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WO2012006174A2 - Procédés, systèmes et supports lisibles par ordinateur pour l'évaluation du risque de mortalité d'un patient hospitalisé - Google Patents

Procédés, systèmes et supports lisibles par ordinateur pour l'évaluation du risque de mortalité d'un patient hospitalisé Download PDF

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Publication number
WO2012006174A2
WO2012006174A2 PCT/US2011/042416 US2011042416W WO2012006174A2 WO 2012006174 A2 WO2012006174 A2 WO 2012006174A2 US 2011042416 W US2011042416 W US 2011042416W WO 2012006174 A2 WO2012006174 A2 WO 2012006174A2
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patient
data
mortality
risk
physiologic
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WO2012006174A3 (fr
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Keith Christopher Kocis
Daniel Joseph Kocis
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University of North Carolina at Chapel Hill
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University of North Carolina at Chapel Hill
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders

Definitions

  • the subject matter described herein relates to analyzing data regarding hospital patients. More particularly, the subject matter described herein relates to methods, systems, and computer readable media for evaluating a hospital patient's risk of mortality.
  • PICU pediatric intensive care unit
  • NICU neonatal intensive care unit
  • non-ICU non-intensive care
  • Rapid Response (RR) teams have been established to rescue non-ICU pediatric patients who are decompensating and in need of critical care evaluation. These teams are triggered by any health care provider (nurse, respiratory therapist, physician, etc.) or family members resulting in a stat evaluation by an expert team of health care providers that can initiate lifesaving support and transfer patients to an ICU. These teams have been shown to be effective at saving the lives of both pediatric and adult hospitalized inpatients.
  • the subject matter described herein can be implemented in software in combination with hardware and/or firmware.
  • the subject matter described herein can be implemented in software executed by a processor.
  • the subject matter described herein can be implemented using a non-transitory computer readable medium having stored thereon computer executable instructions that when executed by the processor of a computer control the computer to perform steps.
  • Exemplary computer readable media suitable for implementing the subject matter described herein include non-transitory computer-readable media, such as disk memory devices, chip memory devices, programmable logic devices, and application specific integrated circuits.
  • a computer readable medium that implements the subject matter described herein may be located on a single device or computing platform or may be distributed across multiple devices or computing platforms.
  • Figure 2 is a block diagram illustrating exemplary components of a system for evaluating a hospital patient's risk of mortality according to an embodiment of the subject matter described herein;
  • Figure 3 is a flow chart illustrating exemplary steps for evaluating a hospital patient's risk of mortality according to an embodiment of the subject matter described herein;
  • Figures 4A and 4B are graphs of ECG voltage versus time that may be analyzed by the system illustrated in Figure 2 to evaluate a patient's risk of mortality according to an embodiment of the subject matter described herein;
  • Figures 5A and 5B are tables respectively illustrating statistical measures for variables for live and dead PICU patients that may be used by the system illustrated in Figure 2 to evaluate a given patient's risk of mortality according to an embodiment of the subject matter described herein;
  • Figures 8A and 8B are tables illustrating exemplary statistical measures for different variables for dead PICU and live PICU patients according to an embodiment of the subject matter described herein;
  • Figures 9A and 9B are tables illustrating exemplary measures of patient physiological data that may be obtained and used by the system illustrated in Figure 2 to evaluate a given patient's risk of mortality according to an embodiment of the subject matter described herein;
  • Figures 10A and 10B illustrate variables, intervals, and statistical measures that may be used by the system illustrated in Figure 2 to evaluate a hospital patient's risk of mortality according to an embodiment of the subject matter described herein;
  • Figures 1 A and 11 B illustrate intervals that may be used by the system in Figure 2 to evaluate a patient's risk of mortality according to an embodiment of the subject matter described herein;
  • Figure 12A is a table
  • Figure 12B is a graph illustrating critical intervals for live and dead patients for a particular variable that may be used by the system illustrated in Figure 2 to evaluate a patient's risk of mortality according to an embodiment of the subject matter described herein
  • Figures 13A and 13B are plots of sodium frequency versus time for a given patient according to an embodiment of the subject matter described herein;
  • Figures 14A-14C are tables illustrating counts of multi-feed transformations on expanded PICU data where derivatives and second derivatives are computed to determine measures of variability of the variable being analyzed;
  • Figure 15A is a table
  • Figure 15B is a graph illustrating critical intervals of a particular variable that may be used by the system illustrated in Figure 2 to evaluate a patient's risk of mortality according to an embodiment of the subject matter described herein;
  • Figure 16B is a graph of extrapolated data in 2 minute increments which calibrates all data into exact 2minute intervals.
