US20120179136A1 - System and method for closed-loop patient-adaptive hemodynamic management - Google Patents

Abstract

A system and method for patient-adaptive hemodynamic management is described. One embodiment includes a system for hemodynamic management including transfusion, volume resuscitation with intravenous fluids, and medications, utilizing monitored hemodynamic parameters including the described dynamic predictors of fluid responsiveness, and including an intelligent algorithm capable of adaptation of the function of the device to specific patients.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS
• The present application claims the benefit of co-pending U.S. Provisional Application Ser. No. 61/432,081, filed on Jan. 12, 2011, entitled “INTELLIGENT CLOSED-LOOP, PATIENT-ADAPTIVE HEMODYNAMIC MANAGEMENT SYSTEM,” which is incorporated herein by reference. This application is related to co-pending U.S. patent application Ser. No. ______, filed on the same date as the present application, entitled “SYSTEM AND METHOD FOR CLOSED-LOOP PATIENT-ADAPTIVE HEMODYNAMIC MANAGEMENT” (temporarily referenced by Attorney Docket No. 1279-564), to co-pending U.S. patent application Ser. No. ______, filed on the same date as the present application, entitled “SYSTEM AND METHOD FOR CLOSED-LOOP PATIENT-ADAPTIVE HEMODYNAMIC MANAGEMENT” (temporarily referenced by Attorney Docket No. 1279-565), and to co-pending U.S. patent application Ser. No. ______, filed on the same date as the present application, entitled “SYSTEM AND METHOD FOR CLOSED-LOOP PATIENT-ADAPTIVE HEMODYNAMIC MANAGEMENT” (temporarily referenced by Attorney Docket No. 1279-566). The contents of each of these applications are hereby incorporated by reference herein in their entirety for all purposes.
FIELD
• The disclosure relates to an apparatus and method for hemodynamic management capable of facilitating fluid administration, transfusion of blood products, and administration of blood pressure supporting medications.
BACKGROUND
• In the past, fluid resuscitation has been approached directly by clinicians using vital signs, or clinical guidelines regarding urine output, for example. Previous automated systems have been tried using either urine output or other physiologic parameters like blood pressure or heart rate, all of which have been unable to accurately predict fluid responsiveness. In some cases trial-and-error was the best option available.
• Although present devices are functional, they are not sufficiently accurate or otherwise satisfactory.
SUMMARY
• In one aspect the disclosure describes a method that incorporates the dynamic predictors of fluid responsiveness (“fluid predictors” i.e. pulse-pressure variation or PPV, stroke volume variation or SVV, parameters derived from the plethysmograph waveform, etc) which have been shown to reliably predict a response to fluid bolus in specific conditions. This allows directed fluid management with the goal of optimizing cardiac output.
• In another aspect, a method is provided that incorporates the dynamic predictors of fluid (“fluid predictors”) responsiveness in conjunction with a patient-adaptive monitoring system that adjusts output based on previous responses represents a substantial improvement over previously proposed automated systems.
• Using a combination of the change in the fluid predictive parameters and the change in cardiac output in response to a bolus allows a very specific measurement of the bias present in a particular patient at a particular time and allows for patient-adaptive responses to be effected. Additionally, using a variety of available vital signs and cardiac output information allows for appropriate administration of blood-pressure supporting pharmacologic agents.
• In another aspect, a method and system is provided comprising a set of processes and a device based on those used to administer IV fluids, blood and medications to patients autonomously.
• In another aspect, a computer-implemented method for facilitating the administration of fluid to a patient is provided. The method includes steps of receiving input information relating to one or more physiologic processes of the patient, determining, using a processor, a predicted change in a physiologic parameter of the patient in response to administration of a fluid bolus to the patient wherein the predicted change is based at least in part on the input information, and generating, using the processor, a fluid administration signal in response to the predicted change.
• In a further aspect, a computer-implemented method for facilitating control of fluid administration is provided. This computer implemented method includes receiving information relating to an initial change in a physiologic parameter of a patient in response to administration of a first fluid bolus to a patient, determining, using a processor and based at least in part upon the information, a predicted change in the physiologic parameter in response to an administration of a second bolus, and adjusting administration of fluid to the patient based upon the predicted change.
• In yet another aspect, a computer-implemented method for facilitating the administration of fluid to a patient is provided. The computer implemented method of this aspect includes receiving, from a monitoring device, an indication of at least one of oxygen delivery to tissues of the patient and oxygen utilization by the patient. The method further includes selecting, using a processor and based upon the indication, an oxygen-carrying fluid from among multiple fluids capable of being infused into the patient, and signaling, using the processor, selection of the oxygen-carrying fluid for infusion into the patient.
• In still another aspect, a computer-implemented method for facilitating the administration of fluid to a patient is provided. The method of this aspect includes receiving input information relating to one or more physiologic processes of the patient, and determining, using a processor and based on the input information, that a change has occurred in a physiologic parameter of the patient. The method further includes generating, using the processor, a fluid administration signal based at least in part on the change in the physiologic parameter.
• In another aspect, a computer-implemented method for facilitating the administration of fluid to a patient is provided. In this aspect, the method includes determining, using a processor, a predicted change in a physiologic parameter of the patient in response to administration of a fluid bolus to the patient, determining, using a processor, that an actual change has occurred in the physiologic parameter of the patient and generating, using the processor, a fluid administration signal based upon at least one of the predicted change and the actual change.
• In yet another aspect, a computer-implemented method for providing clinical decision support relating to the administration of fluid to a patient is provided. The computer-implemented method of this aspect includes receiving input information relating to one or more physiologic processes of the patient, and determining, using a processor, a predicted change in a physiologic parameter of the patient in response to administration of a fluid bolus to the patient wherein the predicted change is based at least in part on the input information. The method further includes providing, using the processor and in response to the predicted change, a fluid administration recommendation.
• In still another aspect, a computer-implemented method for providing clinical decision support relating to the administration of fluid to a patient is provided. The method of this aspect includes receiving information relating to an initial change in a physiologic parameter of a patient in response to administration of a first fluid bolus to a patient, and determining, using a processor and based at least in part upon the information, a predicted change in the physiologic parameter in response to an administration of a second bolus. The method further includes providing, using the processor and in response to the predicted change, a fluid administration recommendation.
• In a further aspect, a system is provided that includes: 1) a means of calculating the expected increase in cardiac output in the general population in response to a fluid bolus given a specific set of physiologic parameters. This calculation is based on previously published and unpublished data; 2) a means of calculating the expected increase in cardiac output in a specific patient given a specific set of physiologic parameters and data collected from previous fluid administrations; 3) a means of calculating bias, artifact, and error in the response to fluid, in part based on the difference between the change in actual cardiac output and the change in the dynamic predictor and the predictable relationship between the two; 4) calculations for determining whether or not blood pressure supporting medications are indicated, and if so how much; and 5) calculations for determining whether or not blood product administration is indicated, and if so how much.
• In yet a further aspect, a clinical device is provided that is capable of administering fluids, blood products, and medications based on the algorithms above, monitoring the patient response, and displaying the monitored information in a specific and novel way to the practitioner. One purpose of this device is to automate and standardize administration of intravenous fluids, blood products, and blood-pressure supporting medications to assist clinicians with the eventual goal of improving outcomes.
• In an additional aspect, an apparatus for hemodynamic and cardiac output management in a patient is provided, comprising a computer readable storage medium storing instructions to perform a method of managing hemodynamic and cardiac output in a patient, the method comprising the steps of determining, given a set of available physiologic data obtained from patient monitoring devices, which parameters to use and in what combinations to predict fluid responsiveness; and determining the expected increase in cardiac output in the general population in response to a fluid bolus of specific size given the chosen set of physiologic parameters; and determining patient-specific bias in the response to said fluid based on the patient's prior responses to fluid administration.
• The apparatus may further comprise detecting and filtering artifact in the monitored data; and detecting and filtering error in the patient bias using the strong relationship between the predictive parameters and their response to fluid administration, especially to detect ongoing bleeding or fluid shifts which might influence bias.
• The apparatus may also comprise dynamically adapting to specific patients using the known biases in conjunction with associated physiologic parameters, their means and standard deviations in relationship to one another, and observed responses to previous interventions by the apparatus; determining whether blood-pressure supporting medications are indicated and if so administer them, again monitoring responses and adapting to the patient, and determining whether blood product administration is indicated and if so administer them.
• The apparatus may be further configured such that adaptation and learning is further enhanced by data shared between devices over time to improve population expectations and the processes of the apparatus.
• The apparatus may be further configured such that the adapting process is concerned not only with adjustments to the fluid administration volume and threshold and to the medication administration dose and threshold, but also with automatic adjustments to the weight of each measured parameter in decision-making by the apparatus.
• The apparatus may further comprise pumps capable of injecting fluid or medication into said patient.
• The apparatus may be further configured to operate, in real time and without operator interaction, to automatically adjust the anticipated response to a new bolus based on the bias information in a state-dependent fashion.
• In yet another aspect, a method for hemodynamic and cardiac output management in a patient is provided comprising: obtaining physiologic data from a patient, injecting fluid, medications, or blood products into a patient as indicated, measuring the results of said interventions in said patient, adapting future interventions based on data collected from previous interventions said fluid to said patient.
• Those skilled in the art can readily recognize that numerous variations and substitutions may be made in the invention, its use and its configuration to achieve substantially the same results as achieved by the embodiments described herein. Accordingly, there is no intention to limit the invention to the disclosed exemplary forms. Many variations, modifications and alternative constructions fall within the scope and spirit of the disclosed invention as expressed in the claims.
• As previously stated, the above-described embodiments and implementations are for illustration purposes only. Numerous other embodiments, implementations, and details of the invention are easily recognized by those of skill in the art from the following descriptions and claims.
BRIEF DESCRIPTION OF THE DRAWINGS
• Various objects and advantages and a more complete understanding of the present invention are apparent and more readily appreciated by reference to the following Detailed Description and to the appended claims when taken in conjunction with the accompanying Drawings wherein:
• FIG. 1A illustrates details of an exemplary patient-adaptive hemodynamic management system in accordance with the disclosure;
• FIG. 1B illustrates details of an embodiment of a control device that can be used, for example, in the system ofFIG. 1A;
• FIG. 2 illustrates details of an embodiment of a vitals manager and log component that can be used, for example, in the control device ofFIG. 1B;
• FIG. 3 illustrates details of an embodiment of a population based predictor component that can be used, for example, in the control device ofFIG. 1B;
• FIG. 4 illustrates details of an embodiment of a fluid bolus log component that can be used, for example, in the control device ofFIG. 1B;
• FIG. 5 illustrates details of an embodiment of a log predictor component and an embodiment of a history analysis component that can be used, for example, in the control device ofFIG. 1B;
• FIG. 6 illustrates details of an embodiment of a prediction engine that can be used, for example, in the control device ofFIG. 1B;
• FIG. 7 illustrates details of an embodiment of an intervention decision component that can be used, for example, in the control device ofFIG. 1B;
• FIG. 8 illustrates details of an embodiment of a pump manager component that can be used, for example, in the control device ofFIG. 1B;
• FIG. 9 illustrates details of another exemplary patient-adaptive hemodynamic management system in accordance with the disclosure;
• FIG. 10 illustrates information flow between various components of the patient-adaptive hemodynamic management system ofFIG. 1A;
• FIG. 11 is a flowchart depicting an exemplary method of providing patient-adaptive hemodynamic management in accordance with the disclosure;
• FIG. 12 is a flowchart depicting an exemplary method of providing patient-adaptive hemodynamic management including a user intervention in accordance with the disclosure;
• FIG. 13 is a flowchart depicting another exemplary method of providing patient-adaptive hemodynamic management in accordance with the disclosure;
• FIG. 14 is a flowchart depicting another exemplary method of providing patient-adaptive hemodynamic management in accordance with the disclosure;
• FIG. 15 is a flowchart depicting another exemplary method of providing patient-adaptive hemodynamic management in accordance with the disclosure;
• FIG. 16 is a flowchart depicting another exemplary method of providing patient-adaptive hemodynamic management in accordance with the disclosure;
• FIG. 17 is a flowchart depicting another exemplary method of providing patient-adaptive hemodynamic management in accordance with the disclosure;
• FIG. 18 is a flowchart depicting another exemplary method of providing patient-adaptive hemodynamic management in accordance with the disclosure;
• FIG. 19 is a flowchart depicting another exemplary method of providing patient-adaptive hemodynamic management in accordance with the disclosure;
• FIG. 20 is a flowchart depicting another exemplary method of providing patient-adaptive hemodynamic management in accordance with the disclosure;
• FIG. 21 is a flowchart depicting another exemplary method of providing patient-adaptive hemodynamic management in accordance with the disclosure;
• FIG. 22 is a flowchart depicting another exemplary method of providing patient-adaptive hemodynamic management in accordance with the disclosure;
• FIG. 23 is a flowchart depicting yet another exemplary method of providing patient-adaptive hemodynamic management in accordance with the disclosure;
• FIG. 24 is a flowchart depicting another exemplary method of providing patient-adaptive hemodynamic management including a clinical intervention in accordance with the disclosure;
