Heart Rate Variability as a Predictor of Death in Burn Patients

Abstract

Objective Heart rate variability (HRV), a noninvasive technique used to quantify fluctuations in the interval between normal heartbeats, is a predictor of mortality in some patient groups. The aim of our study was to assess HRV in burn trauma patients as a predictor of mortality. Methods We 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. Results A total of 40 burn patients with a mean age of 44 ± 15 years were enrolled. Mean % total body surface area burn was 27 ± 22% for the overall population and was significantly higher in those who died compared to those who survived (55 ± 23% vs. 19 ± 13%, p<0.0001). There was a statistically significant inverse linear correlation between standard deviation of NN intervals (SDANN) and % total body surface area (-r 0.337, R2=0.113, 95% CI −0.587 to −0.028, 2-tailed p=0.034), as well as with ultra low frequency (ULF) power and % total body surface area burn (−r 0.351, R2= 0.123, 95% CI −0.152 to −0.009, p=0.027). The receiver-operator characteristic showed the area under the curve for % total body surface area as a predictor of death was 0.82 (p<0.001), for SDANN was 0.94 (p<0.0001), and for ULF power was 0.96 (p<0.0001). Conclusions Deranged HRV in the early post-burn period is a strong predictor of death.

Association between non-acute traumatic injury (TI) and heart rate variability (HRV) in adults: A systematic review protocol

“…The examination of the impact of TI on HRV has the potential to bridge the research gap. Heart rate variability is a unique noninvasive marker of autonomic balance that has been strongly linked to adverse health outcomes including MACE [17] across a broad spectrum of patient populations [14][15][16]. While the effects of acute TI on HRV have been studied [18][19][20][21], there appears to be a paucity of research on the longer terms impacts of non-acute TI and HRV, in particular beyond that of mild traumatic brain injury [24] and spinal cord injury [25].…”

“…The Axis checklist has 20 questions with response of either a yes (1) or a no (0) [32]. Study quality will be further categorized as low (<10), moderate (10)(11)(12)(13)(14)(15) and high (>15) as previously used [33]. The final quality assessment score of each study will be documented and displayed on the quality appraisal table.…”

“…Owing to HRV's ability to indicate somatic tissue damage before the onset of pain and the injury [9], HRV has also been proposed as a new vital sign and triage tool in trauma settings [10][11][12]. In addition to being an objective marker of autonomic function [13], HRV has also been shown to be a robust marker of injury severity and as a predictor of morbidity and mortality in patients with severe traumatic injuries to head, neck, torso, abdomen [14], burns [15], and Traumatic Brain Injury (TBIs) [16]. With this prognostic and diagnostic value, HRV has emerged as an increasingly useful measure of physical fitness and the identification of overtraining.…”

Cardiac disorders in burn injury

“…Нередко наблюдается увеличение дисперсии интервала QT, причем отмечается тесная корреляция этого показателя с тяжестью поражения сердца. Что касается вариабельности сердечного ритма, определяемого при суточном мониторировании ЭКГ, установлено, что отклонение показателей от нормы у пациентов в ранний послеожоговый период является мощным предиктором смертельного исхода [19,20]. При ожоговой травме наблюдается угнетение волновой структуры сердечного ритма и показателей общей мощности спектра [21].…”

A count data model for heart rate variability forecasting and premature ventricular contraction detection

“…Another time domain feature, the standard deviation of normal to normal intervals(SDNN) varies less with heart rate fluctuations and represents the over-all (both long-term 24 hours and short-term 5 minutes) variation within the RR interval series [6]. SDNN has proven to be important clinically as it allows risk identification of adverse cardiac events [39] [13] [23]. Other time domain measures, include NN50, the number of successive cycles differing more than 50ms.…”