Patrick R. Norris

Heart rate multiscale entropy at three hours predicts hospital mortality in 3,154 trauma patients.

Complexity is a measure of variation and randomness potentially indicating improvement or deterioration in critically ill patients. Previously, we have shown integer heart rate (HR) multiscale entropy (MSE), an indicator of complexity, predicts death based on long duration (12 h) and dense (≥0.4 Hz) windows of HR data. However, such restrictions reduce the use of MSE in the clinical setting. We hypothesized MSE predicts death using HR data of shorter duration and lower density. During the initial 24 h of intensive care unit stay, 3,154 patients had at least 3 h of continuous integer HR sampled. The first continuous window of 3, 6, 9, and 12 h was selected for each patient regardless of density, and an open-source MSE algorithm was applied (M. Costa, www.physionet.org; m = 2; r = 0.15). Risk of death based on MSE, alone and with covariates (age, sex, injury severity score), was assessed using randomly selected logistic regression in half of the cases. Area under the receiver operator curve (AUC) was computed in the other half in subgroups having various durations and densities of HR data. At days 2.3 (median) and 4.9 (mean), 441 patients (14%) died. Multiscale entropy stratified patients by mortality and was an independent predictor of death using 3 h or more of data. Multiscale entropy alone (AUC = 0.66 - 0.71) predicted death comparably to covariates alone (AUC = 0.72). We conclude: (1) Heart rate MSE within hours of admission predicts death occurring days later. (2) Multiscale entropy is robust to variation in bedside data duration and density occurring in a working intensive care unit. (3) Complexity may be a new clinical biomarker of outcome.ABBREVIATIONS-AUC-area under receiver operator characteristic curve; HR-heart rate; HRV/HRVi-heart rate/integer heart rate variability; ICU-intensive care unit; IRB-institutional review board; ISS-injury severity score; MSE-multiscale entropy; OR-odds ratio; SIMON-signal interpretation and monitoring; TRACS-Trauma Registry of the American College of Surgeons; VUMC-Vanderbilt University Medical Center

Estradiol is associated with mortality in critically ill trauma and surgical patients

Objective:Sexual dimorphism (variation in outcome related to sex) after trauma–hemorrhage and sepsis is well documented in animals, with the pro-estrus state being proinflammatory and associated with a survival advantage. Although some observational studies confirm this pattern in humans, others demonstrate no difference in mortality. Estrogens are important modulators of the inflammatory response and insulin resistance in humans and have been linked to increased mortality during sepsis. Our objective was to determine whether sex hormone levels were associated with outcomes in critically ill surgical patients. Design:Prospective cohort. Patients:A total of 301 adult critically ill or injured surgical patients remaining in the intensive care unit for ≥48 hrs at two academic medical centers. Interventions:None. Measurements:Blood was collected 48 hrs after intensive care unit admission and assayed for sex hormones (estradiol, testosterone, prolactin, and progesterone) and cytokines (tumor necrosis factor-&agr; and interleukin-1, -2, -4, -6, -8, and -10). Demographic and outcome data were also collected. Main Results:Estradiol was significantly higher in nonsurvivors (p < .001). Analysis by quartiles of estradiol demonstrated greater than a three-fold increase in the mortality rate for the highest vs. the lowest estradiol quartiles (29% vs. 8%, p < .001). Estradiol was also higher in nonsurvivors. An estradiol level of 100 pg/mL was associated with an odds ratio for death of 4.60 (95% confidence interval, 1.56–13.0) compared with a reference estradiol level of 45 pg/mL. Conclusions:We conclude that serum estradiol correlates with mortality in critically ill and injured surgical patients and discuss potential mechanisms for this observation.

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.

Early Loss of Heart Rate Complexity Predicts Mortality Regardless of Mechanism, Anatomic Location, or Severity of Injury in 2178 Trauma Patients

BACKGROUND Reduced heart rate (HR) complexity (e.g., a lack of randomness or unpatterned variability) is an established predictor of trauma patient mortality. However, this finding has not been validated across the diverse spectrum of traumatic injury, and underlying mechanisms of this relationship are poorly understood. MATERIALS AND METHODS Two thousand one hundred seventy-eight trauma patients were admitted directly to the intensive care unit (ICU), and had sufficient (>6h) continuous integer heart rate data within the first d. Patients were stratified by location of isolated severe injury (head, torso, both, or neither), primary mechanism (blunt or penetrating), and probability of survival, an accepted scoring system based on age, admission vital signs, and injury type and severity. HR multiscale entropy (MSE) was calculated (sum of scales, Costas algorithm, physionet.org, m=2, r=0.15) to estimate complexity. Univariate analysis was performed by comparing MSE between survivors and nonsurvivors in each subgroup. Multivariate analysis incorporated logistic regression to characterize the relationship between MSE and risk of death, controlling for probability of survival. The MSE odds ratios (OR) and area under the receiver operator curve (AUC) were calculated. RESULTS Reduced MSE was significantly associated with increasing mortality, and was independent of probability of survival in all multivariate analyses (OR 0.87-0.94). This range of odds ratios implies that a patient with an MSE of 15 has roughly a 2- to 6-fold increase in odds of death versus a patient with an MSE of 25. The relationship between MSE and death was moderately stronger in patients with isolated severe head injury versus torso injury, and significantly stronger in patients with penetrating versus blunt mechanism of injury. MSE measured early in the hospital stay remained a robust predictor of mortality in all subgroups, even stratified by narrow ranges of probability of survival. CONCLUSIONS Early reduction of heart rate complexity is an important risk factor across diverse injury etiology. This suggests common underlying physiologic mechanisms linking the loss of biologic complexity to death.

