Morbid obesity is not a risk factor for mortality in critically ill trauma patients.

BACKGROUND: Age, Injury severity score (ISS), hyperglycemia (HGL) at admission, and morbid obesity are known risk factors of poor outcome in trauma patients. Our aim was to which risk factors had the highest risk of death in the critically ill trauma patient. METHODS: A Trauma Registry of the American College of Surgeons database retrospective study was performed at our Level I trauma center from January 2000 to October 2004. Inclusion criteria were age >15 years and >or=3 days hospital stay. Data collected included age, gender, and ISS. Groups were divided into nonobese and morbidly obese (MO) (body mass index, BMI >or=40 kg/m2) and into HGL (mean >or=150 mg/dL on initial hospital day) and non-HGL. Primary outcome was 30-day mortality. Differences in mortality and demographic variables between groups were compared using Fisher's exact and Wilcoxon's rank sum tests. Univariate and multivariate logistic regression was used to assess the relationship of HGL, morbid obesity, age, and injury severity to risk of death. Relationships were assessed using odds ratios (OR) and area under the receiver operator characteristic curve (AUC). RESULTS: A total of 1,334 patients met study criteria and 70.5% were male. Demographic means were age 40.3, ISS 25.7, length of stay 13.4, and BMI 27.5. The most common mechanism of injury was motor vehicle collision 55.1%. Overall mortality was 4.7%. Mortality was higher in HGL versus non-HGL (8.7% vs. 3.5%; p < 0.001). Mortality was higher in MO versus nonobese, but not significantly (7.8 vs. 4.6%; not significant [NS] p = 0.222). Univariate logistic regression relationships of death to age OR: 1.031, p < 0.001, AUC +/- SE: 0.639 +/- 0.042; ISS OR: 1.044, p < 0.001, AUC +/- SE: 0.649 +/- 0.039; HGL OR: 2.765, p < 0.001; MO: OR: NS, p = NS, AUC +/- SE: NS. Relationships were similar in a combined multivariate model. CONCLUSION: HGL >150 mg/dL on the day of admission is associated with twofold increase in mortality, and an outcome measure should be followed. Morbid obesity (BMI >or=40) is not an independent risk factor for mortality in the critically ill trauma patient.

A computerized insulin infusion titration protocol improves glucose control with less hypoglycemia compared to a manual titration protocol in a trauma intensive care unit.

BACKGROUND: Previous studies reflect reduced morbidity and mortality with intensive blood glucose control in critically ill patients. Unfortunately, implementation of such protocols has proved challenging. This study evaluated the degree of glucose control using manual paper-based vs computer-based insulin protocols in a trauma intensive care unit. METHODS: Of 1455 trauma admissions from May 31 to December 31, 2005, a cohort of 552 critically ill patients met study entry criteria. The patients received intensive blood glucose management with IV insulin infusions. Using Fisher's exact test, the authors compared patients managed with a computerized protocol vs a paper-based insulin protocol with respect to the portion of glucose values in a target range of 80-110 mg/dL, the incidence of hyperglycemia (> or =150 mg/dL), and the incidence of hypoglycemia (< or =40 mg/dL). RESULTS: Three hundred nine patients were managed with a manual paper-based protocol and 243 were managed with a computerized protocol. The total number of blood glucose values across both groups was 21,178. Mean admission glucose was higher in the computer-based protocol group (170 vs 152 mg/dL; p < .001, t-test). Despite this finding by Fisher's exact test, glucose control was superior in the computerized group; a higher portion of glucose values was in range 80-110 mg/dL (41.8% vs 34.0%; p < .001), less hyperglycemia occurred (12.8% vs 15.1%; p < .001), and less hypoglycemia occurred (0.2% vs 0.5%; p < .001). CONCLUSIONS: A computerized insulin titration protocol improves glucose control by (1) increasing the percentage of glucose values in range, (2) reducing hyperglycemia, and (3) reducing severe hypoglycemia.

Adrenal insufficiency, heart rate variability, and complex biologic systems: a study of 1,871 critically ill trauma patients.

