EEG functional connectivity contributes to outcome prediction of postanoxic coma

Objective: To investigate the additional value of EEG functional connectivity features, in addition to non-coupling EEG features, for outcome prediction of comatose patients after cardiac arrest. Methods: Prospective, multicenter cohort study. Coherence, phase locking value, and mutual information were calculated in 19-channel EEGs at 12 h, 24 h and 48 h after cardiac arrest. Three sets of machine learning classification models were trained and validated with functional connectivity, EEG non-coupling features, and a combination of these. Neurological outcome was assessed at six months and cat-egorized as good (Cerebral Performance Category [CPC] 1-2) or poor (CPC 3-5). Results: We included 594 patients (46% good outcome). A sensitivity of 51% (95% CI: 34-56%) at 100% specificity in predicting poor outcome was achieved by the best functional connectivity-based classifier at 12 h after cardiac arrest, while the best non-coupling-based model reached a sensitivity of 32% (0-54%) at 100% specificity using data at 12 h and 48 h. Combination of both sets of features achieved a sensitivity of 73% (50-77%) at 100% specificity. Conclusion: Functional connectivity measures improve EEG based prediction models for poor outcome of postanoxic coma. Significance: Functional connectivity features derived from early EEG hold potential to improve outcome prediction of coma after cardiac arrest. (c) 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Automated summary

The study demonstrates the added value of EEG functional connectivity features in predicting outcomes of comatose patients after cardiac arrest. Combining different sets of features improves the sensitivity of outcome prediction models, showing the potential for early EEG-derived functional connectivity to enhance prediction of coma following cardiac arrest.