  • Figure 17A is a graph of patient event counts per organ and Figure 17B is a table of the same data that may be generated by the system illustrated in Figure 2 according to an embodiment of the subject matter described herein;
  • Figure 17C includes graphs tracking each two minute score along with organ specific critical interval counts that may be generated by the system illustrated in Figure 2 according to an embodiment of the subject matter described herein;
  • Figure 18A is a partial predictive model markup language (PMML) listing of a model for evaluating risk of mortality for a PICU patient according to an embodiment of the subject matter described herein;
  • PMML predictive model markup language
  • Figure 21 is a graph of the covariance between the QRS template illustrated in Figure 20 and the contaminated ECG signal illustrated in Figure 19;
  • Figure 22 is a graph of the output of a quadrature-mirrored, second order linear infinite impulse response filter use to remove baseline shifts in a QRS wave according to an embodiment of the subject matter described herein;
  • Figure 23 is a graph of the R wave determined using a 2 sigma statistical filter according to an embodiment of the subject matter described herein; and Figure 24 is a graph of the output of a threshold and first derivative filter, which represents the ECG signal with contamination removed and after post- processing according to an embodiment of the subject matter described herein.
  • REALTROMINS is a medical device for critically ill patients that integrates and analyzes numerous inputs: 1) real time continuous physiologic signals (e.g. electrocardiogram); 2) advanced measures of variability of these signals (e.g. spectral analysis); 3) physiologic based measures of organ function (e.g. serum glucose) and their variability; and 4) demographic and diagnosis related predictors of mortality; creating a real time, continuously updated risk of mortality score to aid clinicians in administering medical care.
  • real time continuous physiologic signals e.g. electrocardiogram
  • advanced measures of variability of these signals e.g. spectral analysis
  • 3) physiologic based measures of organ function e.g. serum glucose
  • REALTROMINS devices and methods for use in patient populations comprising premature infants in a neonatal intensive care unit (NICU; Neonatal REALTROMINS) and hospitalized children outside the ICU but in a non-ICU pediatric care facility and/or under the care and supervision of a Rapid Response (RR) team (Rapid Response REALTROMINS).
  • NICU neonatal intensive care unit
  • RR Rapid Response
  • REALTROMINS devices and methods for use in the PICU The REALTROMINS devices and methods can guide medical decision making resulting in earlier initiation or withdrawal of costly and risky therapeutic interventions to improve patient outcomes.
  • the presently disclosed subject matter can decrease healthcare costs by better matching expensive and limited: 1) human resources (physician, nursing, etc.) and 2) hospital facilities (intensive care beds) to the changing needs of critically ill and hospitalized patients.
  • REALTROMINS is a bioinformatics technology designed to continuously assess the changing risk of mortality of critically ill pediatric patients and track the success or failure of ongoing medical interventions. Described herein are REALTROMINS technologies to be utilized in treating patients in PICU, NICU and non-ICU pediatric settings. Bedside caregivers can determine which therapies are leading to a decrease in risk while an increased mortality risk would trigger new approaches to the clinical problem. Patient outcomes can improve by displaying the overall risk score and the individual components contributing to that risk. By way of example and not limitation, heart rate variability has been shown to be highly predictive of mortality in a wide variety of critical illnesses in both adults and children.
  • the male to female ratio is 1.27:1 and a diverse racial background is represented.
  • the racial and ethnic breakdowns are as follows: 48% white, 31% black and 21% Hispanic and other races.
  • Patient Enrollment Criteria For the estimation (50% of patients) and validation (50% of patients) data sets, all patients admitted to the PICU, NICU, and non-ICU beds were enrolled over 9 months and comparison between survivors and non-survivors was examined. Since few patients died in the non-ICU beds additional outcome measures, such as transfer to the PICU, triggering a code blue (cardiopulmonary arrest) or Rapid Response team dispatch, were be examined.
  • the electrocardiogram (ECG) was digitized and a java based algorithm was used to detect the fiducial point (R wave of the ECG) from which the RR interval (time between R peaks), heart period, and heart rate (HR) time series were derived.