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.

Reduced heart rate multiscale entropy predicts death in critical illness: a study of physiologic complexity in 285 trauma patients.

PURPOSE We have shown previously that reduced integer heart rate variability (HRVi) predicts death in trauma patients. We hypothesized that heart rate multiscale entropy (MSE), a potential measurement of physiologic complexity, would predict death more robustly than HRVi. MATERIALS AND METHODS Two hundred eighty-five patients had heart rate data meeting completeness and density criteria (>12 hours, >/=0.4 Hz) available in the first 24 hours after admission. Missing data points were interpolated, and a publicly available algorithm (MSE of Costa et al; Phys Rev E Stat Nonlin Soft Matter Phys. 2005;71[2 Pt 1]) was applied (www.physionet.org, m = 2, r = 0.15). Integer heart rate variability was computed using methods described previously (percentage of 5-minute intervals having heart rate SD between 0.3 and 0.6). Sample entropy was compared between survivors and nonsurvivors at each scale factor using Wilcoxon rank sum test. Logistic regression was used to assess risk of death based on HRVi, MSE, and/or covariates (age, sex, injury severity). RESULTS Decreased HRVi and MSE each predicted hospital mortality (median day of death, 3; mean, 7.1). Multiscale entropy-based risk stratification (area under the receiver operating characteristic curve [AUC] = 0.76, scale 15) was superior to HRVi (AUC = 0.70), but this difference in AUC was not statistically significant. Multiscale entropy stratified patients by mortality at every scale factor (P < .001). CONCLUSIONS Multiscale entropy and HRVi measured within the first 24 hours each identify trauma patients at increased risk of subsequent hospital death.

Colon Anastomosis After Damage Control Laparotomy: Recommendations From 174 Trauma Colectomies

BACKGROUND Primary colonic anastomosis in trauma patients has been demonstrated to be safe. However, few studies have investigated this in the setting of damage control laparotomy. We hypothesized that colonic anastomosis for trauma patients requiring an open abdomen (OA) would have a higher anastomotic leak (AL) rate when compared with patients having an immediate abdominal closure following trauma laparotomy. METHODS We performed a cohort comparison study of all trauma patients who underwent colectomy, between the years 2004 and 2009. Exclusion criteria were mortality within 24 hours of admission or colectomy for indications unrelated to injury. Data collected included age, gender, injury severity score, mechanism, length of stay, and mortality. Multivariable logistic regression was performed to assess the relationship of OA to our primary outcome measure, AL. RESULTS Totally, 174 patients met study criteria. Fecal diversion was performed in 58 patients, and colonic anastomosis was performed in the remaining 116 patients. Patients with OA had a clinically significant increase in AL rate compared with immediate abdominal closure (6% vs. 27%, p=0.002). Logistic regression demonstrated that OA was independently associated with AL, with OA patients having more than a sixfold increase in odds of AL compared with those who were closed (odds ratio=6.37, p=0.002, area under the receiver operator curve=0.72). Transfusion requirement and left-sided anastomosis were risk factors for leak. CONCLUSIONS Patients with a colonic anastomosis and an OA have an unacceptably high leak rate compared with those who undergo reconstruction with immediate closure. Given the significant risk of AL, colonic anastomosis should not be routinely performed in patients with OA.

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 Fishers exact and Wilcoxons 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.

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.

Cardiac uncoupling and heart rate variability are associated with intracranial hypertension and mortality: a study of 145 trauma patients with continuous monitoring.

BACKGROUND A noninvasive tool reflecting intracranial hypertension (ICH) should prompt early invasive monitoring and reduce secondary injury after traumatic brain injury. We hypothesized that integer heart rate variability (HRV) may be associated with rises in intracranial pressure (ICP); changes in HRV may precede changes in ICP; and both increases in ICP and cardiac uncoupling (low HRV) predict mortality. METHODS Of 14,330 consecutive trauma admissions, 291 of these patients had an injury requiring intracranial monitoring. Of these patients 145 had simultaneous HRV and ICP monitoring with a Camino monitor. ICP and heart rate (HR) data were matched and divided into 5-minute intervals (N = 117,956, representing 24.4 million HR and ICP data points). In each interval, the median ICP, and SD of HR (HRSD5) were calculated. Cardiac uncoupling was defined as an interval with HRSD5 between 0.3 bpm and 0.6 bpm. Cardiac uncoupling was compared between ICP categories using the Wilcoxon Rank-Sum test, and logistic regression was used to assess the continuous relationship between ICP and risk of uncoupling. RESULTS Cardiac uncoupling increases as ICP increases (p < 0.001). Uncoupling nearly doubles when comparing acceptable ICP (<20 mm Hg, 11% uncoupled) to ICH (31-50 mm Hg, 18% uncoupled), with uncoupling = 13% in the intermediate group (ICP 21-30 mm Hg). This trend continues at the level of malignant ICH (>50 mm Hg, 22% uncoupled). CONCLUSION Cardiac uncoupling increases as ICP increases. Both cardiac uncoupling and ICH predict mortality. Cardiac uncoupling may precede ICH but is not yet an indication for invasive monitoring.