Volatility: A New Vital Sign Identified Using a Novel Bedside Monitoring Strategy

Heart Rate Variability as a Predictor of Death in Burn Patients

Heart rate variability (HRV), a noninvasive technique used to quantify fluctuations in the interval between normal heart beats (NN), is a predictor of mortality in some patient groups. The aim of this study was to assess HRV in burn trauma patients as a predictor of mortality. The authors prospectively performed 24-hour Holter monitoring on burn patients and collected demographic information, burn injury details, and in-hospital clinical events. Analysis of HRV in the time and frequency domains was performed. A total of 40 burn patients with a mean age of 44 ± 15 years were enrolled. Mean %TBSA burn was 27 ± 22% for the overall population and was significantly higher in those who died compared with those who survived (55 ± 23% vs 19 ± 13%; P < .0001). There was a statistically significant inverse linear correlation between SD of NN intervals and %TBSA (r = -.337, R = 0.113, 95% CI = -0.587 to -0.028, two-tailed P = .034), as well as with ultra low frequency power and %TBSA burn (r = -0.351, R = 0.123, 95% CI = -0.152 to -0.009; P = .027). The receiver-operator characteristic showed the area under the curve for %TBSA as a predictor of death was 0.82 (P < .001), for SDANN was 0.94 (P < .0001), and for ultra low frequency power was 0.96 (P < .0001). Deranged HRV in the early postburn period is a strong predictor of death.

The influence of anesthesia on heart rate complexity during elective and urgent surgery in 128 patients

BACKGROUND

As an emerging "new vital sign," heart rate complexity (by sample entropy [SampEn]) has been shown to be a useful trauma triage tool by predicting occult physiologic compromise and need for life-saving interventions. Sample entropy may be confounded by anesthesia possibly limiting its value intraoperatively. We investigated the effects of anesthesia on SampEn during elective and urgent surgical procedures. We hypothesized that SampEn is reduced by general anesthesia.

METHODS

With institutional review board-approved waiver of informed consent, 128 patients undergoing elective or urgent general surgery were prospectively enrolled. Real-time heart rate complexity was calculated using SampEn through electrocardiogram recordings of 200 consecutive beats in a continuous sliding-window fashion. We recorded SampEn starting 10 minutes before induction until 10 minutes after emergence from anesthesia. The time before induction of anesthesia was categorized as period 1, the time after induction and before emergence as period 2 (intraoperative), and the time after emergence as period 3. We analyzed SampEn changes as patients moved between the different periods and made 3 comparisons: from period 1 with period 2 (comparison A), from period 2 with period 3 (comparison B). We also compared period 1 with period 3 SampEn (comparison C).

RESULTS

The mean SampEn value for all patients before induction of anesthesia was 1.55 ± 0.58. In each 1 of the 3, comparisons there was a decline in SampEn. Comparison A had a mean decrease of 0.53 ± 0.55 (P < .0001), comparison B had a decrease of 0.13 ± 0.52 (P < .0051), and the mean SampEn difference for comparison C was 0.66 ± 0.53 (P < .0001). Certain pharmacologics had significant effect on SampEn as did need for urgent surgery and American Society of Anesthesiologists class.

CONCLUSION

Sample entropy decreases after induction of anesthesia and continues to decrease even immediately after emergence in patients without any immediately life-threatening conditions. This finding may complicate interpretation low complexity as a predictor of life-saving interventions in patients in the perioperative period.

Heart rate variability in patients being treated for dengue viral infection: new insights from mathematical correction of heart rate

INTRODUCTION

Severe dengue hemorrhagic fever (DHF) is a viral infection that acts to increase permeability of capillaries, resulting in internal hemorrhage. Linear frequency domain Fourier spectral analysis represents the most published noninvasive tool for diagnosing and assessing health status via calculated heart rate variability (HRV). As such, HRV may be useful in assessing clinical status in DHF patients, but is prone to erroneous results and conclusions due to the influence of the average HR during the time period of HRV assessment (defined as the "prevailing" HR). We tested the hypothesis that alterations in HRV calculated with linear frequency analysis would be minimal when mathematically corrected for prevailing HR following dengue viral infection.