  • the front end loader manages data capture. This component consists of the open source Mirth HL-7 engine for capture of all HL-7 data feeds and a custom Java application that captures real-time ICU monitor data every 30 seconds (configurable) from the vendor's transactional database via a JDBC database connection. At the point of capture, the UTC time stamp, patient location, and patient IDs are standardized and data is then forwarded to the messaging system.
  • a second Java application monitors the RTPS to detect patients that have been discharged from a location. Once detected, all data on that patient is extracted from the RTPS and moved to the data archival system. As part of this process, the patient data is converted from the custom time series representation to a format amendable to model development.
  • Physiologic based indices of organ function were obtained at an hourly frequency (i.e. urine output) or intermittently (i.e. serum glucose) according to patient care needs.
  • Physiologic based measures of organ function included: 1 ) cardiac indicia, comprising non-invasive blood pressure (hourly systolic and diastolic), lactate level; 2) respiratory indicia, comprising respiratory rate, pH, PCO 2 , P0 2 , HCO 3 , 0 2 saturation, fractional inspired oxygen concentration (hourly); 3) neurologic indicia, comprising Glasgow coma score (hourly), pupillary reactions (hourly); 4) fluid indicia, comprising sodium, potassium, glucose, urea nitrogen, creatinine concentrations, ionized calcium, whole blood glucose measurements, urine output (hourly), hourly fluids administered (IV and/or enteral), weight on admission; 5) hematologic indicia, comprising hemoglobin, platelet count, prothrombin time-INR,
  • Demographic and diagnosis related predictors 1) age; 2) general diagnostic groups (e.g., oncologic disease); 3) pre ICU risk factors (e.g., cardiopulmonary resuscitation); and 4) other (e.g., cardiac pacing) were recorded.
  • ICU risk factors e.g., cardiopulmonary resuscitation
  • other e.g., cardiac pacing
  • Quantitative Target Modeling System (QTMS V3- Applied Multivariate Algorithms, Manorville, NY) are proprietary SAS macros used for transforming raw information into optimized "bins" or intervals to be used with SAS EMS.2 neural network model building suite.
  • Raw physiologic information was transformed creating composite variables scanned and summarized by QTMS. This calculated a variety of statistics for each interval found within each variable including lift from base, t-tests, chi-square analysis, indices, missing data, and confidence intervals. Significant intervals from each variable were selected depending on a specified lift from base criteria. Calculating all available model building techniques, the RULE INDUCTION approach delivered the best set of specificity and sensitivity rates within the model comparison wizard.
  • Neonatal REALTROMINS and Rapid Response REALTROMINS were created by streaming and analyzing data from the NICU and non ICU beds, respectively, into a REALTROMINS algorithm.
  • PICU Admission Data The first four hours of data from the 28 matched patients were partitioned out for analyses and outcome (DIED vs. SURVIVE) recorded. This resulted in an analytic data set of 3360 records (1680 SURVIVE packets and 1680 DIED packets). Each patient thus produced 120 records for the first four hours at two minute intervals.
  • the training data set utilized 80% of the data packets with a random 20% hold out sample of packets used to validate. The predictive capabilities of the model are demonstrated below. Identical results were found in the validation data set. Actual Patient Outcome
  • Survivor Validation Dataset An additional 16 survivors were then analyzed during their PICU LOS representing about 25,000 REALTROMINS Scores with LOS from 0.23 to 6.36 days with 35.4 cumulative patient days. The predictive capabilities of the model were nearly identical to the PICU LOS data above.
  • FIG. 2 is a block diagram illustrating an exemplary system for evaluating a hospital patent's risk of mortality according to an embodiment of the subject matter described herein.
  • the system includes a data collection module 200 that receives inputs, referred to a feeds in Figure 2. These inputs may be physiologic signals generated by patient monitors, physiologic signals of organ function, and demographic information for a patient.
  • Data collection module 200 may separate the data into time stamped packets. For example, where the data comprises signals output from an electrocardiogram, each data packet may include a time stamp and a peak, representing a peak ECG voltage.
  • Messaging module 202 communicates the data packets to a real-time patient monitoring system 204.
  • Real-time patient monitoring system 204 includes transactional store 206 where high and low l eauiuiiui i ucua cu e oiui cu, pauci u n u y ⁇ ⁇ i i iOuUiS , v niCi i segregates the data packets into groups for individual patients and a signal processing module 210, which generates measures of variability of at least some of the physiologic signals.
  • signal processing module 210 may receive ECG signals and use a covariance function to remove corruption from the ECG signals.