BACKGROUND: Reduction in integer heart rate variability (HRVi), one potential measurement of complex biologic systems, is common in ICU patients and is strongly associated with hospital mortality. Adrenal insufficiency (AI) and reduced HRVi are associated with autonomic dysfunction. Failure of the autonomic nervous system can be associated with loss of biologic complexity. We hypothesize decreased HRVi is associated with AI, and HRVi improves after treatment of AI, suggesting "recomplexification" (resumption of normal stress response to injury). STUDY DESIGN: Of 4,116 trauma ICU admissions from December 2000 to November 2005, 1,871 patients had sufficient physiologic, laboratory, pharmacy, and demographic data for analysis. Seventy-five patients failing cosyntropin-stimulation testing were defined as AI; the remaining 1,796 were defined as no AI. HRVi was calculated as integer heart rate standard deviation over 5-minute intervals. HRVi 10th, 50th (median), and 90th percentiles were calculated over the 72 hours pre-, or poststeroid, or both administration (AI). HRVi percentiles in non-AI patients were evaluated at the same interval and compared with AI using Wilcoxon's rank-sum test. In patients with AI, daily HRVi was computed 3 days before and after steroid administration, and compared between survivors and nonsurvivors. RESULTS: There were 2.9 million heart-rate intervals measured. HRVi stratified patients with AI (cosyntropin failure), and without AI. HRVi was similar in AI survivors and nonsurvivors before steroid treatment, but differed after treatment. HRVi increased substantially in survivors after steroid administration, yet did not change in nonsurvivors. HRVi does not increase in patients who are unresponsive to steroids and die. CONCLUSIONS: Reduced heart-rate variability, a potential measurement of complex biologic systems, is associated with cosyntropin-confirmed AI; improved in patients responding to steroid therapy; and is a noninvasive, real-time biomarker suggesting AI.

Integrating "best of care" protocols into clinicians' workflow via care provider order entry: impact on quality-of-care indicators for acute myocardial infarction.

OBJECTIVE: In the context of an inpatient care provider order entry (CPOE) system, to evaluate the impact of a decision support tool on integration of cardiology "best of care" order sets into clinicians' admission workflow, and on quality measures for the management of acute myocardial infarction (AMI) patients. DESIGN: A before-and-after study of physician orders evaluated (1) per-patient use rates of standardized acute coronary syndrome (ACS) order set and (2) patient-level compliance with two individual recommendations: early aspirin ordering and beta-blocker ordering. MEASUREMENTS: The effectiveness of the intervention was evaluated for (1) all patients with ACS (suspected for AMI at the time of admission) (N = 540) and (2) the subset of the ACS patients with confirmed discharge diagnosis of AMI (n = 180) who comprise the recommended target population who should receive aspirin and/or beta-blockers. Compliance rates for use of the ACS order set, aspirin ordering, and beta-blocker ordering were calculated as the percentages of patients who had each action performed within 24 hours of admission. RESULTS: For all ACS admissions, the decision support tool significantly increased use of the ACS order set (p = 0.009). Use of the ACS order set led, within the first 24 hours of hospitalization, to a significant increase in the number of patients who received aspirin (p = 0.001) and a nonsignificant increase in the number of patients who received beta-blockers (p = 0.07). Results for confirmed AMI cases demonstrated similar increases, but did not reach statistical significance. CONCLUSION: The decision support tool increased optional use of the ACS order set, but room for additional improvement exists.

The impact of a normoglycemic management protocol on clinical outcomes in the trauma intensive care unit.