• FIG. 25 is a flowchart depicting another exemplary method of providing patient-adaptive hemodynamic management including a clinical intervention in accordance with the disclosure;
• FIG. 26 is a flowchart depicting yet another exemplary method of providing patient-adaptive hemodynamic management including a clinical intervention in accordance with the disclosure; and
• FIG. 27 is a summary of results from initial studies using the methodology of the control device in simulations.
• In the appended figures, similar components and/or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label by a dash and a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.
DETAILED DESCRIPTION
• An intelligent, closed-loop, patient-adaptive hemodynamic management system and method are provided to monitor hemodynamic parameters, including those predictive of fluid responsiveness, the system being capable of fluid administration, transfusion of blood products, and administration of blood pressure supporting medications based on those parameters in conjunction with patient-adaptive algorithms as well as pooled patient data, and displaying the monitored parameters in a way easily interpreted by practitioners.
• The system and method provided are based on hemodynamic monitoring including dynamic parameters of fluid responsiveness ('fluid predictors') derived from arterial pressure waveform, plethysmograph wave form, thoracic ultrasound, bioimpedance, bioreactance or EKG waveform, for example.
• This control device and process are designed to utilize, among other physiologic data, the dynamic predictors of fluid responsiveness ('fluid predictors'). As there are several described parameters that meet these criteria such as, for example, pulse-pressure variation (PPV), stroke volume variation (SVV), plethysmograph variability, and EKG waveform characteristics, the description will simply refer to the group as the “Fluid Predictors” or FP. This term should be taken to mean any of the described predictors of fluid responsiveness.
• Additionally, all of the physical values and constants in the disclosure are subject to change based on results of ongoing studies as the device and algorithm are refined; values contained herein should be taken to be exemplary at the time of this writing.
• Other terms and abbreviations used herein include:
• • CO—Cardiac output
  • Patient or Subject—the “patient” or “subject” is the organism being monitored by and managed by the system. In one embodiment, the patient is a human being. In another embodiment, the patient may be any mammal, reptile, amphibian, or bird of sufficient size to make intravascular resuscitation an appropriate strategy for management of cardiac output and oxygen delivery.
  • Vital Signs or Vitals—Any statistical measure of a physiologic process taking place in a patient—including waveforms derived from physiologic processes. Vitals can include, for example:
    • Heart Rate (HR)—the number of ventricular contractions per minute
    • Stroke Volume (SV)—the volume of blood ejected by the left ventricle during contraction in milliliters.
    • Systolic Blood Pressure (SBP)—the highest blood pressure felt in the systemic arterial vascular tree during a cardiac cycle.
    • Diastolic Blood Pressure (DBP)—the lowest blood pressure felt in the systemic arterial vascular tree during a cardiac cycle.
    • Mean Arterial Pressure (MAP)—the average blood pressure in the systemic arterial system over one or more cardiac cycles, typically calculated as ((SBP+DBP+DBP)/3).
    • Systemic Vascular Resistance (SVR)—An index of arteriolar constriction throughout the body measured in dyn·s/cm-5
    • Cardiac Output—the total volume of blood ejected by the left ventricle over one minute
    • Dynamic Predictor (DP)—one or more measures of preload dependence derived from the arterial pressure waveform, plethysmograph waveform, EKG waveform, thoracic ultrasound, bioimpedance, bioreactance, and including specific maneuvers such as passive leg raising and tele-expiratory pause. As there are several described parameters that meet this criteria (pulse-pressure variation, stroke volume variation, plethysmograph variability, EKG waveform variation in lead II, and more) the phrase Dynamic Predictor will be understood to represent any one or more of the measures in this group. The term fluid predictor may be used interchangeably with the term Dynamic Predictor in the present disclosure.
  • Intravenous fluid (IV Fluid)—any fluid intended for administration intravenously to a monitored subject for the purpose of intravascular volume expansion or increasing oxygen delivery. IV Fluid would therefore include, but not be limited to: crystalloid solutions like Lactated Ringer's Solution, Normal Saline, Dextrose Solutions, Plasmalyte, and in general balanced salt solutions and sugar solutions; colloidal solutions like albumins, starches, and similar; and blood products and blood analogs like whole blood, platelets, fresh frozen plasma, cryoprecipitate, packed red blood cells, salvaged cellular solutions, or any substitutes meant to mimic or replace these products.
  • Fluid bolus—an administration of a specific volume of IV Fluid over a discrete timespan.
  • Supervisor (system supervisor, user)—a human user who monitors system operation, and who may, in one embodiment, accept or reject system recommendations before the system acts on those recommendations, and who may, in another embodiment, at any time, override system operation in favor of a user directed action.
  • “Efficacy” of a Fluid Bolus—the degree to which the intravascular administration of said fluid increases the cardiac output; or the degree to which the intravascular administration of said fluid improves the delivery of oxygen to the tissues, for example.
  • Prediction—the calculated percent increase in cardiac output that a fluid bolus would be expected to cause in the patient.
  • Vasoactive Medications—medications controlled by the system that could include those intended to manipulate blood pressure and cardiac output such as, for example, ephedrine, phenylephrine, norepinephrine, epinephrine (adrenaline), dopamine dobutamine, milrinone, dopexamine, nitroglycerine, nitroprusside, and other vasopressors, inotropes, and vasodilators.
• With reference toFIG. 1A, a patient-adaptivehemodynamic management system10 includes acontrol device100 coupled via electronic interfaces (not shown) to a first fluid pump109-1 and a second fluid pump109-2. Thecontrol device100 includes adisplay120. Thedisplay120 includes a display screen, electronics and interfaces coupled to the first and second fluid pumps109-1 and109-2. The first fluid pump109-1 is coupled to fluid sources such as, for example,IV bags125 containing any of various IV fluids. The second fluid pump109-2 is coupled to medications such as, for example,medication vials130 containing fluid medications. TheIV bags125 andmedication vials130 are fluidically coupled toIV tubing135 which is coupled to apatient110 such that the IV fluids and medication can be administered to the patient and dynamically controlled by thecontrol device100.
• Thecontrol device100 includes one or more processing units (not shown), and one or more computer readable storage medium (not shown). The processing units may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described above, and/or a combination thereof. The computer readable storage medium may include one or more memories for storing data, including read only memory (ROM), random access memory (RAM), magnetic RAM, core memory, magnetic disk storage mediums, optical storage mediums, flash memory devices and/or other machine readable mediums for storing information. The computer readable storage medium may be embodied in one or more portable or fixed storage devices, optical storage devices, wireless channels, and/or various other storage mediums capable of storing that contain or carry instruction(s) and/or data.
• Thecontrol device100 is coupled to the fluid pumps109 via interface ports. The interface ports can include one or more of a standard USB port and a standard serial port. The USB connector can also be used to import patient data in real-time from another source such as a patient monitor (not shown). Optionally, an external component may be connected to thecontrol device100 which will allow for direct monitoring of patient vital signs by thecontrol device100. Finally, the USB/Serial ports will allow data to be transferred to and from thecontrol device100 for sharing data with other networked equipment (not shown), and for receiving firmware upgrades. Additional ports may be added (for interface with electronic records systems, for example).
• In some embodiments, thedisplay120 provides a touch-screen interface for monitoring vital signs of the patient and for entering patient data and user preferences into thecontrol device100.
• The first fluid pump109-1 can be integrated in the same housing as thecontrol device100 or can be an external pump. In either case, the first fluid pump109-1 can regulate and drive the flow of fluid from theIV bags125 to the patient, as well as select which fluid to use from whichIV bag125. The IV fluids can include one or more of crystalloids, colloids, or blood products as well as other fluids. The second fluid pumps109-2 can be integrated with or external to thecontrol device100 and can be syringe pump systems for use withmultiple medication vials130. One or more pumps may be included but are not required for standard use. The fluid pumps109 can also contain an air detector to hold the infusion in the event air is detected in the tubing. Thecontrol device100 can be coupled to commercially available pumps to control those.
• TheIV tubing135 is typically disposable and is coupled with the control device after flushing and prior to use, as for any standard IV fluid pump. TheIV tubing135 is depicted as a single tube at thepatient110 and multiple tubes at the IV bags25 and themedication vials130. However,multiple IV tubes135 can be connected to thepatient110. Thedisposable IV tubing135 is sterile IV tubing where a new disposable tube set is used for each patient to maintain sterility. One end ofIV tubing135 can have standard IV bag taps for use with standard IV solutions, colloids, and blood products. The opposite end of theIV tubing135 can be a male luer lock for connection to standard IV tubing sets and claves. The disposable IV tubing can also have side ports distal to the main fluid pump which the medication syringes from themedication vials130 can be attached to.
• FIG. 1B illustrates thesystem10 including details of an embodiment of thecontrol device100. Each of the components of thecontrol device100 shown inFIG. 1B are described in detail in the figures and sections that follow.
• Referring toFIG. 1B, thesurgical patient110 is monitored by one or moreclinical monitors115, which are coupled with thecontrol device100. Theclinical monitors115 can be integrated with or separate from thecontrol device100. The vitals measured by theclinical monitors115 are communicated in some fashion to a vitals manager andlog component101, which, in some embodiments, filters the incoming data for noise and validity and then maintains an ongoing record of the validated data in a vitals log.
• Data from thevitals manager component101 is passed on request to the population basedpredictor component102. The population basedpredictor component102 is responsible for making predictions about the likely efficacy of a certain fluid bolus by comparing the received vitals of thepatient110 to mean responses obtained from a previous population of patients with similar vitals in response to the certain fluid bolus.
• Data from thevitals manager component101 is also passed, on request in some embodiments, to a fluidbolus log component103. The data is typically passed or requested when a fluid bolus is initiated or terminated, so that the vitals and calculations based on them may be included in the fluid bolus log with the appropriate bolus. The fluidbolus log component103 also receives inputs from a pump manager component108 (e.g., when fluid boluses are initiated or terminated) including the relevant details of the bolus being administered.
• Information from the fluidbolus log component103 is output to alog predictor component104. Thelog predictor component104 is configured to analyze the known history of thecurrent patient110, and, taking into account the vitals and sub-analyses based on the vitals, predict the current efficacy of a fluid bolus.
• Thelog predictor component104 is also configured, after determining the appropriate segments of the Log that are applicable to thepatient110 in the current state and finishing its predictive analysis, to pass the same segments it used for analysis on to ahistory analysis component105. Thehistory analysis component105 then further characterizes these segments to examine the mean historical error of both the population basedpredictor component102 and thelog predictor component104 by determining, for example, the standard deviation of these errors in order to make corrections to current predictions.
• Aprediction engine106 takes the outputs from the population basedpredictor component102, thelog predictor component104, and thehistory analysis component105, and uses these outputs to formulate a combined prediction in cardiac output for the current state of thepatient110. This combined prediction is passed on to anintervention decision component107 which takes the predicted change in cardiac output and determines the appropriate course of action for thecontrol device100. This action may be modified by user specifications in this component.
• Finally, the action dictated by theintervention decision component107 is communicated to thepump manager component108, which is responsible for communication of the action with a supervisor for verification (if necessary) and the actual hardware level control and monitoring of the fluid pump(s)109.
• The fluid pump(s)109, in one embodiment, are externally controlled fluid pumps which contain their own command interface, alarm system, and configuration. The control of the fluid pumps109 can be achieved over serial, network, wireless, Bluetooth, or other electronic protocols. The specific design of the fluid pumps109 is not essential beyond the characteristic that they are able to variably control the rate of administration of IV Fluid and/ormedication113 into thepatient110. There may be one, two, or more than two physical fluid pumps109, depending on the embodiment.
• In another embodiment, the fluid pump(s)109 are an integrated component of thecontrol device100. In this embodiment, the fluid pumps109 may or may not include alarm and control configurations. If the fluid pumps109 do not include alarms and controls, the alarms and controls for the pumps can be included in thecontrol device100. A user interface (not shown) of thecontrol device100 can be used to affect some aspects of the fluid pumps109. There may be one, two, or morephysical pumps109 in this embodiment.
• In yet another embodiment, the fluid pump(s)109 are the embodiment of acontrol device100 with the entirety of the system built into the hardware and electronic control scheme of thefluid pump109. This pump may include one, two, or more individual fluid set channels for control.
• As expected, the fluid pump(s)109, deliver IV fluid and/ormedications113 to thepatient110 at the rate and times dictated by the method of the claim, in some cases as approved by the supervisor.
• An SV/CO monitor111 is another component that keeps track of the stroke volume and cardiac output over time. The SV/CO monitor111 can independently provide information about the current stroke volume and cardiac output compared to the average and maximum SV/CO and this information can be used by theintervention decision component107 either alone or in conjunction with the other components to determine whether or not to provide fluid to the patient.
• FIG. 2 illustrates details of an embodiment of avitals manager component101 that can be used, for example, in themanagement system10 ofFIGS. 1A and 1B. Thevitals manager component101 is responsible for the acceptance and processing of new vital signs received from clinical monitors201. In one embodiment,clinical monitors115 are included within thecontrol device100 and are connected to thepatient110 and simply pass the collected data along to thevitals manager component101 internally. In another embodiment, external or third-partyclinical monitors115 are connected to the patient and the data from theclinical monitors115 is sent to thecontrol device100 through data interfaces using communications protocols, including but not limited to direct serial connections/RS232, TCP/IP or other network protocols including wireless and Bluetooth, USB, and direct analog signals. This data, in a one embodiment, could be expected to arrive about every second, but may be as often as every 1/1000th of a second or less in the case of digitized waveforms or even continuously in analog signals, and may be as infrequent as once a minute in low-performance embodiments.