  • Real-time patient monitoring system 204 may also include a scoring module 212 which generates a score that indicates a patient's risk of mortality or likelihood of survival.
  • scoring module 212 may determine whether a particular physiologic or demographic variable for a patient falls within a critical interval for variable that indicates that the value is predictive of mortality or likelihood of survival. Examples of critical intervals will be described below.
  • scoring module 212 may record the occurrence of an event for the patient. Scoring module 212 may count the number of events for a patient over a time period, such as over a day.
  • Model development module 216 and model validation module 218 may continuously update the variables used in to generate the patient's scores and may update the statistical models associated therewith to improve predictive accuracy.
  • a data archiving module 220 may archive data for patients for further model development or validation.
  • a system monitor 222 monitors the overall activity of the system illustrated in Figure 2.
  • step 304 it was determined whether a value for a particular physiologic or demographic variable falls within a critical interval that indicates that the value is predictive of mortality or likelihood of surviving.
  • scoring module 212 illustrated in Figure 2 may determine whether a measured or calculated value for a variable for a patient falls within a range, referred to as a critical interval, where the range indicates that the patient is likely to die or to survive.
  • step 306 each time the value of for a physiologic or demographic variable falls within a critical interval, the occurrence of an event is recorded for the patient.
  • scoring module 212 may record the occurrence of events of an individual patient or an individual organ in a patient.
  • step 308 the number of events for a patient that occur within a time period is counted.
  • REALTROMINS is a medical device that analyzes in real time 1) continuous ECG signals, 2) commonly analyzed serum lab tests, and 3) diagnosis and demographic related variables. These variables or input into advanced statistical algorithms where the values are compared to critical intervals to determine whether they indicate a patient is likely to die or to survive.
  • Time dimensioned transformations using this information included aggregating all raw data into a common time frequency as HRV information exist in both sub-seconds and within a set of 128 beats while lab tests are intermittent based upon physician requests.
  • First and second order derivatives, centered moving averages and standard deviations are calculated with the goal of data mining to construct a mathematical algorithm that captures viable representations of existing phenomena hidden within this database.
  • QTMS V4 - Optimized Binning a series of SAS Macros, critical intervals found with the total distribution of each variable were calculated. These bins are selected base upon their ability to lift the mortality rate from base. This is done to focus attention onto those specific ranges with significantly increased mortality rates and represent them as a binary inclusionary condition.
  • the following physiologic based measures of organ function may be used by the system illustrated in Figure 2 to evaluate a hospital patient's risk of mortality.
  • Fluids sodium, potassium, glucose, urea nitrogen, creatinine, ionized calcium, magnesium, phosphorus, whole blood glucose measurements, urine output, fluids administered (IV and/or enteral), weight
  • Hematologic hemoglobin, platelet count, prothrombin time-INR, partial thromboplastin time, D-dimers
  • Hepatic bilirubin, transaminases, albumin, total protein, amylase, lipase;
  • Cardiac heart rate, noninvasive blood pressure (systolic and diastolic)*, troponin, CK- MB, lactate level;
  • Neurologic Glasgow coma score, pain score
  • Immunologic temperature, white cell count, absolute neutrophil and lymphocyte count, c reactive protein.
  • the following demographic variables may be used by the system illustrated in Figure 2 to evaluate a hospital patient's risk of mortality.
  • Continuous ECG Data Streaming ECG Data was captured at 224 data points per second tagged with a UTC millisecond time stamp provided by the ICU monitoring system (SpaceLabs Medical). To create time-domain variables, the R peaks (fiducial point) had to be accurately identified. While the monitoring system has an analog process for estimating the location of R peaks, this process does not always identify the correct number or location of the R peak.
  • Figures 4A and 4B illustrate the raw ECG signal with peaks marked using SpaceLabs algorithms.
  • Spectral data was extracted by stripping on the fixed 64 records for each group of records.
  • Frequency bands were grouped as follows using the interpolated heart rate spectra values to calculate area within that specific band of frequencies. These frequencies corresponded to the ultra-low frequency (ULF), very low frequency (VLF), low frequency (LF), and high frequency (HF) bands.
  • ULF ultra-low frequency
  • VLF very low frequency
  • LF low frequency
  • HF high frequency
  • intervals 1 and 2 may be classified as critical intervals because they have mortality rates of 47.63 and 20.53%, respectively. While not accurately predictive of mortality alone, a variable that falls in intervals 1 and 2 can be considered in a model with other variables that can be highly predictive of a patient's risk of mortality. Exemplary models that combine variables will be set forth in detail videow.
