
  Status of the taxon, either "accepted", "not validly published", "synonym", or "unknown"

• kingdom,phylum,class,order,family,genus,species,subspecies
  Taxonomic rank of the microorganism. Note that for fungi,phylum is equal to their taxonomicdivision. Also, for fungi,subkingdom andsubdivision were left out since they do not occur in the bacterial taxonomy.

• rank
  Text of the taxonomic rank of the microorganism, such as"species" or"genus"

• ref
  Author(s) and year of related scientific publication. This contains only thefirst surname and year of thelatest authors, e.g. "Walliset al. 2006emend. Smith and Jones 2018" becomes "Smithet al., 2018". This field is directly retrieved from the source specified in the columnsource. Moreover, accents were removed to comply with CRAN that only allows ASCII characters.

• oxygen_tolerance
  Oxygen tolerance, either "aerobe", "anaerobe", "anaerobe/microaerophile", "facultative anaerobe", "likely facultative anaerobe", or "microaerophile". These data were retrieved from BacDive (seeSource). Items that contain "likely" are missing from BacDive and were extrapolated from other species within the same genus to guess the oxygen tolerance. Currently 68.3% of all ~39 000 bacteria in the data set contain an oxygen tolerance.

• source
  Either "GBIF", "LPSN", "Manually added", "MycoBank", or "manually added" (seeSource)

• lpsn
  Identifier ('Record number') of List of Prokaryotic names with Standing in Nomenclature (LPSN). This will be the first/highest LPSN identifier to keep one identifier per row. For example,Acetobacter ascendens has LPSN Record number 7864 and 11011. Only the first is available in themicroorganisms data set.This is a unique identifier, though available for only ~33 000 records.

• lpsn_parent
  LPSN identifier of the parent taxon

• lpsn_renamed_to
  LPSN identifier of the currently valid taxon

• mycobank
  Identifier ('MycoBank #') of MycoBank.This is a unique identifier, though available for only ~19 000 records.

• mycobank_parent
  MycoBank identifier of the parent taxon

• mycobank_renamed_to
  MycoBank identifier of the currently valid taxon

• gbif
  Identifier ('taxonID') of Global Biodiversity Information Facility (GBIF).This is a unique identifier, though available for only ~49 000 records.

• gbif_parent
  GBIF identifier of the parent taxon

• gbif_renamed_to
  GBIF identifier of the currently valid taxon

• prevalence
  Prevalence of the microorganism based on Bartlettet al. (2022,doi:10.1099/mic.0.001269), seemo_matching_score() for the full explanation

• snomed
  Systematized Nomenclature of Medicine (SNOMED) code of the microorganism, version of July 16th, 2024 (seeSource). Usemo_snomed() to retrieve it quickly, seemo_property().