• Regardless of the original source of the information, thevital signs data112 is received by thevitals manager component101 and first pass through an artifact andnoise filter component206, referred to from herein as theartifact filtration component206. Theartifact filtration component206 compares the newvital signs data112 both to itself (for internal consistency) and to previously received data (for consistency with regards to trends and time), and to general rules about limits on specific parameters. If the newvital signs data112 is found to satisfy the requirements of the noise filters (e.g., within threshold limits and/or within threshold changes over a threshold time) it is deemed valid and passed on to the Vitals Log table202. If not, it is rejected as being a temporary artifact and thevital signs data112 discarded.
• To accomplish this artifact detection, in one embodiment, a mean value and standard deviation can be calculated for any parameter in relationship to any other hemodynamic parameter over time. This calculation is performed for any relevant decision-making variables (for example, the DP and CO). If the target parameter is outside the standard deviation for the associated parameters of comparison, it is more likely that this new measurement is an artifact and will be flagged by the system and temporarily ignored.
• Over the next few measurements, if the target variable returns to a range expected for the associated variables, the previously detected artifact value(s) is/are left flagged and are ignored in any analysis or processing of thevital signs data112. However, if the target variable remains outside the expected range for longer than a discrete timespan, or the incomingvital signs data112 change such that they are now in ranges correlating with the vital sign of interest, the flags are removed and the measurement is assumed to be real.
• Additionally, certain parameters measured from the same equipment/monitors will be expected to reflect artifacts simultaneously. For example, a heart rate, blood pressure, and stroke volume variation from an arterial line waveform would be expected to either all show artifact or none if the signal were to become noisy.
• Similarly, a parameter monitored from differentclinical monitors115 but reporting the same vital sign (for example, a heart rate calculated from the EKG waveform, arterial line waveform, and plethysmograph waveform) would be expected to change in all three measurements. If there is a significant difference between measurements from differentclinical monitors115, theartifact filtration component206 can determine that there is artifact in one or more of the signals and that one or more of the measurements is likely incorrect.
• Referring again toFIG. 2, a Vitals Log table202, in one embodiment, maintains a complete record of allvital signs data112 accepted from theclinical monitors115 regarding thepatient110, whether temporally contiguous or not. Further, should adifferent control device100 be used on thesame patient110, the data from aprevious control device100 could be transferred to the new vitals log table202 of thenew control device100, either over a network, through a portable media device like a USB drive or electronic smart card, or via some other form of electronic media. At any time, the vitals log table202 will service vitals requestinputs204 for patient data at given times that are received, for example, from other components of themanagement system10. Said vitals requestinputs204 can include both a time point and a time span (e.g., a start time and an end time, or a start time and duration). The time span indicates the period over which the vitals log table202 should compile and average (or perform other statistical analysis on) the requested data. The time point indicates when this average time frame data should be pulled from the log; this is not necessarily the current time but may be any time, past or future, over the entire monitoring period of thepatient110. The response to this request is, in one embodiment, a time averagedvitals pack output205, which contains the requested average vitals and trend information (see below), and is accepted as input at other components of the system.
• One adjunct component to the vitals log table202 is thevitals trend processor203. When newvital signs data112 is added to the vitals log table202, thevitals trend processor203 will take the new data along with any or all portions of the entirety of the preceding data and analyze, identify and store specific trends. These trends will be stored in the vitals log table202 along with the new data on acceptance such that the trends at this time point are also available immediately for any incomingvitals request input204. Such trend analysis, in one embodiment, includes factors like the percent change in heart rate, mean arterial pressure, dynamic predictor, cardiac output, stroke volume, and systemic vascular resistance over the last two minutes, five minutes, and ten minutes.
• FIG. 3 illustrates details of an embodiment of a population basedpredictor component102 that can be used, for example, in thecontrol device100 ofFIG. 1B. The population basedpredictor component102 receives a vitals pack output (time averaged, for example)205 supplied by thevitals manager component101. The vitals packoutput205 is first received by avitals processing component302 and distilled into specific core measures for lookup in population reference tables306. In one embodiment of the system, these measures can include heart rate, stroke volume, systemic vascular resistance, and indications dynamic predictor(s) being utilized, but many other measures are interchangeable. For example, as cardiac output is nothing more than stroke volume multiplied by heart rate, cardiac output could be used in place of either heart rate or stroke volume with equal efficacy, and many other such replacements are feasible. The limit or requirement of four parameters necessary; other embodiments may include fewer or more. Conceptually, the purpose of this distillation of vitals packoutput205 is to succinctly characterize the patient's overall hemodynamic state during the time frame in question in as complete but concise a manner as possible for comparison with the population data (which has been previously similarly characterized and stored in the population reference tables306).
• Once the distillation and characterization of thevitals pack output205 is completed by thevitals processing component302, a referencetable selector component303 identifies one of a plurality of the population reference table306 based on a comparison of (1) patient demographics and comorbidities input301 received from thevitals manager component101, and (2) any dynamic predictor(s) available, with similar data stored in association with the population reference tables306. The population reference tables306 are multi-dimensional references that link specific patient characterizations (e.g., patient demographics and comorbidities and dynamic predictors) in specific sub-populations of patients, to an expected increase in cardiac output. The patient demographics andcomorbidities input301 will be used by the population basedpredictor component102 to determine which population reference table306 is the most appropriate reference to use based on the evolving information about how patient diseases and demographic factors influence the dynamic predictors and cardiac output. In an example embodiment, an 80-year old smoker with heart failure would lead to the selection of a population reference table306 that included only elderly smokers with heart failure, while a similar non-smoking patient would lead to a different population reference table306 for non-smokers. The population reference tables306 are expected to evolve as significant sub-groups are identified. Furthermore, all of the population reference tables306 can be identified by not only the patient population represented, but by a particular dynamic predictor or predictors as well. Thus, there will be different population reference tables306 for the example 80 year old smoker if pulse-pressure variability is available rather than plethysmograph variability, for example. Finally, more general population reference tables306 can also be available such that a patient who does not fit the criteria of a narrow subpopulation can be referenced against the broader population.
• Following the characterization of the patient state with thevitals processing component302 and the selection of the population reference table306 with the referencetable selector component303, a population lookup andhybridization component304 will cross-reference the patient characterization with the chosen population reference table306 and identify the expected increase in cardiac output for the given patient in the current state. This expected increase in cardiac output is, in the standard embodiment, the amount the cardiac output would be expected to increase in response to distinct volume of intravenous fluid over a distinct time span. For example, in one embodiment, this could be 500 ml of fluid over 10 minutes, but any volume and timeframe are possible. This predicted increase, along with a measure of the quality of the prediction (based upon the specificity of the population reference table306 and the quality and frequency of the vitals packoutput data205 received from the vitals manager component101) is exported in apopulation prediction output305 to subsequent components of thecontrol device100.
• FIG. 4 illustrates details of an embodiment of a fluidbolus log component103 that can be used, for example, in thecontrol device100 ofFIG. 1B. The purpose of the fluidbolus log component103 is to track and store bolus log entries400 that include information associated with each distinct volume of intravenous fluid delivered by thecontrol device100 into apatient110. Each bolus log can include one or more of a start time, an end time, a patient condition both before and after the bolus, and the impact of the fluid bolus on hemodynamics. Each bolus log entry400 is initiated with the acceptance of anintervention command415 at abolus start interface410. In addition each bolus log entry400 includes a first vitals pack output205-1 at start time received by thebolus start interface410 from thevitals manager component101. The first vitals pack output205-1 includes information indicative of the vitals of thepatient110 at or prior to the start time of the fluid bolus. Theintervention command415 also includes an end time that determines the timespan over which the fluid is to be delivered. The end time information of theintervention command415 is received by abolus end interface420. At the end of the bolus timespan, or shortly before or after the timespan, thebolus end interface420 receives a second vitals pack output205-2. The second vitals pack output205-2 includes information indicative of the vitals of thepatient110 at or near the end of the fluid bolus.
• Upon initiation of a fluid bolus, the fluidbolus log component103 creates a new bolus log entry400 and assigns abolus ID number401 to the bolus log entry400. In addition, the intervention command includes a reason for initiation of the bolus. The fluidbolus log component103 stores the reason for initiation of the bolus402 in the new bolus log entry400. The first vitals pack output205-1 is also recorded as a patient vitals atstart time403 in the bolus log entry400 such that the bolus log entry400 contains a complete record of the patient vitals at the start of each fluid administration without need to access other data structures within the system. In another embodiment, first vitals pack output205-1 is excluded from this bolus log entry400 and merely the start time recorded, with reference made to the vitals log table202 or another similar data store based on the start time should vital signs data be needed. The new bolus log entry400, as identified by thedistinct bolus ID401, is left open while the fluid bolus is administered.
• Following the conclusion of the fluid bolus, a waiting period can be allowed to pass before analyses of the results of the bolus are attempted. In one embodiment, this period is about two minutes, but this waiting period could be as little as instantaneous to as long as ten minutes without changing the significance of the post-analysis. Following the determined time span of the bolus, an end command of theintervention command415 is received at theend bolus interface420 and the fluid bolus is terminated. The fluidbolus log component103 stores the endbolus time information404 in the bolus log entry400. In addition, after the determined waiting period, the second vitals pack output205-2 is received and recorded by the fluidbolus log component103 at a patientvitals end entry405. In many cases, the end command of theintervention command415 will correspond to the time upon which the target volume of the fluid bolus is achieved, but in other cases this may be a user-specified termination, an early termination due to contraindication by the patient state, or a modification of the previous bolus, among other causes. Following the recording of the end of thebolus information404 and the post-bolus vitals in the patientvitals end entry405, an additional set ofcardiac output calculations406 is added to the bolus log entry400 regarding the efficacy of the bolus for future reference.
• The determination of efficacy of a given bolus of intravenous fluid into the patient is now described in detail for one embodiment of thecontrol device100. First, a simple calculation is made to determine the absolute change in cardiac output from the beginning of the bolus to the end of the bolus:
• 
  ΔCO=(CO_end−CO_start)/CO_start  (1)
• This absolute change ΔCO may not always reflect the true efficacy of a fluid bolus, however. For example, if thepatient110 was losing blood at a rapid pace, the cardiac output would steadily fall as a result. A fluid bolus given during this decline may be insufficient to actually overcome the blood loss and cause an increase in cardiac output, however it would be expected to slow the rate of decline or perhaps hold the cardiac output steady. This would still be an effective fluid bolus, though the absolute change in cardiac output would be flat or even negative and considered ineffective by this measure. Thus, a vector-based analysis can also be performed. The mean rate of change of the cardiac output of the patient over discrete timespans immediately prior to the bolus is noted and compared to the mean rate of change of cardiac output during the period of the fluid bolus. If the rate of change of cardiac output becomes less negative as a result of fluid, then this is also considered an effective bolus.
• An additional method of detection of possible patient conditions confounding calculations of efficacy of a fluid bolus is the known relationship between DP, CO increase with fluid, and the change in DP with fluid.
• The relationship between an increase in DP and an increase in CO in response to a 500 ml bolus in published and unpublished data has a linear regression coefficient (R²) of approximately 0.39 resulting in the equation y=1.54x−1.11, where y corresponds to increase in CO and x corresponds to increase in DP [see Cannesson M, Le Manach Y, Hofer C K, Goarin J P, Lehot J J, Vallet B, et al.; “Assessing the Diagnostic Accuracy of Pulse Pressure Variations for the Prediction of Fluid Responsiveness: a ‘Gray Zone’ Approach.” Anesthesiology, 2011. 115(2): p. 231-41.]. The linear regression coefficient represents the statistical strength of the relationship between DP and CO change. This linear regression coefficient was calculated from the clinical data set. As such, it's not reflected in the equation relating y and x, but represents the “tightness” of the equation's fit to the clinical data.
• The relationship between DP and DP decrease (absolute) in response to a 500 ml bolus has a linear regression coefficient (R²) of approximately 0.63 for the equation y=−0.72x+4.3, where y corresponds to expected decrease in PPV and x corresponds to decrease in PPV.
• Since the PPV/APPV relationship is particularly strong, it can be expected that for any fluid bolus the FP value can be expected to decrease predictably. If the CO does not increase as expected in response to a bolus and the FP does not decrease within a standard deviation of the expected, there is a much higher likelihood that volume loss is to blame. If the CO does increase but the FP does decrease as expected, this is more likely to be attributable to true patient bias or deviation from expected population-based responses.
• Once these calculations are completed, thecardiac output calculations406 are stored in the bolus log entry400 and the bolus log entry400 is then complete. The completed bolus log entry400 is stored as bolus log entry400-1 in abolus log buffer408. Thebolus log buffer408 includes an entire list of bolus log entries400-1,400-2,400-3 through400-N which is made accessible to subsequent data processing modules (specifically thelog predictor component104 and the history analysis component105), as well as packaged for export for sharing of the summary data.
