The physiology analysis system: An integrated approach for warehousing, management and analysis of time-series physiology data

Prehospital Emergency Care

Gribok

et al. 2009

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Objectives. We explored whether there are diagnostically useful temporal trends in prehospital vital signs of trauma patients. Methods. Vital signs were monitored during transport to a level I trauma center and electronically archived. Retrospectively, we identified reliable vital signs recorded from the 0-to 7-minute interval and from the 14-to 21-minute interval during transport, and, for each subject, we computed the temporal differences between the two intervals' vital signs, the intrasubject 95% data ranges, the values during the initial 2 minutes, and the 21-minute overall means. We tested for differences between subjects with major hemorrhage versus control subjects, and computed receiveroperating characteristic (ROC) curves. We conducted sensitivity analyses, exploring alternative clinical outcomes, temporal windows, and methods of identifying reliable data. Results. Comparing major hemorrhage cases versus controls, there were no discriminatory differences in temporal vital sign trends. Hemorrhage cases had significantly wider intrasubject data ranges for systolic blood pressure (SBP), respiratory rate (RR), and shock index (SI) versus controls. All results were consistent in several sensitivity analyses. Conclusions. Our findings add to a growing body of evidence that prehospital vital sign trends over 21 minutes or less are unlikely to be diagnostically useful because of substantial nondirectional fluctuations in vital signs that would obscure any subtle, progressive temporal trends. SBP, RR, and SI values were significantly different for high-acuity patients, and had more variability. Taken together, these findings suggest that higher-acuity patients experience episodes of instability rather than gradual, steady decline. Measures that account for data variability, such as taking the average of multiple measurements, may improve the diagnostic utility of prehospital vital signs.

Section: Methodsmentioning

confidence: 99%

Exploration of Prehospital Vital Sign Trends for the Prediction of Trauma Outcomes

Prehospital Emergency Care

Gribok

et al. 2009

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“…The complete investigational data set was subsequently uploaded to our data warehousing system. 23 Protected health information was not included. All subsequent data analyses were performed using MATLAB (version 7; MathWorks, Natick, MA).…”

Section: Clinical Data Collectionmentioning

confidence: 99%

Prehospital Heart Rate and Blood Pressure Increase the Positive Predictive Value of the Glasgow Coma Scale for High-Mortality Traumatic Brain Injury

Kumar

et al. 2014

Journal of Neurotrauma

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We hypothesized that vital signs could be used to improve the association between a trauma patient's prehospital Glasgow Coma Scale (GCS) score and his or her clinical condition. Previously, abnormally low and high blood pressures have both been associated with higher mortality for patients with traumatic brain injury (TBI). We undertook a retrospective analysis of 1384 adult prehospital trauma patients. Vital-sign data were electronically archived and analyzed. We examined the relative risk of severe head Abbreviated Injury Scale (AIS) 5-6 as a function of the GCS, systolic blood pressure (SBP), heart rate (HR), and respiratory rate (RR). We created multi-variate logistic regression models and, using DeLong's test, compared their area under receiver operating characteristic curves (ROC AUCs) for three outcomes: head AIS 5-6, all-cause mortality, and either head AIS 5-6 or neurosurgical procedure. We found significant bimodal relationships between head AIS 5-6 versus SBP and HR, but not RR. When the GCS was < 15, ROC AUCs were significantly higher for a multi-variate regression model (GCS, SBP, and HR) versus GCS alone. In particular, patients with abnormalities in all parameters (GCS, SBP, and HR) were significantly more likely to have high-mortality TBI versus those with abnormalities in GCS alone. This could be useful for mobilizing resources (e.g., neurosurgeons and operating rooms at the receiving hospital) and might enable new prehospital management protocols where therapies are selected based on TBI mortality risk.

“…25 The original data were imported from the Trauma Vitals Database, which contains clinical data and continuous physiologic data from trauma patients transported from the scene of injury by helicopter service to the Level I unit at the Memorial Hermann Hospital in Houston, TX. Additional details about the trauma population and the Emergency Medical Service are available in.…”

Section: Settingmentioning

confidence: 99%

“…RTS was automatically-computed from (a) the mean of all reliable RR and SBP data in the initial 15 minutes of a subject's Propaq electronic record; versus (b) the mean of less reliable RR and SBP data in the initial 15 minutes. Data were extracted using the ad hoc filtering capabilities of the Physiology Analysis System, 25 and the computations were performed using routines we developed in the MatLab v7.1 computing environment (MathWorks; Natick, MA).…”

Section: Reliable Versus Unreliable Datamentioning

confidence: 99%

Automatically-Computed Prehospital Severity Scores are Equivalent to Scores Based on Medic Documentation

Journal of Trauma: Injury, Infection &Amp; Critical Care

McKenna

et al. 2008

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Methods:In a retrospective analysis of trauma casualties transported by air ambulance, we computed the Triage Revised Trauma Score (RTS) from archived travel monitor data. We compared the areas-under-the-curve (AUC's) of receiver operating characteristic curves for prediction of mortality and red blood cell transfusion for 187 subjects with comparable quantities of good-quality and poorquality data.Results: Vital signs deemed reliable by SQI's led to significantly more discriminatory severity scores than vital signs deemed unreliable. We also compared automatically-computed RTS (using the SQI's) versus RTS computed from vital signs documented by medics. For the subjects in whom the SQI algorithms identified 15 consecutive seconds of reliable vital signs data (n ‫؍‬ 350), the automaticallycomputed scores' AUC's were the same as the medic-based scores' AUC's. Using the Prehospital Index in place of RTS led to very similar results, corroborating our findings. Conclusions: SQI algorithms improveautomatically-computed severity scores, and automatically-computed scores using SQI's are equivalent to medic-based scores.