• Hiroshi Endoh, Natuo Kamimura, Hiroyuki Honda, and Masakazu Nitta.
• Department of Emergency & Critical Care Medicine, Niigata University Faculty of Medicine, 1-757 Asahimachi-dori, Chuo-ku, Niigata, 951-8520, Japan. endoh@med.niigata-u.ac.
• Crit Care. 2019 Oct 17; 23 (1): 323.

BackgroundMost deaths of comatose survivors of out-of-hospital sudden cardiac arrest result from withdrawal of life-sustaining treatment (WLST) decisions based on poor neurological prognostication and the family's intention. Thus, accurate prognostication is crucial to avoid premature WLST decisions. However, targeted temperature management (TTM) with sedation or neuromuscular blockade against shivering significantly affects early prognostication. In this study, we investigated whether heart rate variability (HRV) analysis could prognosticate poor neurological outcome in comatose patients undergoing hypothermic TTM.MethodsBetween January 2015 and December 2017, adult patients with out-of-hospital sudden cardiac arrest, successfully resuscitated in the emergency department and admitted to the intensive care unit of the Niigata University in Japan, were prospectively included. All patients had an initial Glasgow Coma Scale motor score of 1 and received hypothermic TTM (at 34 °C). Twenty HRV-related variables (deceleration capacity; 4 time-, 3 geometric-, and 7 frequency-domain; and 5 complexity variables) were computed based on RR intervals between 0:00 and 8:00 am within 24 h after return of spontaneous circulation (ROSC). Based on Glasgow Outcome Scale (GOS) at 2 weeks after ROSC, patients were divided into good outcome (GOS 1-2) and poor outcome (GOS 3-5) groups.ResultsSeventy-six patients were recruited and allocated to the good (n = 22) or poor (n = 54) outcome groups. Of the 20 HRV-related variables, ln very-low frequency (ln VLF) power, detrended fluctuation analysis (DFA) (α1), and multiscale entropy (MSE) index significantly differed between the groups (p = 0.001), with a statistically significant odds ratio (OR) by univariate logistic regression analysis (p = 0.001). Multivariate logistic regression analysis of the 3 variables identified ln VLF power and DFA (α1) as significant predictors for poor outcome (OR = 0.436, p = 0.006 and OR = 0.709, p = 0.024, respectively). The area under the receiver operating characteristic curve for ln VLF power and DFA (α1) in predicting poor outcome was 0.84 and 0.82, respectively. In addition, the minimum value of ln VLF power or DFA (α1) for the good outcome group predicted poor outcome with sensitivity = 61% and specificity = 100%.ConclusionsThe present data indicate that HRV analysis could be useful for prognostication for comatose patients during hypothermic TTM.

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