Objective: Early prognostication in comatose patients after cardiac arrest (CA) is difficult but essential to inform relatives and optimize treatment. Here we investigate the predictive value of heart-rate variability captured by multiscale entropy (MSE) for long-term outcomes in comatose patients during the first 24 hours after CA.
Methods: In this retrospective analysis of prospective multi-centric cohort, we analyzed MSE of the heart rate in 79 comatose patients after CA while undergoing targeted temperature management and sedation during the first day of coma. From the MSE, two complexity indices were derived by summing values over short and long time scales (CIs and CIl). We splitted the data in training and test datasets for analysing the predictive value for patient outcomes (defined as best cerebral performance category within 3 months) of CIs and CIl.
Results: Across the whole dataset, CIl provided the best sensitivity, specificity, and accuracy (88%, 75%, and 82%, respectively). Positive and negative predictive power were 81% and 84%.
Conclusions: Characterizing the complexity of the ECG in patients after CA provides an accurate prediction of both favorable and unfavorable outcomes.
Significance: The analysis of heartrate variability by means of MSE provides accurate outcome prediction on the first day of coma.
Keywords: Cardiac arrest; Central Autonomic Network; Critical Care Outcomes; Heart Rate Variability; Multiscale Entropy.
Copyright © 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.