METHODS

Male (N = 16) and female (N = 11) patients between the ages of 6 months and 15 years of age (10 ± 6 SD years) were tracked through the progression of the dengue viral infection with treatment following the abatement of a fever (defervescence). Electrocardiographic recordings were collected and analyzed for HRV.

RESULTS

High frequency (HF), low frequency (LF), and LF/HF ratio were unaffected by correction for prevailing HR.

CONCLUSION

HRV corrected for changes in HR did not alter the interpretation of our data. Therefore, we conclude that cardiac parasympathetic activity (based on HF frequency) is responsible for the majority of the HR reduction following defervescence in patients with dengue viral infection.

Predicting the need for urgent intubation in a surgical/trauma intensive care unit

BACKGROUND

Analysis and modeling of data monitoring vital signs and waveforms in patients in a surgical/trauma intensive care unit (STICU) may allow for early identification and treatment of patients with evolving respiratory failure.

METHODS

Between February 2011 and March 2012, data of vital signs and waveforms for STICU patients were collected. Every-15-minute calculations (n = 172,326) of means and standard deviations of heart rate (HR), respiratory rate (RR), pulse-oxygen saturation (SpO2), cross-correlation coefficients, and cross-sample entropy for HR-RR, RR-SpO2, and HR-SpO2, and cardiorespiratory coupling were calculated. Urgent intubations were recorded. Univariate analyses were performed for the periods <24 and ≥24 hours before intubation. Multivariate predictive models for the risk of unplanned intubation were developed and validated internally by subsequent sample and bootstrapping techniques.

RESULTS

Fifty unplanned intubations (41 patients) were identified from 798 STICU patients. The optimal multivariate predictive model (HR, RR, and SpO2 means, and RR-SpO2 correlation coefficient) had a receiving operating characteristic (ROC) area of 0.770 (95% confidence interval [CI], 0.712-0.841). For this model, relative risks of intubation in the next 24 hours for the lowest and highest quintiles were 0.20 and 2.95, respectively (15-fold increase, baseline risk 1.46%). Adding age and days since previous extubation to this model increased ROC area to 0.865 (95 % CI, 0.821-0.910).

CONCLUSION

Among STICU patients, a multivariate model predicted increases in risk of intubation in the following 24 hours based on vital sign data available currently on bedside monitors. Further refinement could allow for earlier detection of respiratory decompensation and intervention to decrease preventable morbidity and mortality in surgical/trauma patients.

Relationship between mortality and first-day events index from routinely gathered physiological variables in ICU patients

OBJECTIVE

To test the hypothesis that the degree and duration of alterations in physiological variables routinely gathered by intensive care unit (ICU) monitoring systems during the first day of admission to the ICU, together with a few additional routinely recorded data, yield information similar to that obtained by traditional mortality prediction systems.

DESIGN

A prospective observational multicenter study (EURICUS II) was carried out.

SETTING

Fifty-five European ICUs.

PATIENTS

A total of 17,598 consecutive patients admitted to the ICU over a 10-month period.

INTERVENTIONS

None.

MAIN VARIABLES OF INTEREST

Hourly data were manually gathered on alterations or "events" in systolic blood pressure, heart rate and oxygen saturation throughout ICU stay to construct an events index and mortality prediction models.

RESULTS

The mean first-day events index was 6.37±10.47 points, and was significantly associated to mortality (p<0.001), with a discrimination capacity for hospital mortality of 0.666 (area under the ROC curve). A second index included this first-day events index, age, pre-admission location, and the Glasgow coma score. A model constructed with this second index plus diagnosis upon admission was validated by using the Jackknife method (Hosmer-Lemeshow, H=13.8554, insignificant); the area under ROC curve was 0.818.

CONCLUSIONS

A prognostic index with performance very similar to that of habitual systems can be constructed from routine ICU data with only a few patient characteristics. These results may serve as a guide for the possible automated construction of ICU prognostic indexes.