• FIG. 5 illustrates details of an embodiment of alog predictor component104 and an embodiment of ahistory analysis component105 that can be used, for example, in thecontrol device100 ofFIG. 1B. The action of these components is to: 1) in the case of thelog predictor component104, to take bolus log entries400 from the fluidbolus log component103 and vitals packoutput data205 from thevitals manager component101 and determine a likely resulting change in cardiac output due to a new fluid bolus given the current patient state; and 2) in the case of thehistory analysis component105, to determine the accuracy of both the population basedpredictor component102 and thelog predictor component104 in predicting the correct increase in cardiac output in previous boluses in terms of both mean error and standard deviation of the mean error.
• A boluslog processor component501 receives the current vitals packoutput205 and performs a comparison of the current patient state to the patient state at the beginning of any previous boluses. This is accomplished through a process referred to herein as “state similarity” that is essentially a mathematical combination of distinct hemodynamic measures using Bayesian weights. In one embodiment, the state similarity is the square root of the sum of the squares of the differences in each measured parameter divided by the Bayesian weight of the parameter. The specific weights and hemodynamic measures used in this particular embodiment of the device are heart rate, systemic vascular resistance, stroke volume, and dynamic predictor, using weights of 0.33, 4, 0.2, and 0.1, respectively. These weights are merely one example set and may change, depending on patient population, new clinical observations, or the dynamic predictor in question. The calculation of the state similarity for these four parameters of this embodiment is made between the current state C and the previous state P as follows:
• 
  HRSS=HRC−HRP/0.33  (2)
• where HRC is the current heart rate, HRP is the previous heart rate and HRSS is the heart rate state similarity;
• 
  SVRSS=SVRC−SVRP/4  (3)
• where SVRC is the current systemic vascular resistance, SVRP is the previous systemic vascular resistance and SVRSS is the systemic vascular resistance similarity state;
• 
  SVSS=SVC−SVP/0.2  (4)
• where SVC is the current stroke volume, SVP is the previous stroke volume and SVSS is the stroke volume similarity state; and
• 
  DPSS=DPC−DPP/0.1  (5)
• where DPC is the current dynamic predictor, DPP is the previous dynamic predictor and DPSS is the dynamic predictor similarity state. The resulting total state similarity measure of quality “SSTotal” is calculated as follows:
• 
  SSTotal=100−HRSS−SVSS−SVRSS−DPSS  (6)
• The state similarity parameters HRSS, SVRSS, SVSS, DPSS and SSTotal and the corresponding bolus log entry are stored in a statesimilarity subgroup buffer502. Entries in the state similarity boluslog subgroup buffer502 that exhibit a quality measure SSTotal greater than a “similar” threshold level, and are of a sufficient volume and given over a timespan appropriate to draw meaningful conclusions from, are passed on to apredictive analysis component503. Entries in the state similarity boluslog subgroup buffer502 that exhibit a quality measure SSTotal greater than a “highly similar” state similarity threshold level are given added weight. In a typical embodiment, the “similar” and “highly similar” state similarity threshold levels are 50 and 75 respectively. Entries in the state similarity boluslog subgroup buffer502 that exhibit quality measures SSTotal that do not meet these thresholds are discarded. In this way the prediction made by thelog predictor component104 is specific to the current global hemodynamic state of thepatient110, such that if the patient110 changes dramatically over the course of care, previous intervention results are not assumed to be comparable in the new state.
• Thelog processor component501 can start the state similarity analysis of the bolus log entries400 with the most recent entries and work backward in time. As previous bolus log entries400 meet the predetermined state similarity thresholds, a running tally of the total state similarity of entries found is kept. When this tally reaches another threshold level, thelog processor component501 stops further state similarity analysis and retains only the entries already captured. In this way, the algorithm manifests both a memory of its past efficacy as well as a ‘forgetfulness’, such that if some aspect of the patient changes in a way that truly alters the patient response to fluid the system will not be permanently biased by the early results but rather will be progressively more influence by the recent results of interventions.
• As an example of this behavior, take a patient in steady state during surgery. The patient cardiac output is 4.5 and the system attempts a fluid bolus. The cardiac output improves to 5.6, a 21% increase. This is considered effective by the system and is recorded as such in one of the bolus log entries400. Subsequently the patient has some ischemic heart injury and the cardiac output falls back to 4.5, not because of hypovolemia but because of a loss of inotropy. The system will likely attempt another bolus in this state as the previous one was successful, but in this instance there will be no improvement in cardiac output because the intravascular volume is already maximized. Were it not for the forgetful component of the state similarity analysis of the bolus log entries400, the system might continually operate using the initial success indefinitely and thus continue to deliver fluid inappropriately. The emphasis of recent activity and results over more distant activity and results ensures that ineffective interventions are rapidly phased out.
• Once the entries in the state similarity boluslog subgroup buffer502 that are applicable to the current patient state are selected by thelog processor component501, a predictive analysis is performed on thesubgroup buffer502 by apredictive analysis component503. The predictive analysis calculates a running average of the change in cardiac output recorded for the entries stored in thesubgroup buffer502. In one embodiment, the entries in thesubgroup buffer502 are weighted in the running average calculation by their quality measure SSTotal as determined by thelog processor component501, such that the higher the SSTotal of a previous bolus to the current patient state the more weight the prediction will carry in the combined running average. The output frompredictive analysis component503 is alog prediction packet507 and aquality rating packet506 that includes information about the total number of entries in the subgroup and the corresponding state similarity quality measures SSTotal.
• Referring again toFIG. 5, thesubgroup buffer502 selected by thelog processor component501 is also passed into thehistorical accuracy analyzer504 of thehistory analysis component105. Thehistorical accuracy analyzer504 reviews each of the selected bolus log entries400 contained in thesubgroup buffer502 and compares the cardiac output increase predicted by the population basedpredictor component102 and by thelog predictor component104 to the actual increase seen in the patient and calculates the average error and standard deviation of the error for each respective predictor. This information is exported and stored in a bolus history analysis buffer505.
• FIG. 6 illustrates details of an embodiment of theprediction engine106 that can be used, for example, in thecontrol device100 ofFIG. 1B. Theprediction engine106 receives inputs from all of the other predictive components. Specifically, theprediction engine106 receives thepopulation prediction output305 from thepopulation predictor component102, thelog prediction packet507 and thequality rating packet506 from thelog predictor component104, and the bolus history analysis buffer505 from the history analysis component505. Theprediction engine106 uses these inputs to generate a final combined predicted cardiac output change for the current state of thepatient110.
• The process begins with theprediction engine106 receiving apopulation prediction output305, alog prediction packet507, alog quality rating506, and a bolus history analysis505. The predictions included in thelog prediction packet507 and thepopulation prediction output305 are adjusted by the mean error value contained in the bolus history analysis buffer505 for the respective components. Then the standard deviation of error is both added and subtracted from each component to yield two predictions for each component representing the top and bottom of the range of increase of cardiac output expected. The top and bottom predictions from a first one of the predictive components is averaged with the top and bottom predictions from the second one of the predictive components using the inverse of the standard deviations of each to weight the averages. This results in the narrower standard deviation of the two being given more weight in the resulting value. This results in two final predictions, one for the top and one for the bottom. In the embodiment shown, a bestcase prediction component602 calculates the top or greatest predicted change in cardiac output and a worst case prediction component603 calculates the bottom or least predicted change in cardiac output.
• Afinal prediction component604 combines the best case prediction and the worst case prediction to determine the final predicted change incardiac output605. In the embodiment shown, an “optimism”input601 is provided to thefinal prediction component604. In this embodiment, theoptimism input601 is a user-defined value between zero and 100 that is used to determine the final predicted change incardiac output605 made by thefinal prediction component604 within the best case and worst case values determined by the best and worstcase prediction components602 and603, respectively. A value of 100 of theoptimism input601 results in the best case predicted change, while a value of zero results in the worst case predicted change in cardiac output. Values between zero and 100 will result in values between the best and worst case values (e.g., linearly interpolated values). The final predicted change incardiac output605 is then output. In another embodiment, theoptimism input601 is a fixed value of 100, 75, 50, 25, 0, or some value in-between determined in advance or hard-coded into the algorithm. In yet another embodiment theoptimism input601 is a range of values between the lower end of zero and the upper end of 100.
• FIG. 7 illustrates details of an embodiment of anintervention decision component107 that can be used, for example, in thecontrol device100 ofFIG. 1B. Theintervention decision component107 receives the final predicted change incardiac output605 determined by theprediction engine106, and in conjunction with anaggression rating710 determines an action to be taken by thecontrol device100. In one embodiment the aggression rating is a user-specified value within the range −10 to +10, inclusive. In another embodiment, theaggression rating710 can be fixed, or may have a different range. Regardless of the way theaggression rating710 is determine, theaggression rating710 is used to generate theintervention command415 that is provided to thepump manager component108.
• The final predicted change in cardiac output605 (given in percentage increase in cardiac output in this embodiment) received by theintervention decision component107 is received by anintervention range selector700 and compared to decision ranges to determine the appropriate course of action. In one embodiment, there are four such ranges, though more or less may be present in other embodiments. The lowest such range in this embodiment is thelow threshold zone704, in which the administration of fluid is likely to be at best not helpful and at worst detrimental. A second range is thegray zone703, in which the outcome of fluid administration is either indeterminate or mixed. A third range is thehigh threshold zone702 in which fluid is likely to increase cardiac output significantly. A fourth range is thecritical threshold zone701 in which fluid is almost certain to have a substantial positive impact. In this embodiment, the percentage increase in cardiac output for these zones are 0-7% for thelow threshold zone704, 8-14% for thegray zone703, 15-25% for thehigh threshold zone702, and 25% and greater for thecritical threshold zone701.
• As indicated inFIG. 7, each of these zones corresponds to an action or set ofactions705,706,707 or708 to be taken by thecontrol device100. The specific response to a given zone will depend on the embodiment of the system and the number of zones. In the embodiment illustrated inFIG. 7, the response taken in each of the zones is as follows: in thelow threshold zone704 the response is to stop any ongoing infusion and continue monitoring708; in thegray zone703, the response is to continue whatever action is currently takingplace707, and in some cases to deliver a test bolus (see below); in thehigh threshold zone702, the response is to initiate a fluid infusion, or to continue an infusion if already running706; and in thecritical zone701, the response is to accelerate any ongoing infusion to the maximum system rate or start such an infusion if not already active705. The final action is sent as anintervention command415 to thepump manager108.
• The volumes and rates of the infusions being started upon intervention are specific to the embodiment of thecontrol device100 and depend on the characteristics of thepumps109. In an embodiment for use in ahuman patient110, ahigh threshold zone702 infusion response706 is 250 ml of fluid, given over a time span of 5-10 minutes depending on the pump capacity, and acritical zone701 infusion705 is 500 ml of fluid given over 5-10 minutes depending on the pump capacity. In other embodiments these volumes may be modified as well as the time spans of delivery.
• Again referring toFIG. 7 and the preferred embodiment of the device, in some cases a “test bolus” will be delivered by the pump in the Gray Zone to determine whether or not fluid is likely to be beneficial. This test bolus can be 100 ml of fluid delivered over a time span of between one and ten minutes. Specifically, when a patient is determined to be in the gray zone, and there are no high quality bolus log entries400 as determined by thestate similarity processor501 within a specified timeframe, a test bolus is initiated. In one embodiment this timeframe is two, three, four, six, ten, or twelve hours.
• With further reference toFIG. 7, theaggression rating710 is used to modify the zone boundaries during operation of thecontrol device100. Apositive aggression rating710 will lower the percentage change in cardiac output of the boundaries, thereby making thecontrol device100 more likely to deliver fluid for any given prediction by the system. A negative aggression rating will raise the percentage change in cardiac output of the boundaries, and thereby make thecontrol device100 less likely to deliver fluid for any given prediction by the system. The boundaries may, in one embodiment, be raised by a fixed value for each point of theaggression rating710 and in another embodiment may be multiplied or divided by a value for each point of theaggression rating710. Theaggression rating710 is, in one embodiment, determined by a supervisor during operation of thecontrol device100.
• In another embodiment, theintervention decision component107 may also include a direct input from thevitals manager component101 through which theintervention decision component107 receives instantaneous or short-time-averaged vital signs. In this embodiment theintervention decision component107, in addition to making recommendations and decisions about IV fluid administration also makes recommendations and decisions about medication administrations.
• In such an embodiment, vasoactive medications may be recommended to the supervisor if deemed appropriate, or, in one embodiment, delivered to thepatient110 directly by the system after supervisory approval or autonomously.
• FIG. 8 illustrates details of an embodiment of apump manager component108 that can be used, for example, in thecontrol device100 ofFIG. 1B. As discussed above, the function of thepump manager component108 to is to coordinate fluid pump activity, to monitor the fluid pumps109 to ensure proper response to commands issued to the fluid pumps109, and to interact with a user interface810 when appropriate. In one embodiment, thepump manager component108 is the only component of thecontrol device100 that receives direct instructions in the form of actionable commands from the user interface810.