BACKGROUND: The purpose of this study was to determine if protocol-driven normoglycemic management in trauma patients affected glucose control, ventilator-associated pneumonia, surgical-site infection, and inpatient mortality. METHODS: A prospective, consecutive-series, historically controlled study design evaluated protocol-driven normoglycemic management among trauma patients at Vanderbilt University Medical Center. Those mechanically ventilated > or =24 hours and > or =15 years of age were included. A glycemic-control protocol required insulin infusion therapy for glucose >110 mg/dL. Control patients included those who met criteria, were admitted the year preceding protocol implementation, and had hyperglycemia treated at the physician's discretion. RESULTS: Eight hundred eighteen patients met study criteria; 383 were managed without protocol; 435 underwent protocol. The protocol group had lower glucose levels 7 of 14 days measured. After admission, both groups had mean daily glucose levels <150 mg/dL. No difference in pneumonia (31.6% vs 34.5%; p = .413), surgical infection (5.0% vs 5.7%; p = .645) or mortality (12.3% vs 13.1%; p = .722) occurred between groups. If one episode of blood glucose level was > or =150 mg/dL (n = 638; 78.0%), outcomes were worse: higher daily glucose levels for 14 days after admission (p < .001), pneumonia rates (35.9% vs 23.3%; p = .002), and mortality (14.6% vs 6.1%; p = .002). One or more days of glucose > or =150 mg/dL had a 2- to 3-fold increase in the odds of death. Protocol use in these patients was not associated with outcome improvement. CONCLUSIONS: Protocol-driven management decreased glucose levels 7 of 14 days after admission without outcome change. One or more glucose levels > or =150 mg/dL were associated with worse outcome.

Investigation of vocal jitter and glottal flow spectrum as possible cues for depression and near-term suicidal risk.

Among the many clinical decisions that psychiatrists must make, assessment of a patient's risk of committing suicide is definitely among the most important, complex, and demanding. When reviewing his clinical experience, one of the authors observed that successful predictions of suicidality were often based on the patient's voice independent of content. The voices of suicidal patients judged to be high-risk near-term exhibited unique qualities, which distinguished them from nonsuicidal patients. We investigated the discriminating power of two excitation-based speech parameters, vocal jitter and glottal flow spectrum, for distinguishing among high-risk near-term suicidal, major depressed, and nonsuicidal patients. Our sample consisted of ten high-risk near-term suicidal patients, ten major depressed patients, and ten nondepressed control subjects. As a result of two sample statistical analyses, mean vocal jitter was found to be a significant discriminator only between suicidal and nondepressed control groups (p < 0.05). The slope of the glottal flow spectrum, on the other hand, was a significant discriminator between all three groups (p < 0.05). A maximum likelihood classifier, developed by combining the a posteriori probabilities of these two features, yielded correct classification scores of 85% between near-term suicidal patients and nondepressed controls, 90% between depressed patients and nondepressed controls, and 75% between near-term suicidal patients and depressed patients. These preliminary classification results support the hypothesized link between phonation and near-term suicidal risk. However, validation of the proposed measures on a larger sample size is necessary.

Decision support for patient care: implementing cybernetics.

The application of principles and methods of cybernetics permits clinicians and managers to use feedback about care effectiveness and resource expenditure to improve quality and to control costs. Keys to the process are the specification of therapeutic goals and the creation of an organizational culture that supports the use of feedback to improve care. Daily feedback on the achievement of each patient's therapeutic goals provides tactical decision support, enabling clinicians to adjust care as needed. Monthly or quarterly feedback on aggregated goal achievement for all patients on a clinical pathway provides strategic decision support, enabling clinicians and managers to identify problems with supposed "best practices" and to test hypotheses about solutions. Work is underway at Vanderbilt University Medical Center to implement feedback loops in care and management processes and to evaluate the effects.

Medical decision support using machine learning for early detection of late-onset neonatal sepsis.

OBJECTIVE: The objective was to develop non-invasive predictive models for late-onset neonatal sepsis from off-the-shelf medical data and electronic medical records (EMR). DESIGN: The data used in this study are from 299 infants admitted to the neonatal intensive care unit in the Monroe Carell Jr. Children's Hospital at Vanderbilt and evaluated for late-onset sepsis. Gold standard diagnostic labels (sepsis negative, culture positive sepsis, culture negative/clinical sepsis) were assigned based on all the laboratory, clinical and microbiology data available in EMR. Only data that were available up to 12 h after phlebotomy for blood culture testing were used to build predictive models using machine learning (ML) algorithms. MEASUREMENT: We compared sensitivity, specificity, positive predictive value and negative predictive value of sepsis treatment of physicians with the predictions of models generated by ML algorithms. RESULTS: The treatment sensitivity of all the nine ML algorithms and specificity of eight out of the nine ML algorithms tested exceeded that of the physician when culture-negative sepsis was included. When culture-negative sepsis was excluded both sensitivity and specificity exceeded that of the physician for all the ML algorithms. The top three predictive variables were the hematocrit or packed cell volume, chorioamnionitis and respiratory rate. CONCLUSIONS: Predictive models developed from off-the-shelf and EMR data using ML algorithms exceeded the treatment sensitivity and treatment specificity of clinicians. A prospective study is warranted to assess the clinical utility of the ML algorithms in improving the accuracy of antibiotic use in the management of neonatal sepsis.