Heart Rate Variability during Simulated Hemorrhage with Lower Body Negative Pressure in High and Low Tolerant Subjects

Heart rate variability (HRV) decreases during hemorrhage, and has been proposed as a new vital sign to assess cardiovascular stability in trauma patients. The purpose of this study was to determine if any of the HRV metrics could accurately distinguish between individuals with different tolerance to simulated hemorrhage. Specifically, we hypothesized that (1) HRV would be similar in low tolerant (LT) and high tolerant (HT) subjects at presyncope when both groups are on the verge of hemodynamic collapse; and (2) HRV could distinguish LT subjects at presyncope from hemodynamically stable HT subjects (i.e., at a submaximal level of hypovolemia). Lower body negative pressure (LBNP) was used as a model of hemorrhage in healthy human subjects, eliciting central hypovolemia to the point of presyncopal symptoms (onset of hemodynamic collapse). Subjects were classified as LT if presyncopal symptoms occurred during the −15 to −60 mmHg levels of LBNP, and HT if symptoms occurred after LBNP of −60 mmHg. A total of 20 HRV metrics were derived from R–R interval measurements at the time of presyncope, and at one level prior to presyncope (submax) in LT and HT groups. Only four HRV metrics (Long-range Detrended Fluctuation Analysis, Forbidden Words, Poincaré Plot Descriptor Ratio, and Fractal Dimensions by Curve Length) supported both hypotheses. These four HRV metrics were evaluated further for their ability to identify individual LT subjects at presyncope when compared to HT subjects at submax. Variability in individual LT and HT responses was so high that LT responses overlapped with HT responses by 85–97%. The sensitivity of these HRV metrics to distinguish between individual LT from HT subjects was 6–33%, and positive predictive values were 40–73%. These results indicate that while a small number of HRV metrics can accurately distinguish between LT and HT subjects using group mean data, individual HRV values are poor indicators of tolerance to hypovolemia.

Heart rate variability is an independent predictor of morbidity and mortality in hemodynamically stable trauma patients

BACKGROUND

Reduced heart rate variability (HRV) reflects autonomic dysfunction and can triage patients better than routine trauma criteria or vital signs. However, there is questionable specificity and no consensus measurement technique. The purpose of this study was to analyze whether factors that alter autonomic function affect the specificity of HRV for assessing traumatic injury.

METHODS

We evaluated 216 hemodynamically stable adults (3:1 M:F; 97:3 blunt:penetrating; age 49 years ± 1 year, mean ± standard error) undergoing computed axial tomography (CT) scan to rule out traumatic brain injury (TBI). All were prospectively instrumented with a Mars Holter system (GE Healthcare, Milwaukee, WI). HRV was determined offline using time domain (standard deviation of normal-normal intervals, root-mean-square successive difference) and frequency domain (very low frequency [VLF], LF, wideband frequency, high frequency [HF], low to HF index ratio) calculations from 15-minute electrocardiogram and correlated with routine vital signs, mortality, TBI, morbidity, length of stay (LOS), and comorbidities. Significance (p ≤ 0.05) was determined using nonparametric analysis, Student's t test, analysis of variance, or multiple logistic regression.

RESULTS

VLF alone predicted survival, severity of TBI, intensive care unit LOS, and hospital LOS (all p < 0.05). Beta-blockers or diabetes had no effect, whereas age, sedation, mechanical ventilation, spinal cord injury, and intoxication influenced one or more of the variables with age being the most powerful confounder (all p < 0.05). Except for the Glasgow Coma Scale, no other routine trauma or hemodynamic criteria correlated with any of these outcomes.

CONCLUSIONS

Decreased VLF is an independent predictor of mortality and morbidity in hemodynamically stable trauma patients. Other time and other frequency domain variables correlated with some, but not all, outcomes. All were heavily influenced by factors that alter autonomic function, especially patient age.

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.