• The monitoring and control functions of thepump manager component108 are provided by interaction between a corepump manager component801, apump director component803, the fluid pumps109-3 and109-4, and apump monitor802. Any desired pump action communicated from the user interface810 or received in anintervention command415 from theintervention decision component107 is received by the corepump manager component801 and relayed to thepump director component803. The received pump actions contain explicit information and formatting enabling the corepump manager component801 to communicate those actions to the specific fluid pumps used in each particular embodiment. Thepump director component803 will confirm that the commands are received and accepted by the fluid pumps109-3 and/or109-4, but thepump director component803 does not track the action of the pumps109-3 and109-4 beyond this step. If the commands are rejected, not acknowledged, or any other communication error arises between thepump director component803 and the fluid pumps109-3 and109-4, this is communicated back to the corepump manager component801 along with the nature of the error.
• One feature of operation of thepump manager component108 is that any command sent to the fluid pumps109-1 or109-2 for delivery of an IV fluid bolus113-1 or113-2 contains both a rate and a volume for delivery. Furthermore, the fluid pumps109 themselves can be designed such that only a command containing both elements is accepted by the pumps. This feature can ensure safety to thepatient110 should communication between the system and the fluid pumps fail for any reason, and further, should the fluid pumps109, upon loss of communication with the rest of the system, fail to halt active infusions and enter a standby mode until communication is reestablished, they will automatically stop infusing fluid at the completion of the dictated bolus volume. Thus, the fluid pumps109 are not left indefinitely in an infusing mode due to loss of system communication which could result in over-administration of fluid and pose a safety risk to the Patient.
• Thepump monitor component802 is responsible for the ongoing supervision of the fluid pumps109 following the initial issuance of a command by thepump director component803. Thepump monitor component802, at system specified time intervals, queries the fluid pumps109 and requests an update on their status. The time interval, in one embodiment, may be 100 seconds, 10 seconds, 1 second, 1/10th seconds, 1/100th seconds, or 1/1000th seconds. The status update, in one embodiment, can contain the current pump state (on, off, waiting, infusing, alarm, error) along with detailed information about the amount of volume given in the current infusion (if active), the rate of the current infusion (if active), the total volume given to this patient, the back pressure in the infusion tubing, the available volume to infuse (if present), the nature of the fluid available to infuse (if present), and detailed alarm information (if active). This information is organized and transmitted to the corepump manager component801.
• The corepump manager component801, as previously described, is responsible for coordination of all pump activity involving the intervention commands415 received from theintervention decision component107 or from other parts of the system such as the user interface810. Commands are passed into thepump director component803 for transmission to the fluid pumps109. If the intervention commands415 are accepted no further action is needed. If a command fails due to any of the communication errors discussed above, or for any other reason, this failure is relayed back to the corepump manager component801 by thepump director component803 for further action. If the nature of the failure may be temporary, for example, a failure of acknowledgement of the command by one of the fluid pumps109, the corepump manager component801 may attempt to repeat the command one or more times before taking other action. In other failures that are not likely to be resolvable by the system (for example, air detected in the pump tubing or similar failure alarms, or a pump hardware failure), then an alarm condition may be set by the corepump manager component801 indicating the nature of the alarm and relayed to the user interface810 for communication with and input from the human supervisor.
• In the event of an alarm condition specific to asingle fluid pump109, in an embodiment that possesses two or more fluid pumps109, the corepump manager component801 may continue operating and attempt to compensate for the fluid pump109-3 that has failed with the other available fluid pump109-4 or vice-versa until the condition can be rectified by the supervisor or the condition resulting in the alarm is otherwise resolved. In the event of an alarm condition affecting the entire system, the monitoring of vital signs or the quality of information received into thevitals manager component101, or in an embodiment with only onefluid pump109, the corepump manager component801 will attempt to halt active infusions if possible and will standby until the alarm condition is resolved satisfactorily by the supervisor. Any desired actions dictated by the system in this state will be ignored.
• In addition to passing commands on to thepump director component803, the corepump manager component801 also verifies the data received from thepump monitor component802 to make sure the actual state of the fluid pumps109 is consistent with the state dictated by the last command provided by thepump director component803. If the fluid pumps109 are in a state different from the expected state but communication between the fluid pumps109 and corepump manager component801 is intact, the corepump manager component801 will attempt to issue corrective commands to thepump director component803 to place the fluid pumps109 into the correct state. If this fails to correct the discrepancy then a global alarm condition can be set and communicated to the user interface810 and further pump commands ignored until the alarm condition is resolved.
• In some instances, the state of one or more of the fluid pumps109 will be affected by the last command issued by thepump director component803 in an expected manner. For example, if thepump director component803 issues a command to infuse 500 ml of fluid over 10 minutes, after this infusion completes the fluid pumps109 will return to standby mode. This is the expected progression of the last issued instruction and as such will be anticipated by the corepump manager component801 and treated as consistent with the last issued command. Thus, no alarm condition would result.
• Referring again toFIG. 8, the corepump manager component801 is also configured, in a decision-support or physician verification mode, for communication of desired actions to the user interface810 for verification by the supervisor. In this mode, anyintervention command415 received is not acted on by the corepump manager component801 autonomously. Instead, theintervention command415 is conveyed to the user interface810 and displayed to the user along with the option to accept or reject the command. If the command is rejected the corepump manager component801 takes no further action and will ignore additional identical commands until such a time as the command changes or a pre-determined time span passes. Depending on the embodiment, this time span can be between one and sixty minutes, for example. If the command is approved then the corepump manager component801 passes the command along to thepump director component803 for conveyance to the fluid pumps109.
• The corepump manager component801 may also receive specific override commands from the supervisor. These are described in detail later. Should the corepump manager component801 receive such an override command that command is also communicated to the fluidbolus log component103 in the form of anintervention command415, for proper registration of the intervention command as a bolus log entry400 in thebolus log buffer408.
• Referring again toFIG. 8, in one embodiment, thepump director component803 may be absent from the operation of the system. In this embodiment, the system has no direct control over the fluid pumps109 whatsoever. Instead, the operation of the fluid pump(s)109 is handled by the supervisor directly through the fluid pump(s) own control interface(s). The activity of the fluid pump(s)109 is relayed back to the corepump manager component801 through thepump monitor component802, and this information is passed along to the fluidbolus log component103 in order to track the administration ofIV Fluid113 into thepatient110. Meanwhile, the decisions of thecontrol device100 are relayed to the supervisor through the user interface810 as recommendations of care. These recommendations may be displayed to the supervisor in graphical or numeric fashion, the main point being to convey the degree of fluid responsiveness (or predicted efficacy of additional fluid) in thepatient110 by the system.
• FIG. 9 illustrates details of another exemplary patient-adaptivehemodynamic management system10 in accordance with the disclosure. In this embodiment, anIV bag125 of fluid is hung above thecontrol device100 and, using purposedtubing135 already primed and inserted into thecontrol device100tubing135, channel and then connect to standard IV access points on thepatient110 or other tubing, delivered by thecontrol device100 as described throughout the disclosure. Vital signs are delivered to thecontrol device100 through a physical connection with standard operating roomclinical monitors115 in the illustrated embodiment ofFIG. 9. In another embodiment theclinical monitors115 are replaced with monitors integrated into thecontrol device100 and connected to thepatient110, or integrated into thecontrol device100 and in communication with wireless leads and contacts placed on thepatient110. In another embodiment the vital signs are communicated to thecontrol device100 from other monitors over a wireless network such as Bluetooth or other wireless communication protocols. In yet another embodiment, the vital signs are monitored from a distance by devices not in physical contact with the patient and subsequently communicated to thecontrol device100.
• Again referring toFIG. 9, in the illustrated embodiment there oneIV bag125 fluid shown and one line oftubing135 is shown connecting theIV bag125 to thecontrol device100. In another embodiment, two, three, or fourdifferent IV bags125 each with its own tubing run through a dedicated channel in the device, and eachIV bag125 containing different fluids may be used. In such an embodiment, the user of thecontrol device100 may specify which bag should be used first, second, third, or so on by the system. In another embodiment of thecontrol device100, thecontrol device100 is made aware of what the individual fluid bags contain and will determine which fluid is appropriate based on internal rule sets.
• In another embodiment of themanagement system10, one of theclinical monitors115 provides a continuous or intermittent measure ofpatient110 hemoglobin concentrations. In another embodiment of themanagement system10, one of themonitors115 provides a measure of mixed central venous oxygen saturation or regional central venous oxygen. In yet another embodiment of themanagement system10 one of the monitors provides a continuous or intermittent measure of regional or global tissue oxygen delivery or utilization.
• In another embodiment of themanagement system10, thecontrol device100 is coupled to either oxygen delivery, tissue oxygenation or utilization, blood hemoglobin concentration, mixed central venous oxygen saturation, or regional venous oxygen saturation, and one of the fluids available to thecontrol device100 is an oxygen carrying product such as, for example, whole blood, packed red blood cells, salvaged patient blood from the surgical field, artificial blood products or an oxygen-carrying blood substitute. In another embodiment, based on the described setup, themanagement system10 may elect to give such an oxygen carrying product instead of other available fluids in the event oxygen delivery to tissues is determined to be inadequate. This may occur independently of decisions made regarding the benefit of fluids alone as determined by the components described above.
• FIG. 10 illustrates information flow between various components of the patient-adaptivehemodynamic management system10 ofFIG. 1A.FIG. 10 shows the same components asFIG. 1, but in this illustration the connections between the components are not shown. Instead the connections between the system components and the user interface810 and anetwork server interface1010 are demonstrated.
• As illustrated inFIG. 10, each component of themanagement system10 has outputs to the user interface810. These outputs may provide data that can be displayed or not displayed to the supervisor depending on the particular embodiment and implementation of thecontrol device100. In the embodiment shown, the only active input from the user interface810 to components of thecontrol device100 is an input thepump manager component108, specifically the corepump manager component801. This input carries, in one embodiment, the approval or rejection of recommended intervention commands415 as determined by the supervisor. Additionally, in another embodiment, this input carries supervisor directed override commands to either halt the fluid pump(s)109 or to initiate a new infusion. These override commands will either persist indefinitely until cleared by the supervisor or will timeout after a predetermined timespan at which point the affectedfluid pump109 will revert to the previous mode of operation.
• In one embodiment, an additional supervisor override command is a “line flush” command. When the fluid pumps109 are already actively infusing fluid, this line flush command has no effect. When the fluid pumps109 are in a standby state, the line flush command will cause the fluid pumps109 to briefly activate and infuse, for example, 2, 5, 8, 10, or 15 ml of fluid depending on the total volume of fluid present in the IV fluid set in the current configuration. After this volume of fluid is infused, the fluid pumps109 return to a standby state. The purpose of the line flush command is to drive the column of fluid present in the line into thepatient110 such that any medication administered into the line downstream of the fluid pumps109 will also be flushed into thepatient110.
• An additional supervisor override command present in another embodiment of thecontrol device100 is a “test bolus” command. When the fluid pumps109 are in a standby mode, the test bolus command causes a new fluid bolus to be initiated. The volume and time of this bolus are, in one embodiment, 50 ml, 100 ml, 150 ml, 200 ml, or 250 ml of fluid given over 1, 2, 3, 4, 5, 10, or 15 minutes. If a new infusion command is received (and accepted by the supervisor, if necessary), during this test bolus, the test bolus is stopped and the new bolus is started. Otherwise the test bolus will run to completion and then halt.
• Referring again toFIG. 10, two components of the system interact with thenetwork server interface1010, the fluidbolus log component103 and the population basedpredictor component102. These interactions may not occur continuously during routine operation of the system, nor are they required as a component of the system.
• In one embodiment, specific information is exchanged between thecontrol device100 and the network at the beginning and end of operation, or at preset time intervals. Specifically, thebolus log408 and the patient demographics andcomorbidities301 are packaged and communicated to other networked components via thenetwork server interface1010. This data includes the details of thebolus log408, significant patient disease states, and non-identifying demographic data such as patient age, height, weight, and gender. This data may not contain, in one embodiment, any information making specific identification of the source patient possible through the dataset.
• The information communicated via thenetwork server interface1010 is received by othernetworked control devices100 and processed and reorganized into population reference tables306 which may be specific to a pertinent patient subset or to the population in general. Subsequently, at device startup time or at preset time intervals, thenetworked control devices100 can update their own internal population reference tables306 through thenetwork server interface1010. In this way, the population reference tables306 will evolve over time as increasingly detailed population data becomes available for any given patient population.
• Thenetwork server interface1010 may connect to other networked servers and/orcontrol devices100 over any of a number of forms of electronic communication, including but not limited to Ethernet, Wireless TCP/IP, Bluetooth, Cellular technologies, or removable media formats.
• Attention is now directed toFIG. 11 which is a flowchart depicting anexemplary process1100 of providing patient-adaptive hemodynamic management in accordance with the disclosure. Theprocess1100 can be performed by the patient-adaptivehemodynamic management system10 illustrated inFIGS. 1A and 1B including thecontrol device100 illustrated inFIGS. 2-8. Theprocess1100 is exemplary only and not limiting. Theprocess1100 can be altered, e.g., by having stages added, removed, rearranged, combined and/or performed concurrently.