Core temperature variation is associated with heart rate variability independent of cardiac index: a study of 278 trauma patients.

PURPOSE: The purpose of this study is to determine if temperature extremes are associated with reduced heart rate variability (HRV) and "cardiac uncoupling." MATERIALS AND METHODS: This was a retrospective, observational cohort study performed on 278 trauma intensive care unit admissions that had continuous HR, cardiac index (CI), and core temperature data from "thermodilution" Swan-Ganz catheter. Dense (captured second-by-second) physiologic data were divided into 5-minute intervals (N = 136 133; 11 344 hours of data). Mean CI, mean temperature, and integer HR SD were computed for each interval. Critically low HRV was defined as HR SD from 0.3 to 0.6 beats per minute. Temperature extremes were defined as less than 36°C or greater than 39°C. RESULTS: Low HRV and CI vary with temperature. Temperature extremes are associated with increased risk for critically low HRV (odds ratio, >1.8). Cardiac index increases with temperature until hyperthermia (>40°C). At temperature extremes, changes in CI were not explained solely by changes in HR. CONCLUSIONS: The conclusions of this study are (1) temperature extremes are associated with low HRV, potentially reflecting cardiac autonomic dysfunction; (2) CI increases with temperature; and (3) HRV provides additional physiologic information unobtainable via current invasive cardiac monitoring and current vital signs.

Characteristics and effects of nurse dosing over-rides on computer-based intensive insulin therapy protocol performance.

OBJECTIVE: To determine characteristics and effects of nurse dosing over-rides of a clinical decision support system (CDSS) for intensive insulin therapy (IIT) in critical care units. DESIGN: Retrospective analysis of patient database records and ethnographic study of nurses using IIT CDSS. MEASUREMENTS: The authors determined the frequency, direction-greater than recommended (GTR) and less than recommended (LTR)- and magnitude of over-rides, and then compared recommended and over-ride doses' blood glucose (BG) variability and insulin resistance, two measures of IIT CDSS associated with mortality. The authors hypothesized that rates of hypoglycemia and hyperglycemia would be greater for recommended than over-ride doses. Finally, the authors observed and interviewed nurse users. RESULTS: 5.1% (9075) of 179,452 IIT CDSS doses were over-rides. 83.4% of over-ride doses were LTR, and 45.5% of these were ≥ 50% lower than recommended. In contrast, 78.9% of GTR doses were ≤ 25% higher than recommended. When recommended doses were administered, the rate of hypoglycemia was higher than the rate for GTR (p = 0.257) and LTR (p = 0.033) doses. When recommended doses were administered, the rate of hyperglycemia was lower than the rate for GTR (p = 0.003) and LTR (p < 0.001) doses. Estimates of patients' insulin requirements were higher for LTR doses than recommended and GTR doses. Nurses reported trusting IIT CDSS overall but appeared concerned about recommendations when administering LTR doses. CONCLUSION: When over-riding IIT CDSS recommendations, nurses overwhelmingly administered LTR doses, which emphasized prevention of hypoglycemia but interfered with hyperglycemia control, especially when BG was >150 mg/dl. Nurses appeared to consider the amount of a recommended insulin dose, not a patient's trend of insulin resistance, when administering LTR doses overall. Over-rides affected IIT CDSS protocol performance.

Social, organizational, and contextual characteristics of clinical decision support systems for intensive insulin therapy: a literature review and case study.