Clinical applications of heart rate variability in the triage and assessment of traumatically injured patients

Heart rate variability (HRV) is a method of physiologic assessment which uses fluctuations in the RR intervals to evaluate modulation of the heart rate by the autonomic nervous system (ANS). Decreased variability has been studied as a marker of increased pathology and a predictor of morbidity and mortality in multiple medical disciplines. HRV is potentially useful in trauma as a tool for prehospital triage, initial patient assessment, and continuous monitoring of critically injured patients. However, several technical limitations and a lack of standardized values have inhibited its clinical implementation in trauma. The purpose of this paper is to describe the three analytical methods (time domain, frequency domain, and entropy) and specific clinical populations that have been evaluated in trauma patients and to identify key issues regarding HRV that must be explored if it is to be widely adopted for the assessment of trauma patients.

Is heart period variability associated with the administration of lifesaving interventions in individual prehospital trauma patients with normal standard vital signs?

OBJECTIVE

To determine whether heart period variability provides added value in identifying the need for lifesaving interventions (LSI) in individual trauma patients with normal standard vital signs upon early medical assessment.

DESIGN

Retrospective database review.

SETTING

Helicopter transport to Level 1 trauma center and first 24 hrs of in-hospital care.

PATIENTS

Prehospital trauma patients requiring helicopter transport to Level 1 trauma center.

MEASUREMENTS AND MAIN RESULTS

Heart period variability was analyzed from electrocardiographic recordings collected from 159 prehospital trauma patients with normal standard vital signs (32 LSI patients, 127 No-LSI patients). Although 13 of the electrocardiogram derived metrics demonstrated simple (i.e., univariate) discrimination between groups, at the multivariate level, only fractal dimension by curve length (FD-L) was uniquely associated with group membership (LSI vs. No-LSI, p = .0004). Whereas the area under the receiver operating characteristics curve for FD-L was 0.70, the overall correct classification rate (true positives and true negatives) of 82% was only 2% higher than the baseline prediction rate of 80% (i.e., no information except for the known proportion of overall No-LSI cases, 127 of 159 patients). Furthermore, 84% of the individual FD-L values for the LSI group were within the range of the No-LSI group.

CONCLUSIONS

Only FD-L was uniquely able to distinguish patient groups based on mean values when standard vital signs were normal. However, the accuracy of FD-L in distinguishing between patients was only slightly better than the baseline prediction rate. There was also very high overlap of individual heart period variability values between groups, so many LSI patients could be incorrectly classified as not requiring an LSI if a single heart period variability value was used as a triage tool. Based on this analysis, heart period variability seems to have limited value for prediction of LSIs in prehospital trauma patients with normal standard vital signs.

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.

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.

Challenges for modeling and interpreting the complex biology of severe injury and inflammation

Human injury is associated with inflammatory responses that are modulated by the acute and chronic activity of endogenous factors and exogenous interventions. A characteristic feature of chronic, severe inflammatory states is the diminished signal output variability of many organ systems, including innate immune responsiveness and endogenous neural and endocrine-mediated functions. The attenuation of signal/response variability and integration of feedback capacity may contribute to systemic and tissue-specific deterioration of function. Some well-intentioned therapies directed toward support of systemic and tissue functions may actually promote the loss of system(s) adaptability and contribute to adverse outcomes in severely stressed patients. In vivo and in silico models of stress, injury, and infection have yet to fully define the influences of ongoing stressful stimulae as well as genetic variation and epigenetic factors in the context of an evolving inflammatory state. Experimental and human models incorporating variable, antecedent stress(es) and altered neuroendocrine rhythms might approximate the altered adaptability in immune and organ function responses. Such models may also provide insights into the salient mechanisms of risk and outcome more precisely than do the constrained study conditions of current animal or human models of systemic inflammation.

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.

Validation of stroke work and ventricular arterial coupling as markers of cardiovascular performance during resuscitation

BACKGROUND

Resuscitation regimens based on stroke work index (SWI) and ventricular-arterial coupling (VAC) are controversial. The Signal Interpretation and Monitoring (SIMON) system continuously collects and stores physiologic intensive care unit (ICU) bedside data at 3- to 5-second intervals. The purpose of this study was to demonstrate the capabilities of a completely automated data management system by further evaluating SWI-based resuscitation.

