Improving the Prediction of Mortality and the Need for Life-Saving Interventions in Trauma Patients Using Standard Vital Signs With Heart-Rate Variability and Complexity

Abstract

The goal of this study was to determine the effectiveness of using traditional and new vital signs (heart rate variability and complexity [HRV, HRC]) for predicting mortality and the need for life-saving interventions (LSIs) in prehospital trauma patients. Our hypothesis was that statistical regression models using traditional and new vital signs would be superior in predictive performance over models using standard vital signs alone. This study involved 108 prehospital trauma patients transported from the point of injury via helicopter. Heart rate variability and HRC were calculated using criterion standard R-R interval sequences manually verified from the patients' electrocardiograms. Means and standard deviations for vital signs, HRV, HRC, and Glasgow coma scale (GCS) scores were obtained for nonsurvivors versus survivors and LSI versus non-LSI patient groups and then compared using Wilcoxon statistical tests. Receiver-operating characteristic curves were also obtained to compare different regression models for predicting mortality and the need for LSIs. Seventeen patients (16%) died. Eighty-two patients (76%) received a total of 142 LSIs. Receiver-operating characteristic curves demonstrated better prediction of mortality and LSI needs using heart rate and HRC (area under the curve [AUC]; AUCs, 0.86 and 0.86) than using heart rate alone (AUCs, 0.79 and 0.57). Likewise, receiver-operating characteristic curves demonstrated better prediction using total GCS score and HRC (AUCs, 0.82 and 0.97) than using total GCS score (AUCs, 0.81 and 0.91). Similar results were obtained for heart rate and HRV (AUCs, 0.86 and 0.73). The major implication of this study was that traditional and new vital signs (HRV and HRC) should be used simultaneously to improve prediction of mortality and the need for LSIs in prehospital trauma patients during all echelons of trauma care. Improvements in the timely use and diagnostic accuracy of transportable vital signs monitors will require use of traditional and new vital signs from the trauma patient cohort.

Autonomic impairment of patients in coma with different Glasgow coma score assessed with heart rate variability

“…Reduced HRV has been previously observed and reported by us [13,15,16] and by many other authors that have investigated patients in coma with brain lesions of different etiologies [6, 7, 10-12, 14, 18, 21, 26]. Several studies have demonstrated the potential utility of HRV as an indicator of mortality in intensive care unit patients [24,25,[28][29][30][31][32], and also as an indicator of prediction of lifesaving interventions in patients with trauma [22,[33][34][35][36][37]. However, to our knowledge, this study is the first reporting this issue by applying a novel method for the analysis of HRV indices, the so called Hilbert-Huang method, that has been developed for the digital processing of signals that contain non-Gaussian, nonlinear or non-stationary processes, as is the case of the cardiac R-R tachograms, from which are derived the HRV indices [63,68,69,74,[91][92][93] Significant differences have been found of HRV indices between survivors and nonsurvivors in several studies.…”

“…It has been shown that the ANS functional state predicts responsiveness in disorders of consciousness [5]. Multiple indices of HRV calculated in the time, frequency or informational domains have been studied in patients in coma with different objectives: to assess brainstem dysfunction [6], to assess brain death [7][8][9], differentiation of autonomic nervous activity in different stages of coma [10], diagnosis of dysautonomia after traumatic brain injuries (TBI) [11], calculation and description of HRV indices of patients in coma [12] correlation of the HRV parameters with the Glasgow coma score (GCS) [13][14][15][16], prediction or prognosis of the outcome in TBI, acute cerebral vascular disease, or critically ill neurosurgical patients [17][18][19][20][21][22][23], association of HRV indices with mortality in patients with TBI [24][25][26][27][28][29][30][31][32], and prediction of life saving interventions in patients with trauma [22,[33][34][35][36][37].…”

Evaluating performance of early warning indices to predict physiological instabilities

“…Warning indices and notification systems that use physiological measurements to produce comprehensive metrics of patient health [3,4] and identify patterns predictive of patient instabilities [5,6] are a promising tool to provide lead time prior to a critical event when effective mitigations can be taken [7]. A sampling of such methods include approaches for identifying need for life-saving interventions in trauma patients [8,9], patients at risk for septic shock in the intensive care unit [10], need for care escalation from step-down units [11–13], and potential heart failure in at-home monitoring [14,15] environments. Patient monitoring environments are known for the number of false alarms [16,17], presenting a need to balance the performance of any new monitoring index and warning system to provide timely warnings prior to an event and low false-alarm rates.…”

Low intraoperative heart rate volatility is associated with early postoperative mortality in general surgical patients: a retrospective case–control study

“…Very few studies however have examined the relationship between pre-operative heart rate variability and post-operative outcome [29]. Our observations therefore add a new temporal dimension (the intraoperative period) to the previously published data indicating that markers of autonomic dysfunction may serve as clinically useful tools in the evaluation and management of the critically ill [30][31][32][33].…”