• Theprocess1100 will be described in reference to thecontrol device100 ofFIGS. 1A,1B andFIGS. 2-8. Theprocess1100 begins atstage1110 where thevitals manager component101 receives measurements of vital signs fromclinical monitors115. The received vital signs can include physiologic processes such as cardiac output, stroke volume, heart rate, blood pressure and arterial pressure for example.
• Upon receiving the vital signs, theprocess1100 continues atstage1114 where thecontrol device100 determines, based at least in part upon the vital signs measurements, a predicted change in a physiologic parameter in response to a fluid bolus. The predicted change can be predicted in part by the population basedpredictor component102 based on population statistics, and/or in part by thelog predictor component104 and/or, in part by thehistory analysis component105 as discussed above. In addition, the population based prediction portion, the bolus log prediction portion and the history analysis predictions can be combined by theprediction engine106 as discussed above. The physiologic parameter can include one or more of cardiac output or stroke volume of thepatient110.
• Upon predicting the change in the physiologic parameter, theprocess1100 continues atstage1118 where theintervention decision component107 generates a fluid administration signal in response to the predicted change. The administration signal can be a signal to stop a fluid bolus currently being administered, to continue a current action and continue to test the vital signs, to start a new fluid bolus or continue a fluid bolus if one is being administered and to provide a maximum fluid bolus.
• The administration signal is communicated by theintervention decision component107 to thepump manager component108. Atstage1122, thepump manager component108 generates control signal(s) for infusion pump(s) such as the fluid pumps108 based upon fluid administration signal to cause the fluid pumps109 to take the appropriate action.
• Attention is now directed toFIG. 12 which is a flowchart depicting aexemplary method1200 of providing patient-adaptive hemodynamic management including a user intervention in accordance with the disclosure. Theprocess1200 can be performed by the patient-adaptivehemodynamic management system10 illustrated inFIGS. 1A and 1B including thecontrol device100 illustrated inFIGS. 2-8. Theprocess1200 is exemplary only and not limiting. Theprocess1200 can be altered, e.g., by having stages added, removed, rearranged, combined and/or performed concurrently.
• Atstage1210, thevital signs manager101 receives measurements of vital signs fromclinical monitors115 in a similar fashion as atstage1110 discussed above. Atstage1214, thecontrol device100 determines, based upon the vital signs measurements, a predicted change in a physiologic parameter in response to a fluid bolus.Stage1214 can be performed in a similar fashion to stage1114 discussed above. Atstage1218, theintervention decision component107 generates a fluid administration signal in response to the predicted change in a similar fashion as discussed above in reference tostage1118.
• Atstage1222, instead of generating a control signal with thepump manager component107, as was done atstage1122 in theprocess1100, theintervention decision component107 causes the user interface810 to generate a representation of an intervention command on the user interface which corresponds to the fluid administration signal. Atstage1226, the user interface810 receives a user input indicating acceptance or rejection of the intervention command. If the user input is a rejection, theprocess1200 continues atstage1234 where the current infusion or lack of infusion is continued in its existing state. If the user input received atstage1226 is an acceptance, theprocess1200 continues atstage1230 where the user interface810 or theintervention decision component107 generates a control signal or signals based upon the intervention command received atstage1226 and communicates the control signal(s) to thepump manager component108.
• Attention is now directed toFIG. 13 which is a flowchart depicting anotherexemplary process1300 of providing patient-adaptive hemodynamic management in accordance with the disclosure. Theprocess1300 can be performed by the patient-adaptivehemodynamic management system10 illustrated inFIGS. 1A and 1B including thecontrol device100 illustrated inFIGS. 2-8. Theprocess1300 is exemplary only and not limiting. Theprocess1300 can be altered, e.g., by having stages added, removed, rearranged, combined and/or performed concurrently.
• Theprocess1300 starts atstage1310 where thevitals manager component101 receives information relating to an initial change in a physiologic parameter of a patient in response to administration of a first fluid bolus to the patient. Atstage1314, a predicted change in the physiologic parameter in response to administration of a second bolus to the patient is made by one or more of the population basedpredictor component102, thelog predictor component104, thehistory analysis component105 and theprediction engine106 using any of the methods discussed above.
• Upon determining the predicted change atstage1314, theprocess1300 continues to stage1318 where theintervention decision component107 and thepump manager component108 adjust administration of the second fluid bolus to the patient based upon the predicted change.
• Attention is now directed toFIG. 14 which is a flowchart depicting anotherexemplary process1400 for providing patient-adaptive hemodynamic management in accordance with the disclosure. Theprocess1400 can be performed by the patient-adaptivehemodynamic management system10 illustrated inFIGS. 1A and 1B including thecontrol device100 illustrated inFIGS. 2-8. Theprocess1400 is exemplary only and not limiting. Theprocess1400 can be altered, e.g., by having stages added, removed, rearranged, combined and/or performed concurrently.
• Atstage1410, thevitals manager component101 receives input information relating to one or more physiologic processes of a patient from one or more of the clinical monitors115. Atstage1414, the fluidbolus log component103 provides bolus log entries associated with a current state of the patient to thelog predictor component104 and thelog predictor component104 determines, based at least in part upon the input information, a subgroup of the bolus log entries. The subgroup of entries can be biased based on a state similarity analysis as discussed above in reference toFIG. 5.
• Atstage1418, theintervention decision component107 adjusts administration of fluid to the patient based at least in part upon log data included within the subgroup of bolus log entries.
• Attention is now directed toFIG. 15 which is a flowchart depicting anotherexemplary process1500 for providing patient-adaptive hemodynamic management in accordance with the disclosure. Theprocess1500 can be performed by the patient-adaptivehemodynamic management system10 illustrated inFIGS. 1A and 1B including thecontrol device100 illustrated inFIGS. 2-8. Theprocess1500 is exemplary only and not limiting. Theprocess1500 can be altered, e.g., by having stages added, removed, rearranged, combined and/or performed concurrently.
• Theprocess1500 starts atstage1514 where thevitals manager component101 determines a first effect on a physiologic parameter of the patient associated with administration of a first fluid bolus to the patient. Thevitals manager component101 or the fluidbolus log component103 can determine the first effect by analyzing vitals signals received from theclinical monitors115 and comparing the received signals to previously received and stored signals. The physiologic parameter can include one or more of cardiac output, stroke volume, etc.
• Atstage1518, the fluidbolus log component103 stores first information relating to the first effect in a first bolus log entry. The first bolus log entry can be the bolus log entry400 discussed above in reference toFIG. 4. The first bolos log entry can be stored in memory such as RAM, ROM, flash or other type of computer readable storage medium.
• Atstage1522, thevitals manager component101 or the fluidbolus log component103 determines a second effect on the physiologic parameter of the patient associated with administration of a second fluid bolus to the patient. The first effect can be determined by analyzing vitals signals received from the clinical monitors115.
• Atstage1526, thevitals manager component101 or the fluidbolus log component103 stores second information relating to the second effect in a second bolus log entry. The second bolus log entry can be a second bolus log entry400 store in a computer readable storage medium.
• Atstage1530, theintervention decision component107 generates a fluid administration signal based at least in part upon at least one of the first bolus log entry and the second bolus log entry. Theintervention decision component107 can receive information indicating one or more of the type of fluid bolus to administer, an amount of fluid bolus to administer and or an rate and time over which to administer the fluid bolus from thelog predictor component104 or theprediction engine106.
• Attention is now directed toFIG. 16 which is a flowchart depicting anotherexemplary process1600 for providing patient-adaptive hemodynamic management in accordance with the disclosure. Theprocess1600 can be performed by the patient-adaptivehemodynamic management system10 illustrated inFIGS. 1A and 1B including thecontrol device100 illustrated inFIGS. 2-8. Theprocess1600 is exemplary only and not limiting. Theprocess1600 can be altered, e.g., by having stages added, removed, rearranged, combined and/or performed concurrently.
• Theprocess1600 starts atstage1610 where the population basedpredictor component102 receives information relating to an expected change in cardiac output in a population group in response to administration of a fluid bolus. The population based information can be stored in a computer readable medium such as one of the population reference tables306 discussed above.
• Atstage1614, thelog predictor component104 determines a patient-specific bias relating to the administration of the fluid bolus based upon prior responses of the patient to fluid administration. The bias can be accomplished by using a state similarity analysis of a subgroup ofbolus log entries502 as discussed above in reference toFIG. 5.
• Atstage1618, thelog predictor component104 and/or theprediction engine106 generates a predicted change in cardiac output of the patient in response to administration of the fluid bolus based at least in part upon the information relating to the expected change and the patient-specific bias.
• Attention is now directed toFIG. 17 which is a flowchart depicting anotherexemplary process1700 for providing patient-adaptive hemodynamic management in accordance with the disclosure. Theprocess1700 can be performed by the patient-adaptivehemodynamic management system10 illustrated inFIGS. 1A and 1B including thecontrol device100 illustrated inFIGS. 2-8. Theprocess1700 is exemplary only and not limiting. Theprocess1700 can be altered, e.g., by having stages added, removed, rearranged, combined and/or performed concurrently.
• Atstage1710, thevitals manager component101 receives an indication of at least one of oxygen delivery to tissues of the patient and oxygen utilization by the patient. The indication can be received from a monitoring device such as one of theclinical monitors115 or from an input to the user interface810, for example. Thevitals manager component101 stores the indication in one of the bolus log entries in the vitals log table202. The vitals log entry is then communicated to theintervention decision component107. Alternatively, theintervention decision component107 can receive the indication directly.
• Atstage1714, based upon the indication, theintervention decision component107 selects an oxygen-carrying fluid from among multiple fluids capable of being infused into the patient. Atstage1718, theintervention decision component107 signals, using a processor, selection of the oxygen-carrying fluid for infusion into the patient.
• Attention is now directed toFIG. 18 which is a flowchart depicting anotherexemplary process1800 of providing patient-adaptive hemodynamic management in accordance with the disclosure. Theprocess1800 can be performed by the patient-adaptivehemodynamic management system10 illustrated inFIGS. 1A and 1B including thecontrol device100 illustrated inFIGS. 2-8. Theprocess1800 is exemplary only and not limiting. Theprocess1800 can be altered, e.g., by having stages added, removed, rearranged, combined and/or performed concurrently.
• Atstage1810, a network server receives bolus log information from a plurality of fluid delivery systems communicatively coupled to the network server. The fluid delivery systems can include, for example, the patient-adaptivehemodynamic management system10 discussed above. After receiving the bolus log information, theprocess1800 proceeds to stage1814 where the network server generates a set of population reference tables respectively containing portions of the bolus log information such as, for example the population reference tables306 discussed above in reference toFIG. 3.
• Atstage1818, the network server sends at least a portion of the bolus log information to one or more of the plurality of fluid delivery systems such as the patient-adaptivehemodynamic management system10. The patient-adaptivehemodynamic management system10 can then utilize the population reference tables306 to predict changes in physiologic parameters of a patient based on an ever increasing population of patients.
• Attention is now directed toFIG. 19 which is a flowchart depicting anotherexemplary process1900 of providing patient-adaptive hemodynamic management in accordance with the disclosure. Theprocess1900 can be performed by the patient-adaptivehemodynamic management system10 illustrated inFIGS. 1A and 1B including thecontrol device100 illustrated inFIGS. 2-8. Theprocess1900 is exemplary only and not limiting. Theprocess1900 can be altered, e.g., by having stages added, removed, rearranged, combined and/or performed concurrently.
• Atstage1910, thevitals manager component101 receive input information relating to one or more physiologic processes of the patient. The input information can be received from one of theclinical monitors115 and/or from the user interface810, for example. Thevitals manager component101 stores information indicative of the input information in a bolus log entry of the bolus log table202.
• Atstage1914, theintervention decision component107 determines, based upon the input information obtained by receiving the bolus log entry containing the stored information, that a change has occurred in a physiologic parameter of the patient. In response, depending on the physiologic parameter being in one of a plurality of range of values such as one of theranges701,702,703 or704 discussed above in reference toFIG. 7, theintervention decision component107 generates a fluid administration signal based at least in part on the change in the physiologic parameter. As discussed above, the administration signal can stop a current bolus, start a new bolus, increase or decrease an infusion rate or maximize an infusion rate, depending on which of the plurality of threshold ranges the physiologic parameter is in.
• Attention is now directed toFIG. 20 which is a flowchart depicting anotherexemplary process2000 for providing patient-adaptive hemodynamic management in accordance with the disclosure. Theprocess2000 can be performed by the patient-adaptivehemodynamic management system10 illustrated inFIGS. 1A and 1B including thecontrol device100 illustrated inFIGS. 2-8. Theprocess2000 is exemplary only and not limiting. Theprocess2000 can be altered, e.g., by having stages added, removed, rearranged, combined and/or performed concurrently.
• Atstage2010, thevitals manager component101 receives input information relating to one or more physiologic processes of thepatient110. The input information can be received from one of theclinical monitors115 or from the user interface810, for example. Upon receiving the input information, thevitals manager component101 stores information indicative of the input information in a bolus log entry of the bolus log table202.
• Atstage2014, one or more of the population basedpredictor component102, thelog predictor component104, thehistory analysis component105 and theprediction engine106 determines, based at least in part upon the input information using the methods discussed above, a predicted change in a physiologic parameter of thepatient110 in response to administration of a fluid bolus to the patient.
• Atstage2018, one or more of the components of thecontrol device100, e.g., theintervention decision component107 or theprediction engine106, provides a fluid administration recommendation in response to the predicted change to the user interface810. Atstage2022, a display of the user interface810 displays information relating to the recommendation on the user interface810. Atstage2022, theintervention decision component107 or the user interface810 receives user input relating to the recommendation through the user interface810. Theintervention decision component107 or the user interface810 can then generate an administration signal to thepump manager component108 related to the recommendation.