INTRODUCTION: Evaluations of computerized clinical decision support systems (CDSS) typically focus on clinical performance changes and do not include social, organizational, and contextual characteristics explaining use and effectiveness. Studies of CDSS for intensive insulin therapy (IIT) are no exception, and the literature lacks an understanding of effective computer-based IIT implementation and operation. RESULTS: This paper presents (1) a literature review of computer-based IIT evaluations through the lens of institutional theory, a discipline from sociology and organization studies, to demonstrate the inconsistent reporting of workflow and care process execution and (2) a single-site case study to illustrate how computer-based IIT requires substantial organizational change and creates additional complexity with unintended consequences including error. DISCUSSION: Computer-based IIT requires organizational commitment and attention to site-specific technology, workflow, and care processes to achieve intensive insulin therapy goals. The complex interaction between clinicians, blood glucose testing devices, and CDSS may contribute to workflow inefficiency and error. Evaluations rarely focus on the perspective of nurses, the primary users of computer-based IIT whose knowledge can potentially lead to process and care improvements. CONCLUSION: This paper addresses a gap in the literature concerning the social, organizational, and contextual characteristics of CDSS in general and for intensive insulin therapy specifically. Additionally, this paper identifies areas for future research to define optimal computer-based IIT process execution: the frequency and effect of manual data entry error of blood glucose values, the frequency and effect of nurse overrides of CDSS insulin dosing recommendations, and comprehensive ethnographic study of CDSS for IIT.

Effects of blood glucose transcription mismatches on a computer-based intensive insulin therapy protocol.

PURPOSE: Computerized clinical decision support systems (CDSS) for intensive insulin therapy (IIT) generate recommendations using blood glucose (BG) values manually transcribed from testing devices to computers, a potential source of error. We quantified the frequency and effect of blood glucose transcription mismatches on IIT protocol performance. METHODS: We examined 38 months of retrospective data for patients treated with CDSS IIT in two intensive care units at one teaching hospital. A manually transcribed BG value not equal to a corresponding device value was deemed mismatched. For mismatches we recalculated CDSS recommendations using device BG values. We compared matched and mismatched data in terms of CDSS alerts, blood glucose variability, and dosing. RESULTS: Of 189,499 CDSS IIT instances, 5.3% contained mismatched BG values. Mismatched data triggered 93 false alerts and failed to issue 170 alerts for nurses to notify physicians. Four of six BG variability measures differed between matched and mismatched data. Overall insulin dose was greater for matched than mismatched [matched 3.8 (1.6-6.0), median (interquartile range, IQR), versus 3.6 (1.6-5.7); p < 0.001], but recalculated and actual dose were similar. In mismatches preceding hypoglycemia, recalculated insulin dose was significantly lower than actual dose [recalculated 2.7 (0.4-5.0), median (IQR), versus 3.5 (1.4-5.6)]. In mismatches preceding hyperglycemia, recalculated insulin dose was significantly greater than actual dose [recalculated 4.7 (3.3-6.2), median (IQR), versus 3.3 (2.4-4.3); p < 0.001]. Administration of recalculated doses might have prevented blood glucose excursions. CONCLUSIONS: Mismatched blood glucose values can influence CDSS IIT protocol performance.

A simple non-physiological artifact filter for invasive arterial blood pressure monitoring: a study of 1852 trauma ICU patients.

Invasive arterial blood pressure (BP) is a vital sign in hemodynamic monitoring of trauma intensive care unit (ICU) patients. Continuous BP analysis can potentially provide additional information about patient status, predict morbidity and mortality, and automatically populate electronic nurse charting systems than intermittent monitoring. Challenges to routine application in the ICU include integration of complex physiological data collection systems, artifacts, missing data, and the various clinical interventions that may temporarily corrupt the BP signal. We have developed and previously described SIMON (signal interpretation and monitoring), a physiological data collection system in the Trauma ICU at Vanderbilt University. In order to extract useful information from continuous arterial line BP monitoring, it is necessary to remove non-physiological artifacts. In this setting, potential artifacts appear to be caused by resonance, over-damping, and data transmission. We designed a simple filter to identify various sources of non-physiological artifacts using statistical signal processing techniques. We implemented the filter to arterial invasive BP signals of 1852 trauma patients throughout their length of ICU stay. After filtering, the power of BP measures to predict hospital death was enhanced. Therefore, we concluded that our strategy of removing non-physiological artifact was simple, fast and useful for an accurate assessment of BP measures in trauma patients.