METHODS

This study was a retrospective review of all severely injured patients requiring a pulmonary artery catheter (PAC) for acute postinjury resuscitation. Patients with a severe head injury were excluded. Hemodynamic (HD) data (21 million datapoints) were densely acquired and archived by SIMON. Mean values of HD variables were compared between survivors and nonsurvivors. Receiver operator characteristic (ROC) curves were constructed for HD variables. Threshold values which maximized sensitivity and specificity were determined.

RESULTS

Eighty-eight patients over a 19-month time period met criteria and were included in the analysis. SWI was significantly greater in survivors versus nonsurvivors (4421 +/- 1278 versus 3163 +/- 1066 mm Hg . mL/m, p = 0.0008). VAC was quantified by the ratio (RATIO) of afterload (Ea) to contractility (Ees). RATIO (Ea/Ees) in survivors was significantly better than in nonsurvivors (1.9 +/- 1.1 vs. 2.9 +/- 1.0, p = 0.002). ROC curves identified threshold values of 3250 mm Hg x mL/m for SWI and 2.1 for RATIO (AUC = 0.78 and 0.82, respectively).

CONCLUSION

Previous work demonstrating the use of SWI and VAC as resuscitation guidelines was supported through the use of a powerful ICU data management system (SIMON). The emergence of these "new vital signs" may change the way injured patients are evaluated and resuscitated in the ICU.

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.

Search for proteins with similarity to the CFTR R domain using an optimized RDBMS solution, mBioSQL

The cystic fibrosis transmembrane conductance regulator (CFTR) comprises ATP binding and transmembrane domains, and a unique regulatory (R) domain not found in other ATP binding cassette proteins. Phosphorylation of the R domain at different sites by PKA and PKC is obligatory for the chloride channel function of CFTR. Sequence similarity searches on the R domain were uninformative. Furthermore, R domains from different species show low sequence similarity. Since these R domains resemble each other only in the location of the phosphorylation sites, we generated different R domain patterns masking amino acids between these sites. Because of the high number of the generated patterns we expected a large number of matches from the UniProt database. Therefore, a relational database management system (RDBMS) was set up to handle the results. During the software development our system grew into a general package which we term Modular BioSQL (mBioSQL). It has higher performance than other solutions and presents a generalized method for the storage of biological result-sets in RDBMS allowing convenient further analysis. Application of this approach revealed that the R domain phosphorylation pattern is most similar to those in nuclear proteins, including transcription and splicing factors.

Abdominal compartment syndrome

PURPOSE OF REVIEW

This review will set forth the new consensus definitions for intra-abdominal pressure, intra-abdominal hypertension, and the abdominal compartment syndrome from the World Congress on the Abdominal Compartment Syndrome in December 2004. The review will explore the challenges in diagnosis, pathophysiology, and recent concepts in the treatment of abdominal compartment syndrome.

RECENT FINDINGS

Intra-abdominal pressure greater than 12 mm Hg may exert adverse physiologic sequelae, progressing to intra-abdominal hypertension and full-blown abdominal compartment syndrome as intra-abdominal pressure increases. The first challenge is to recognize that abdominal compartment syndrome may be a potential problem in critically ill patients. Intra-abdominal pressure monitoring is essential for this. Continuous monitoring of intra-abdominal pressure and abdominal perfusion pressure adds real-time measurements and can be performed by way of the stomach or bladder. Intra-abdominal hypertension occurs in approximately 35% of patients in the intensive care unit, and abdominal compartment syndrome in approximately 5%.

SUMMARY

Massive resuscitation is increasingly recognized as a major contributor to abdominal compartment syndrome. Prophylactic decompression and temporary abdominal closure have important roles in preventing tertiary or recurrent abdominal compartment syndrome. Failure to recognize and treat intra-abdominal hypertension will result in increased risk of renal impairment, visceral and intestinal ischemia, respiratory failure and death.

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.

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.