• Attention is now directed toFIG. 21 which is a flowchart depicting anotherexemplary process2100 for providing patient-adaptive hemodynamic management in accordance with the disclosure. Theprocess2100 can be performed by the patient-adaptivehemodynamic management system10 illustrated inFIGS. 1A and 1B including thecontrol device100 illustrated inFIGS. 2-8. Theprocess2100 is exemplary only and not limiting. Theprocess2100 can be altered, e.g., by having stages added, removed, rearranged, combined and/or performed concurrently.
• Atstage2110, thevitals manager component101 receives information relating to an initial change in a physiologic parameter of apatient110 in response to administration of a first fluid bolus to thepatient110. The received information can be received from one of theclinical monitors115 or from the user interface810, for example. Upon receiving the input information, thevitals manager component101 stores information indicative of the input information in a bolus log entry of the bolus log table202.
• Atstage2114, one or more of the population basedpredictor component102, thelog predictor component104, thehistory analysis component105 or theprediction engine106 determines, based upon the information, a predicted change in the physiologic parameter in response to administration of a second bolus to the patient.
• Atstage2118, theprediction engine106 or theintervention decision engine107 can provide a fluid administration recommendation through the user interface810 in response to the predicted change.
• Attention is now directed toFIG. 22 which is a flowchart depicting anotherexemplary process2200 for providing patient-adaptive hemodynamic management in accordance with the disclosure. Theprocess2200 can be performed by the patient-adaptivehemodynamic management system10 illustrated inFIGS. 1A and 1B including thecontrol device100 illustrated inFIGS. 2-8. Theprocess2200 is exemplary only and not limiting. Theprocess2200 can be altered, e.g., by having stages added, removed, rearranged, combined and/or performed concurrently.
• Atstage2210, thevitals manager component101 receives input information relating to one or more physiologic processes of a patient. The input information can be received from one of theclinical monitors115 or from the user interface810, for example. Upon receiving the input information, thevitals manager component101 stores information indicative of the input information in a bolus log entry of the bolus log table202.
• Atstage2210, one or more of thelog predictor component104, thehistory analysis component105 or theprediction engine106 determines, based at least in part upon the input information, a subgroup of bolus log entries associated with a current state of the patient.
• Atstage2214, theprediction engine106 or theintervention decision engine107 can provide a fluid administration recommendation based at least in part upon log data included within the subgroup of bolus log entries. The fluid administration recommendation can be provided to the user interface810 to prompt a user for acceptance or rejection of the recommendation. Alternatively, the fluid administration recommendation can be converted to a signal to cause the fluidpump manager component108 to administer the recommendation automatically.
• Attention is now directed toFIG. 23 which is a flowchart depicting yet anotherexemplary process2300 for providing patient-adaptive hemodynamic management in accordance with the disclosure. Theprocess2300 can be performed by the patient-adaptivehemodynamic management system10 illustrated inFIGS. 1A and 1B including thecontrol device100 illustrated inFIGS. 2-8. Theprocess2300 is exemplary only and not limiting. Theprocess2300 can be altered, e.g., by having stages added, removed, rearranged, combined and/or performed concurrently.
• Atstage2314, thevitals manager component101 or the fluidbolus log component103 determines a first effect on a physiologic parameter of the patient associated with administration of a first fluid bolus to the patient.
• Atstage2318, the fluidbolus log component103 stores first information relating to the first effect in a first bolus log entry400. Atstage2322, thevitals manager component101 or the fluidbolus log component103 determines a second effect on a physiologic parameter of the patient associated with administration of a second fluid bolus to the patient.
• Atstage2326, the fluidbolus log component103 stores second information relating to the second effect in a second bolus log entry400.
• Atstage2330, one or more of the population basedpredictor component102, thelog predictor component104, thehistory analysis component105, theintervention decision engine107 or theprediction engine106 provides, a fluid administration recommendation based at least in part upon at least one of the first bolus log entry and the second bolus log entry using the methods discussed above. The recommendation can be provided to the user interface810 to prompt a user for acceptance or rejection of the recommendation. Alternatively, the fluid administration recommendation can be converted to a signal to cause the fluidpump manager component108 to administer the recommendation automatically.
• Attention is now directed toFIG. 24 which is a flowchart depicting anotherexemplary process2400 for providing patient-adaptive hemodynamic management including a clinical intervention in accordance with the disclosure. Theprocess2400 can be performed by the patient-adaptivehemodynamic management system10 illustrated inFIGS. 1A and 1B including thecontrol device100 illustrated inFIGS. 2-8. Theprocess2400 is exemplary only and not limiting. Theprocess2400 can be altered, e.g., by having stages added, removed, rearranged, combined and/or performed concurrently.
• Atstage2410, thelog predictor component104 receives bolus log information, e.g., from the fluidbolus log component103, relating to one or more effects on a state of the patient associated with prior administration of fluid to the patient. Atstage2414, thelog predictor component104 determines, based upon the bolus log information, a predicted change in a physiologic parameter of the patient in response to the administration of a fluid bolus to the patient.
• Atdecision block2418, theintervention decision component107 determines if clinical intervention is required. The decision can be made based on the physiologic parameter lies in one of a plurality of range of values such as the one of theranges701,702,703 or704 discussed above in reference toFIG. 7. If it is determined that clinical intervention is not required, theprocess2400 continues to stage2430 where theintervention decision component107 and thepump manager component108 communicate with each other to generate a fluid administration signal based upon the predicted change.
• If, atstage2418, it is determined that clinical intervention is required, theprocess2400 continues atstage2422 where theintervention decision component107 provides a fluid administration recommendation through the user interface810 based upon the predicted change. The fluid administration signal can recommend to a user what fluid administration is recommended.
• Atstage2426, theintervention decision component107 or the user interface810 receives user input relating to the recommendation through the user interface810. After receiving the user input, the process continues atstage2430 where theintervention decision component107 or the user interface810 generates a fluid administration signal to thepump manager component108 to affect the fluid administration to the patient based upon the predicted change and further based upon the user input.
• Attention is now directed toFIG. 25 which is a flowchart depicting anotherexemplary process2500 of providing patient-adaptive hemodynamic management including a clinical intervention in accordance with the disclosure. Theprocess2500 can be performed by the patient-adaptivehemodynamic management system10 illustrated inFIGS. 1A and 1B including thecontrol device100 illustrated inFIGS. 2-8. Theprocess2500 is exemplary only and not limiting. Theprocess2500 can be altered, e.g., by having stages added, removed, rearranged, combined and/or performed concurrently.
• Atstage2510, the population basedpredictor component102 receives information relating to an expected change in a physiologic parameter of patients in a patient population in response to infusion of a fluid bolus in the patients. Atstage2414, the population basedpredictor component102 determines, based at least in part upon the information using the methods discussed above, a predicted change in the physiologic parameter of the patient in response to administration of the fluid bolus to the patient.
• Atdecision block2518, theintervention decision component107 determines if clinical intervention is required. The decision can be made based on the physiologic parameter lies in one of a plurality of range of values such as the one of theranges701,702,703 or704 discussed above in reference toFIG. 7. If it is determined that clinical intervention is not required, theprocess2500 continues to stage2530 where theintervention decision component107 and thepump manager component108 communicate with each other to generate a fluid administration signal based upon the predicted change.
• If, atstage2518, it is determined that clinical intervention is required, theprocess2500 continues atstage2522 where theintervention decision component107 provides a fluid administration recommendation through the user interface810 based upon the predicted change. The fluid administration signal can recommend to a user what fluid administration is recommended.
• Atstage2526, theintervention decision component107 or the user interface810 receives user input relating to the recommendation through the user interface810. After receiving the user input, the process continues atstage2530 where theintervention decision component107 or the user interface810 generates a fluid administration signal to thepump manager component108 to affect the fluid administration to the patient based upon the predicted change and further based upon the user input.
• Attention is now directed toFIG. 26 which is a flowchart depicting yet anotherexemplary process2600 of providing patient-adaptive hemodynamic management including a clinical intervention in accordance with the disclosure. Theprocess2600 can be performed by the patient-adaptivehemodynamic management system10 illustrated inFIGS. 1A and 1B including thecontrol device100 illustrated inFIGS. 2-8. Theprocess2600 is exemplary only and not limiting. Theprocess2600 can be altered, e.g., by having stages added, removed, rearranged, combined and/or performed concurrently.
• Atstage2610, the fluidbolus log component103 receives bolus log information relating to one or more effects on a state of the patient associated with prior administration of fluid to the patient. Atstage2614, thelog predictor component104 determines, based upon the bolus log information and using the methods discussed above, a predicted change in a physiologic parameter of the patient in response to the administration of a fluid bolus to the patient.
• Atdecision block2618, theintervention decision component107 determines if clinical intervention is required. The decision can be made based on the physiologic parameter lies in one of a plurality of range of values such as the one of theranges701,702,703 or704 discussed above in reference toFIG. 7. If it is determined that clinical intervention is not required, theprocess2600 continues to stage2630 where theintervention decision component107 and thepump manager component108 communicate with each other to generate a fluid administration signal based upon the predicted change.
• If, atstage2618, it is determined that clinical intervention is required, theprocess2600 continues atstage2622 where theintervention decision component107 provides a fluid administration recommendation through the user interface810 based upon the predicted change. The fluid administration signal can recommend to a user what fluid administration is recommended.
• Atstage2626, theintervention decision component107 or the user interface810 receives user input relating to the recommendation through the user interface810. After receiving the user input, the process continues atstage2630 where theintervention decision component107 or the user interface810 generates a fluid administration signal to thepump manager component108 to affect the fluid administration to the patient based upon the predicted change and further based upon the user input.
• A display of a possible user interface could comprise a standard “Starling Curve” of ventricular function, a graphical indicator of where the patient is perceived to be presently along that curve, and a band showing the ideal range of the curve for the patient. In addition, minimum and maximum curves can be displayed showing the observed ranges of cardiac function in a given patient (not shown).
• FIG. 27 is a summary of a table of results from initial studies using the methodology of thecontrol device100 in simulations. The performance of thecontrol device100 was compared to the performance of anesthesiologists across a number of dimensions. The table inFIG. 27 is presented as evidence of the effectiveness of the methodology of thecontrol device100 in the studies. Closed-loop management of fluid resuscitation has historically been difficult. The table inFIG. 27 is representative of simulation data for a closed-loop fluid-management algorithm using pulse pressure variation (PPV) as the input variable.
• Using a simulator which includes physiologic PPV output, twenty practicing anesthesiology residents and faculty were asked to manage fluids and pressors for a one-hour simulated hemorrhage case of 2 L blood loss over 20 minutes (group1). One week later, they repeated the simulation, but this time fluids were secretly managed by the closed-loop system while practitioner fluid administrations were ignored and only the pressors were entered (group2). The simulation was also run twenty times with only the closed-loop (group3) and twenty times with no management (group4). As illustrated by the data included inFIG. 27, conditions across all groups were similar at baseline for simulated patient weight, height, heart rate (HR), mean arterial pressure (MAP), and cardiac output (CO). Once the hemorrhage began, the closed loop groups (2&3) intervened significantly earlier than the practitioners (group1) and gave more fluid. The mean and final CO was higher in both closed-loop groups than in the practitioners group, and the coefficient of variance was lower. There was no difference in MAP between intervention groups, but all were significantly higher than the unmanaged group. In conclusion, the data demonstrate that closed-loop management of fluid resuscitation is feasible using a dynamic parameter based algorithm and that this approach can be used to optimize cardiac output.
• In one or more exemplary embodiments, the functions, methods and processes described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or encoded as one or more instructions or code on a computer-readable medium. Computer-readable media includes computer storage media. Storage media may be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.
• As used herein, computer program products comprising computer-readable media including all forms of computer-readable medium except, to the extent that such media is deemed to be non-statutory, transitory propagating signals.
• It is understood that the specific order or hierarchy of steps or stages in the processes and methods disclosed are examples of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged while remaining within the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not meant to be limited to the specific order or hierarchy presented.
• Those of skill in the art would understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.
• Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
• The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
• The steps or stages of a method, process or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.
• The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
• The disclosure is not intended to be limited to the aspects shown herein, but is to be accorded the full scope consistent with the specification and drawings, wherein reference to an element in the singular is not intended to mean “one and only one” unless specifically so stated, but rather “one or more.” Unless specifically stated otherwise, the term “some” refers to one or more. A phrase referring to “at least one of” a list of items refers to any combination of those items, including single members. As an example, “at least one of: a, b, or c” is intended to cover: a; b; c; a and b; a and c; b and c; and a, b and c.
• The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein. It is intended that the following claims and their equivalents define the scope of the disclosure.