Cardiac uncoupling and heart rate variability stratify ICU patients by mortality: a study of 2088 trauma patients.

OBJECTIVE: We have previously shown that cardiac uncoupling (reduced heart rate variability) in the first 24 hours of trauma ICU stay is a robust predictor of mortality. We hypothesize that cardiac uncoupling over the entire ICU stay independently predicts mortality, reveals patterns of injury, and heralds complications. METHODS: A total of 2088 trauma ICU patients satisfied the inclusion criteria for this study. Cardiac uncoupling by outcome was compared using the Wilcoxon rank sum test. Risk of death from cardiac uncoupling and covariates (age, ISS, AIS Head Score, total transfusion requirements) was assessed using multivariate logistic regression models at each ICU day. Univariate logistic regression was used to assess risk of death from uncoupling irrespective of covariates at each ICU day. RESULTS: A total of 1325 (63.5%) patients displayed some degree of uncoupling over their ICU stay. The difference in uncoupling between survivors and nonsurvivors is both dramatic and consistent across the entire ICU stay, indicating that the presence of uncoupling is unrelated to the cause of death. However, the magnitude of uncoupling varies by day when data is stratified by cause of death. CONCLUSIONS: Cardiac uncoupling: 1) is an independent predictor of death throughout the ICU stay, 2) has a predictive window of 2 to 4 days, and 3) appears to increase in response to inflammation, infection, and multiple organ failure.

Reduced heart rate variability: an indicator of cardiac uncoupling and diminished physiologic reserve in 1,425 trauma patients.

BACKGROUND: Measurements of a patient's physiologic reserve (age, injury severity, admission lactic acidosis, transfusion requirements, and coagulopathy) reflect robustness of response to surgical insult. We have previously shown that cardiac uncoupling (reduced heart rate variability, HRV) in the first 24 hours after injury correlates with mortality and autonomic nervous system failure. We hypothesized: Deteriorating physiologic reserve correlates with reduced HRV and cardiac uncoupling. METHODS: There were 1,425 trauma ICU patients that satisfied the inclusion criteria. Differences in mortality across categorical measurements of the domains of physiologic reserve were assessed using the chi test. The relationship of cardiac uncoupling and physiologic reserve was examined using multivariate logistic regression models for various levels of cardiac uncoupling (>0 through 28% reduced HRV in the first 24 hours). RESULTS: Of these, 797 (55.9%) patients exhibited cardiac uncoupling. Deteriorating measures of physiologic reserve reflected increased risk of death. Measures of acidosis (admission lactate, time to lactate normalization, and lactate deterioration over the first 24 hours), coagulopathy, age, and injury severity contributed significantly to the risk of cardiac uncoupling (area under receiver operator curve, ROC=0.73). The association between deteriorating reserve and cardiac uncoupling increases with the threshold for uncoupling (ROC=0.78). CONCLUSIONS: Reduced heart rate variability is a new biomarker reflecting the loss of command and control of the heart (cardiac uncoupling). Risk of cardiac uncoupling increases significantly as a patient's physiologic reserve deteriorates and physiologic exhaustion approaches. Cardiac uncoupling provides a noninvasive, overall measure of a patient's clinical trajectory over the first 24 hours of ICU stay.

Heart rate variability predicts trauma patient outcome as early as 12 h: implications for military and civilian triage.

