Multiparameter Intelligent Monitoring in Intensive Care II: A public-access intensive care unit database*

Saeed, Mohammed MD, PhD; Villarroel, Mauricio MBA; Reisner, Andrew T. MD; Clifford, Gari PhD; Lehman, Li-Wei PhD; Moody, George; Heldt, Thomas PhD; Kyaw, Tin H. MEng; Moody, Benjamin; Mark, Roger G. MD, PhD

Author Information

From the University of Michigan Hospitals (MS), Ann Arbor, MI, the Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, and Philips Healthcare, Andover, MA; the Division of Health Sciences and Technology, Massachusetts Institute of Technology (MV), Cambridge, MA; the Division of Health Sciences and Technology, Massachusetts Institute of Technology (ATR), and the Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA; the Division of Health Sciences and Technology (GC), Massachusetts Institute of Technology, Cambridge, MA, and the Department of Engineering Sciences, Institute of Biomedical Engineering, University of Oxford, Oxford, UK; the Division of Health Sciences and Technology (L-WL, GM, TH, BM), Massachusetts Institute of Technology, Cambridge, MA; AdMob Inc (THK), San Mateo, CA; and the Division of Health Sciences and Technology (RGM), Massachusetts Institute of Technology, Cambridge, MA, and Beth Israel Deaconess Medical Center, Boston, MA.

This research was supported by grant R01 EB001659 from the National Institute of Biomedical Imaging and Bioengineering and by support from Philips Healthcare.

This research was performed at the Massachusetts Institute of Technology, Cambridge, MA, and the Beth Israel Deaconess Medical Center, Boston, MA.

Dr. Saeed is employed by Philips Healthcare. Dr. Villarroel, Dr. Lehman, Mr. Moody, Dr. Heldt, and Dr. Mark received funding from the National Institutes of Health (NIH). Dr. Reisner consulted with General Electric Healthcare and received funding from the NIH. The remaining authors have not disclosed any potential conflicts of interest.

For information regarding this article, E-mail:[email protected]

Critical Care Medicine39(5):p 952-960, May 2011. |DOI:10.1097/CCM.0b013e31820a92c6

Abstract

Objective:

We sought to develop an intensive care unit research database applying automated techniques to aggregate high-resolution diagnostic and therapeutic data from a large, diverse population of adult intensive care unit patients. This freely available database is intended to support epidemiologic research in critical care medicine and serve as a resource to evaluate new clinical decision support and monitoring algorithms.

Design:

Data collection and retrospective analysis.

Setting:

All adult intensive care units (medical intensive care unit, surgical intensive care unit, cardiac care unit, cardiac surgery recovery unit) at a tertiary care hospital.

Patients:

Adult patients admitted to intensive care units between 2001 and 2007.

Interventions:

None.

Measurements and Main Results:

The Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database consists of 25,328 intensive care unit stays. The investigators collected detailed information about intensive care unit patient stays, including laboratory data, therapeutic intervention profiles such as vasoactive medication drip rates and ventilator settings, nursing progress notes, discharge summaries, radiology reports, provider order entry data, International Classification of Diseases, 9th Revision codes, and, for a subset of patients, high-resolution vital sign trends and waveforms. Data were automatically deidentified to comply with Health Insurance Portability and Accountability Act standards and integrated with relational database software to create electronic intensive care unit records for each patient stay. The data were made freely available in February 2010 through the Internet along with a detailed user's guide and an assortment of data processing tools. The overall hospital mortality rate was 11.7%, which varied by critical care unit. The median intensive care unit length of stay was 2.2 days (interquartile range, 1.1–4.4 days). According to the primary International Classification of Diseases, 9th Revision codes, the following disease categories each comprised at least 5% of the case records: diseases of the circulatory system (39.1%); trauma (10.2%); diseases of the digestive system (9.7%); pulmonary diseases (9.0%); infectious diseases (7.0%); and neoplasms (6.8%).

Conclusions:

MIMIC-II documents a diverse and very large population of intensive care unit patient stays and contains comprehensive and detailed clinical data, including physiological waveforms and minute-by-minute trends for a subset of records. It establishes a new public-access resource for critical care research, supporting a diverse range of analytic studies spanning epidemiology, clinical decision-rule development, and electronic tool development.