  • Figures 8A and 8B illustrate transformable variables that may be used by the system illustrated in Figure 2 to predict a hospital patient's risk of mortality.
  • Physiologic based measures of organ function important in predicting mortality were defined by a battery of 96 laboratory and streaming physiological tests collected during the patients stay in the PICU. These tests were collected based upon clinical need which varied by patient and varied over time within patient. This was accomplished by looking within each variable's distributions and identifying intervals that index high against base- mortality using the optimized binning procedure found within QTMS V4.0 and previously described. This procedure was also performed on the eight additional transformations for each variable (centered moving averages, standard deviation on first and second derivatives).
  • Figures 9A and 9B illustrate tables that contain laboratory tests that are available for evaluating a hospital patient's risk of mortality.
  • FIGS. 10A and 10B are tables illustrating composite and transformation variables that may be used by the system illustrated in Figure 2 to evaluate a hospital patient's risk of mortality.
  • Figures 11 A and 11 B are tables illustrating critical intervals for the variables of Figures 10A and 10B that may be used to evaluate a hospital patient's risk of mortality.
  • Figures 14A - 14C illustrate event counts for different organs for different patients.
  • Figures 15A - 15D illustrate critical intervals that may be used by the system illustrated in Figure 2 for evaluating a patient's risk of mortality.
  • the critical intervals in Figures 15A - 15D may be determined using a single factor scan procedure found with QTMSV3.2 profiling for extreme derogatory ranges within the battery of lab tests.
  • Figure 17A is a graph and Figure 17B is a table illustrating exemplary scores that may be generated for a patient.
  • the scores may be presented on a per-organ and/or per-patient basis. Such scores may be emailed to a physician in graphical format or in tabular format to allow the physician to make a quick evaluation of the patient's risk of mortality.
  • Information visualization is to present ' significant and non-obvious patterns within these vast high-dimensional datasets and provide users with intuitive interface in their exploratory analysis. Many salient patterns may be only visible in subspaces of the data. Dimensionality reduction and clustering have become an effective approach to capture and organize these patterns, aid in the removal of irrelevant or redundant information, and enhances the comprehensibility of the whole dataset.
  • each patient is monitored continuously and a high- dimensional feature vector is derived for every 2-minute long interval.
  • a method to organize and summarize these feature vectors so that a user can navigate through them easily is desirable.
  • Given the pairwise relationships between feature vectors a representation that can efficiently organize them is needed. This includes the ability to group and summarize similar vectors and the ability to allow users to examine the relationship between feature vectors and/or groups of feature vectors at various resolutions seamlessly.
  • each factor represents a physiologic variable or combination of such variables.
  • the "0" column represents beta weights for Live NICU patients while the “1" column represents beta weights for the dead NICU patients.
  • PMML formatted scoring algorithms of the PICU and RR population Abbreviated examples can be found for PICU in Figure 18A and for RR in Figure 18B. Although different critical intervals were identified for each patient population (NICU, PICU and RR), all follow the same reporting outlined in Figures 17A- 17C. Complete versions of these models can be found in the above-referenced provisional patent application.
  • the predictive accuracy of the models can be improved by pre-processing the ECG signal using a covariance function to lessen the effects of corruption in the ECG signal.
  • Traditional methods of "R" wave detection of the ECG use a combination of signal conditioning, QRS detection and post-processing. QRS detection methods include amplitude threshold, first derivative threshold, moving average filters, high pass filters, cross correlation and others.
  • Our approach is to use the covariance function (formula 1) in conjunction with statistical filtering to detect the "R" wave.
  • covariance function is superior for QRS detection than standard cross-correlation or correlation coefficient function, since it removes baseline shifts and does not amplify other portions of the ECG other than the "R" wave.
  • Figure 19 is an ECG signal which has been corrupted: from second 2-4 with baseline shift due to motion artifact from deep breathing; from seconds 3-7 with moderate EMG artifacts due to a Valsalva maneuver; and from seconds 7-11 with intense pectoralis major muscle flexion.
  • the corruption can be removed. For example, when missing data is seen in ECG signals we "fill-in” using the correlation (covariance) with secondary sources (e.g., respiration) to estimate the missing ECG value. When high covariance(correlation) with secondary feeds exist, this approach has yielded significant value over a randomly assigned/blank values. In addition, when looking across multiple secondary feeds simultaneously we have reported more accurate estimates (fill-in value) then when just using single source.