BACKGROUND: Our previous work demonstrated dense physiological data capture in the intensive care unit (ICU), defined a new vital sign Cardiac Volatility Related Dysfunction (CVRD) reflecting reduced heart rate variability, and demonstrated CVRD predicts death during the hospital stay adjusting for age and injury severity score (ISS). We hypothesized a more precise definition of variability in integer heart rate improves predictive power earlier in ICU stay, without adjusting for covariates. METHODS: Approximately 120 million integer heart rate (HR) data points were prospectively collected and archived from 1316 trauma ICU patients, linked to outcome data, and de-identified. HR standard deviation was computed in each 5-min interval (HR(SD5)). HR(SD5) logistic regression identified ranges predictive of death. The study group was randomly divided. Integer heart rate variability (% time HR(SD5) in predictive distribution ranges) models were developed on the first set (N = 658) at 1, 2, 4, 6, 8, 12, and 24 h after ICU admission, and validated on the second set (N = 658). RESULTS: HR(SD5) is bimodal, predicts death at low (0.1-0.9 bpm) and survival at high (1.8-2.6 bpm) ranges. HRV predicts death as early as 12 h (ROC = 0.67). HRV in a moving 1-h window is a simple graphic display technique. CONCLUSIONS: Dense physiological data capture allows calculation of HRV, which: 1) Independently predicts hospital death in trauma patients at 12 h; 2) Shows early differences by mortality in groups of patients when viewed in a moving window; and 3) May have implications for military and civilian triage.

Volatility: a new vital sign identified using a novel bedside monitoring strategy.

BACKGROUND: SIMON (Signal Interpretation and Monitoring) monitors and archives continuous physiologic data in the ICU (HR, BP, CPP, ICP, CI, EDVI, SVO2, SPO2, SVRI, PAP, and CVP). We hypothesized: heart rate (HR) volatility predicts outcome better than measures of central tendency (mean and median). METHODS: More than 600 million physiologic data points were archived from 923 patients over 2 years in a level one trauma center. Data were collected every 1 to 4 seconds, stored in a MS-SQL 7.0 relational database, linked to TRACS, and de-identified. Age, gender, race, Injury Severity Score (ISS), and HR statistics were analyzed with respect to outcome (death and ventilator days) using logistic and Poisson regression. RESULTS: We analyzed 85 million HR data points, which represent more than 71,000 hours of continuous data capture. Mean HR varied by age, gender and ISS, but did not correlate with death or ventilator days. Measures of volatility (SD, % HR >120) correlated with death and prolonged ventilation. CONCLUSIONS: 1) Volatility predicts death better than measures of central tendency. 2) Volatility is a new vital sign that we will apply to other physiologic parameters, and that can only be fully explored using techniques of dense data capture like SIMON. 3) Densely sampled aggregated physiologic data may identify sub-groups of patients requiring new treatment strategies.

Integrating automated patient letter generation into the electronic medical record.

This poster describes the design and functionality of StarLetter, an electronic patient letter generation tool. StarLetter is integrated into the new results feature of the electronic medical record front end allowing the clinicians to generate electronic letters to patients within their workflow.

Reduced heart rate volatility: an early predictor of death in trauma patients.

OBJECTIVE: To determine if using dense data capture to measure heart rate volatility (standard deviation) measured in 5-minute intervals predicts death. BACKGROUND: Fundamental approaches to assessing vital signs in the critically ill have changed little since the early 1900s. Our prior work in this area has demonstrated the utility of densely sampled data and, in particular, heart rate volatility over the entire patient stay, for predicting death and prolonged ventilation. METHODS: Approximately 120 million heart rate data points were prospectively collected and archived from 1316 trauma ICU patients over 30 months. Data were sampled every 1 to 4 seconds, stored in a relational database, linked to outcome data, and de-identified. HR standard deviation was continuously computed over 5-minute intervals (CVRD, cardiac volatility-related dysfunction). Logistic regression models incorporating age and injury severity score were developed on a test set of patients (N = 923), and prospectively analyzed in a distinct validation set (N = 393) for the first 24 hours of ICU data. RESULTS: Distribution of CVRD varied by survival in the test set. Prospective evaluation of the model in the validation set gave an area in the receiver operating curve of 0.81 with a sensitivity and specificity of 70.1 and 80.0, respectively. CVRD predict death as early as 24 hours in the validation set. CONCLUSIONS: CVRD identifies a subgroup of patients with a high probability of dying. Death is predicted within first 24 hours of stay. We hypothesize CVRD is a surrogate for autonomic nervous system dysfunction.