Claims (80)

1. A computer-implemented method for facilitating the administration of fluid to a patient, the method comprising:

receiving input information relating to one or more physiologic processes of the patient;

determining, using a processor, a predicted change in a physiologic parameter of the patient in response to administration of a fluid bolus to the patient wherein the predicted change is based at least in part on the input information; and

generating, using the processor, a fluid administration signal in response to the predicted change.

2. The method ofclaim 1 wherein the physiologic parameter comprises a cardiac output of the patient.

3. The method ofclaim 1 wherein the physiologic parameter comprises a stroke volume of the patient.

4. The method ofclaim 1 wherein the one or more physiologic processes include at least one of heart rate, cardiac stroke volume, blood pressure and arterial pressure.

5. The method ofclaim 1 wherein the predicted change is further based upon information relating to an expected change in the physiologic parameter in a patient population in response to infusion of the fluid bolus in patients included in the patient population.

6. The method ofclaim 1 wherein the predicting is based at least in part upon results of prior administration of a different fluid bolus to the patient.

7. The method ofclaim 1 wherein the input information comprises measurement values, the method further including filtering the measurement values.

8. The method ofclaim 7 wherein the filtering includes comparing ones of the measurement values to other of the measurement values.

9. The method ofclaim 7 further including identifying artifacts among the measurement values by determining, based upon the measurement values, relative changes in one or more physiologic parameters of the patient.

10. The method ofclaim 5 further including:

receiving information relating to a medical condition of the patient; and

determining the patient population based at least upon the medical condition.

11. The method ofclaim 10 wherein the medical condition is included within state information associated with the patient, the determining the patient population further including selecting the patient population from among plural patient populations based at least upon the state information.

12. The method ofclaim 11 further including determining the expected change in the physiologic parameter based upon information included within a population reference table associated with the patient population.

13. The method ofclaim 1 further including creating a bolus log entry corresponding to the administration of the fluid bolus to the patient, the bolus log entry including an indication of an effect of the fluid bolus on the physiologic parameter of the patient.

14. The method ofclaim 1 further including:

creating, in a bolus log, a bolus log entry corresponding to the administration of the fluid bolus to the patient; and

determining an effect of the administration of the fluid bolus on a hemodynamic state of the patient, the bolus log entry including log information relating to the effect.

15. The method ofclaim 14 wherein the determining the predicted change is further based upon other log information obtained from the bolus log, wherein the other log information is associated with prior administration of fluid to the patient.

16. The method ofclaim 1 further including:

generating a bolus log containing log information relating to one or more effects on a hemodynamic state of the patient associated with prior administration of fluid to the patient; and

determining, based upon the log information, an expected change in the physiologic parameter in response to the administration of the fluid bolus to the patient, the predicted change being based at least in part upon the expected change.

17. The method ofclaim 1 wherein the determining the predicted change further includes:

determining a first expected change in the physiologic parameter based upon bolus log information relating to one or more effects of prior administration of fluid to the patient on a condition of the patient; and

determining a second expected change in the physiologic parameter associated with infusion of the fluid bolus in a patient population in response.

18. The method ofclaim 17 wherein the determining the predicted change further includes relatively weighting the first expected change and the second expected change in accordance with error values respectively associated with the first expected change and the second expected change.

19. The method ofclaim 1 wherein the predicted change in the physiologic parameter comprises at least one of a reduction or a lack of change in a cardiac output of the patient and wherein the fluid administration signal provides an indication that an ongoing fluid administration to the patient should be terminated.

20. The method ofclaim 1 wherein the predicted change in the physiologic parameter is indeterminate and wherein the fluid administration signal provides an indication that an ongoing fluid administration to the patient should be maintained.

21. The method ofclaim 1 wherein the predicted change in the physiologic parameter comprises an improvement and wherein the fluid administration signal provides an indication that the fluid bolus should be administered to the patient.

22. The method ofclaim 1 further including automatically generating a pump control signal in response to the fluid administration signal, the pump control signal being provided to at least one fluid pump.

23. The method ofclaim 1 further including:

generating, on a user interface, an indication of an intervention command corresponding to the fluid administration signal;

receiving user input corresponding to acceptance of the intervention command; and

generating, in response to the user input, a pump control signal wherein the pump control signal is provided to at least one fluid pump.

24. The method ofclaim 22 further including:

receiving pump status information indicative of a status of the at least one fluid pump; and

determining whether the pump status information is consistent with an expected status related to the fluid administration signal.

25. The method ofclaim 1 further including providing the fluid administration signal to a user interface configured to display one or more recommendations for care of the patient in response to the fluid administration signal.

26. The method ofclaim 1 wherein the input information comprises measurements of the one or more physiologic processes.

27. The method ofclaim 1 wherein the input information relates to variation in at least one of the one or more physiologic processes.

28. The method ofclaim 1 wherein the input information comprises at least one of pulse pressure variation, stroke volume variation, plethysmograph waveform variability, and EKG waveform variability.

29. A computer-implemented method for facilitating control of fluid administration; the method comprising:

receiving information relating to an initial change in a physiologic parameter of a patient in response to administration of a first fluid bolus to a patient;

determining, using a processor and based at least in part upon the information, a predicted change in the physiologic parameter in response to an administration of a second bolus; and

adjusting administration of fluid to the patient based upon the predicted change.

30. The method ofclaim 29 wherein the determining is further based upon input information relating to one or more physiologic processes of the patient.

31. The method ofclaim 29 further including determining, using the processor, the initial change based upon measured values of one or more physiologic processes of the patient.

32. The method ofclaim 29 wherein the physiologic parameter comprises a cardiac output of the patient.

33. The method ofclaim 29 wherein the physiologic parameter comprises a stroke volume of the patient.

34. The method ofclaim 30 wherein the one or more physiologic processes include at least one of heart rate, cardiac stroke volume, blood pressure and arterial pressure.

35. The method ofclaim 30 wherein the predicted change is further based upon information relating to an expected change in the physiologic parameter in a patient population in response to infusion of a fluid bolus in patients included in the patient population.

36. The method ofclaim 30 wherein the input information comprises measurements of the one or more physiologic processes.

37. The method ofclaim 30 wherein the input information relates to variation in at least one of the one or more physiologic processes.

38. The method ofclaim 30 wherein the input information comprises at least one of pulse pressure variation, stroke volume variation, plethysmograph waveform variability, and EKG waveform variability.

39. A computer-implemented method for facilitating the administration of fluid to a patient, the method comprising:

receiving, from a monitoring device, an indication of at least one of oxygen delivery to tissues of the patient and oxygen utilization by the patient;

selecting, using a processor and based upon the indication, an oxygen-carrying fluid from among multiple fluids capable of being infused into the patient; and

signaling, using the processor, selection of the oxygen-carrying fluid for infusion into the patient.

40. The method ofclaim 39 further including:

determining, using the processor, a predicted change in a physiologic parameter of the patient in response to administration of a fluid bolus to the patient, and

signaling, using the processor, an infusion pump device to infuse another of the multiple fluids into the patient in response to the predicted change.

41. A computer-implemented method for facilitating the administration of fluid to a patient, the method comprising:

receiving input information relating to one or more physiologic processes of the patient;

determining, using a processor and based on the input information, that a change has occurred in a physiologic parameter of the patient; and

generating, using the processor, a fluid administration signal based at least in part on the change in the physiologic parameter.

42. The method ofclaim 41 further including:

determining that the change exceeds a predefined amount; and

infusing a test bolus of fluid into the patient in response to the fluid administration signal.

43. The method ofclaim 41 wherein the physiologic parameter comprises a cardiac output of the patient.

44. The method ofclaim 41 wherein the physiologic parameter comprises a stroke volume of the patient.

45. The method ofclaim 41 wherein the one or more physiologic processes include at least one of heart rate, cardiac stroke volume, blood pressure and arterial pressure.

46. The method ofclaim 41 wherein the input information comprises measurements of the one or more physiologic processes.

47. The method ofclaim 41 wherein the input information relates to variation in at least one of the one or more physiologic processes.

48. The method ofclaim 41 wherein the input information comprises at least one of pulse pressure variation, stroke volume variation, plethysmograph waveform variability, and EKG waveform variability.

49. The method ofclaim 43 wherein the determining includes determining that the cardiac output has decreased by at least a predefined percentage.

50. The method ofclaim 44 wherein the determining includes determining that the stroke volume has decreased by at least a predefined percentage.

51. The method ofclaim 41 further including:

determining, using the processor, a predicted change in the physiologic parameter of the patient in response to administration of a fluid bolus to the patient wherein the predicted change is based at least in part on the input information;

wherein the generating the fluid administration signal is further based at least in part upon the predicted change.

52. A computer-implemented method for facilitating the administration of fluid to a patient, the method comprising:

determining, using a processor, a predicted change in a physiologic parameter of the patient in response to administration of a fluid bolus to the patient;

determining, using a processor, that an actual change has occurred in the physiologic parameter of the patient; and

generating, using the processor, a fluid administration signal based upon at least one of the predicted change and the actual change.

53. The method ofclaim 52 further including:

determining that the actual change exceeds a predefined amount; and

infusing a test bolus of fluid into the patient in response to the fluid administration signal.

54. A computer-implemented method for providing clinical decision support relating to the administration of fluid to a patient, the method comprising:

receiving input information relating to one or more physiologic processes of the patient;

determining, using a processor, a predicted change in a physiologic parameter of the patient in response to administration of a fluid bolus to the patient wherein the predicted change is based at least in part on the input information; and

providing, using the processor and in response to the predicted change, a fluid administration recommendation.

55. The method ofclaim 54 wherein the physiologic parameter comprises a cardiac output of the patient.

56. The method ofclaim 54 wherein the physiologic parameter comprises a stroke volume of the patient.

57. The method ofclaim 54 wherein the one or more physiologic processes include at least one of heart rate, cardiac stroke volume, blood pressure and arterial pressure.

58. The method ofclaim 54 wherein the predicted change is further based upon information relating to an expected change in the physiologic parameter in a patient population in response to infusion of the fluid bolus in patients included in the patient population.

59. The method ofclaim 54 wherein the predicting is based at least in part upon results of prior administration of a different fluid bolus to the patient.

60. The method ofclaim 59 further including filtering the measurement values.

61. The method ofclaim 60 wherein the filtering includes comparing ones of the measurement values to other of the measurement values.

62. The method ofclaim 54 further including identifying artifacts among the measurement values by determining, based upon the measurement values, relative changes in one or more physiologic parameters of the patient.

63. The method ofclaim 58 further including:

receiving information relating to a medical condition of the patient; and

determining the patient population based at least upon the medical condition.

64. The method ofclaim 63 wherein the medical condition is included within state information associated with the patient, the determining the patient population further including selecting the patient population from among plural patient populations based at least upon the state information.

65. The method ofclaim 58 further including determining the expected change in the physiologic parameter based upon information included within a population reference table associated with the patient population.

66. The method ofclaim 54 further including creating a bolus log entry corresponding to the administration of the fluid bolus to the patient, the bolus log entry including an indication of an effect of the fluid bolus on the physiologic parameter of the patient.

67. The method ofclaim 54 further including:

creating, in a bolus log, a bolus log entry corresponding to the administration of the fluid bolus to the patient; and

determining an effect of the administration of the fluid bolus on a hemodynamic state of the patient, the bolus log entry including log information relating to the effect.

68. The method ofclaim 67 wherein the determining the predicted change is further based upon other log information obtained from the bolus log, wherein the other log information is associated with prior administration of fluid to the patient.

69. The method ofclaim 54 further including:

generating a bolus log containing log information relating to one or more effects on a hemodynamic state of the patient associated with prior administration of fluid to the patient; and

determining, based upon the log information, an expected change in the physiologic parameter in response to the administration of the fluid bolus to the patient, the predicted change being based at least in part upon the expected change.

70. The method ofclaim 54 wherein the determining the predicted change further includes:

determining a first expected change in the physiologic parameter based upon bolus log information relating to one or more effects of prior administration of fluid to the patient on a condition of the patient; and

determining a second expected change in the physiologic parameter associated with infusion of the fluid bolus in a patient population in response.

71. The method ofclaim 70 wherein the determining the predicted change further includes relatively weighting the first expected change and the second expected change in accordance with error values respectively associated with the first expected change and the second expected change.

72. The method ofclaim 54 wherein the predicted change in the physiologic parameter comprises at least one of a reduction or a lack of change in a cardiac output of the patient and wherein the recommendation indicates that an ongoing fluid administration to the patient should be terminated.

73. The method ofclaim 54 wherein the predicted change in the physiologic parameter is indeterminate and wherein the recommendation indicates that an ongoing fluid administration to the patient should be maintained.

74. The method ofclaim 54 wherein the predicted change in the physiologic parameter comprises an improvement and wherein the recommendation indicates that the fluid bolus should be administered to the patient.

75. The method ofclaim 54 further including displaying information relating to the recommendation on a user interface.

76. The method ofclaim 75 further including receiving user input relating to the recommendation through the user interface.

77. The method ofclaim 54 wherein the input information comprises measurements of the one or more physiologic processes.

78. The method ofclaim 54 wherein the input information relates to variation in at least one of the one or more physiologic processes.

79. The method ofclaim 54 wherein the input information comprises at least one of pulse pressure variation, stroke volume variation, plethysmograph waveform variability, and EKG waveform variability.

80. A computer-implemented method for providing clinical decision support relating to the administration of fluid to a patient, the method comprising:

receiving information relating to an initial change in a physiologic parameter of a patient in response to administration of a first fluid bolus to a patient;

determining, using a processor and based at least in part upon the information, a predicted change in the physiologic parameter in response to an administration of a second bolus; and

providing, using the processor and in response to the predicted change, a fluid administration recommendation.

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