  • FIR finite impulse response
  • IIR infinite impulse response
  • adaptive filtering cubic spline fitting
  • moving average filtering median filtering
  • weighted average filtering mathematical morphology and neural networks
  • the R wave was then distinguished using a 2-sigma statistical filter, follow -pass discriminatory filter, as shown in Figure 23.
  • the ECG signal in Figure 24 represents a section of a worst-case scenario test signal with a SNR of 0.5 (-6.11dB), which only occurs occasionally with poor sensor connectivity or during extreme patient activity (0.4 Hz - respiration, 3 Hz cardioballistic, 60 Hz - power line, 0-155Hz, white noise). Applying this detection procedure, we are able to correctly detect the "R" wave on this ECG test signal at 92% sensitivity, with 20% false positives.
  • Three parallel records of heart period time-series will be derived following the initial signal conditioning and RR interval detection from the two ECG signals and the invasive or non-invasive pulse waveform obtained from the pulsed oximeter. Missing or false detections of the R-wave will be implied from unusually long (multiple of a single interval) or short (fractional) intervals. Paired comparisons will be made between these three data sets and improperly detected events will be corrected when they coincide with the expected interval. When a long segment of any of the three waveforms is false, a low-level alarm will be issued (poor sensor contact, high level noise, or sensor disconnect). When long segments (5 seconds) of all three waveforms are false, a high level alarm will be issued.

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Abstract

La présente invention porte sur des procédés, des systèmes et des supports lisibles par ordinateur pour l'évaluation du risque de mortalité d'un patient hospitalisé. Selon un aspect, la présente invention porte sur un procédé d'évaluation du risque de mortalité d'un patient hospitalisé. Le procédé comprend la collecte de données à partir de signaux physiologiques générés par des dispositifs de surveillance de patient, de signaux physiologiques de fonction d'organe, et d'informations démographiques pour un patient. Le procédé comprend en outre la détermination d'une mesure de la variabilité d'au moins l'un des signaux physiologiques. Le procédé comprend en outre l'analyse des données et la mesure de la variabilité pour déterminer si une valeur pour une variable physiologique ou démographique particulière tombe ou non dans un intervalle critique pour la variable qui indique que la valeur est prédictive d'une mortalité ou d'une survie. Chaque fois qu'une valeur pour une variable physiologique ou démographique pour le patient tombe dans un intervalle critique, le procédé comprend l'enregistrement de la survenue d'un événement pour le patient. Le procédé comprend en outre le comptage du nombre d'événements pour le patient au cours d'une période de temps. Le procédé comprend en outre la génération, sur la base du comptage, d'un signal perceptible par un utilisateur humain qui indique le risque de mortalité ou les chances de survie du patient.
PCT/US2011/042416 2006-05-30 2011-06-29 Procédés, systèmes et supports lisibles par ordinateur pour l'évaluation du risque de mortalité d'un patient hospitalisé Ceased WO2012006174A2 (fr)

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014143743A1 (fr) * 2013-03-15 2014-09-18 Zansors Llc Monitorage et surveillance de l'état de santé, et détection d'anomalie
US20160256063A1 (en) * 2013-09-27 2016-09-08 Mayo Foundation For Medical Education And Research Analyte assessment and arrhythmia risk prediction using physiological electrical data
US10163174B2 (en) 2006-05-30 2018-12-25 The University Of North Carolina At Chapel Hill Methods, systems, and computer program products for evaluating a patient in a pediatric intensive care unit
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US10163174B2 (en) 2006-05-30 2018-12-25 The University Of North Carolina At Chapel Hill Methods, systems, and computer program products for evaluating a patient in a pediatric intensive care unit
US10694965B2 (en) 2010-07-14 2020-06-30 Mayo Foundation For Medical Education And Research Non-invasive monitoring of physiological conditions
WO2014143743A1 (fr) * 2013-03-15 2014-09-18 Zansors Llc Monitorage et surveillance de l'état de santé, et détection d'anomalie
CN105208921A (zh) * 2013-03-15 2015-12-30 赞索斯有限责任公司 健康监测、监察和异常检测
US20160256063A1 (en) * 2013-09-27 2016-09-08 Mayo Foundation For Medical Education And Research Analyte assessment and arrhythmia risk prediction using physiological electrical data
CN112151182A (zh) * 2020-08-14 2020-12-29 北京大学 一种基于贪心搜索的智能医疗建议生成方法及系统

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