Combat casualties undergoing lifesaving interventions have decreased heart rate complexity at multiple time scales
- PMID: 24140167
- PMCID: PMC4018756
- DOI: 10.1016/j.jcrc.2013.08.022
Combat casualties undergoing lifesaving interventions have decreased heart rate complexity at multiple time scales
Abstract
Purpose: We found that heart rate (HR) complexity metrics such as sample entropy (SampEn) identified patients with trauma receiving lifesaving interventions (LSIs). We now aimed (1) to test a multiscale entropy (MSE) index, (2) to compare it to single-scale measures including SampEn, and (3) to assess different parameter values for calculation of SampEn and MSE.
Methods: This was a study of combat casualties in an emergency department in Iraq. Electrocardiograms of 70 acutely injured adults were recorded. Twelve underwent LSIs and 58 did not. Lifesaving interventions included endotracheal intubation (9), tube thoracostomy (9), and emergency transfusion (4). From each electrocardiogram, a segment of 800 consecutive beats was selected. Offline, R waves were detected and R-to-R interval time series were generated. Sample entropy, MSE, and time-domain measures of HR variability (mean HR, SD, the proportion of pairs of consecutive NN intervals that differ by more than 20 and 50 milliseconds, square root of the mean of the squares of differences between adjacent NN intervals) were computed.
Results: Differences in mean HR (LSI: 111 ± 33, non-LSI: 90 ± 17 beats/min) were not significant. Systolic arterial pressure was statistically but not clinically different (LSI: 123 ± 19, non-LSI: 135 ± 19 mm Hg). Sample entropy (LSI: 0.90 ± 0.42, non-LSI: 1.19 ± 0.35; P < .05) and MSE index (LSI: 2.58 ± 2.55, non-LSI: 5.67 ± 2.48; P < .001) differed significantly.
Conclusions: Complexity of HR dynamics over a range of time scales was lower in high-risk than in low-risk combat casualties and outperformed traditional vital signs.
Keywords: Electrocardiography; Entropy; Heart rate; Iraq War 2003; Nonlinear dynamics; Wounds and injuries.
© 2013.
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References
-
- Martin M, Oh J, Currier H, Tai N, Beekley A, Eckert M, et al. An analysis of in-hospital deaths at a modern combat support hospital. J Trauma. 2009;66:S51–S60. - PubMed
-
- Butler FK. Tactical combat casualty care: update 2009. J Trauma. 2010;69:S10–S13. - PubMed
-
- Pincus SM, Goldberger AL. Physiological time-series analysis: what does regularity quantify? Am J Physiol. 1994;266:H1643–H1656. - PubMed
-
- Richman JS, Moorman JR. Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology - Heart & Circulatory Physiology. 2000;278:H2039–H2049. - PubMed
-
- Batchinsky AI, Cooke WH, Kuusela T, Cancio LC. Loss of complexity characterizes the heart rate response to experimental hemorrhagic shock in swine. Crit Care Med. 2007;35:519–525. - PubMed
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