Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2020
Dynamic functional connectivity of the EEG in relation to outcome of postanoxic coma.
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Keijzer H M, Tjepkema-Cloostermans M C, Klijn C J M, Blans M, van Putten M J A M, Hofmeijer J
Аннотация
OBJECTIVE: Early EEG contains reliable information for outcome prediction of comatose patients after cardiac arrest. We introduce dynamic functional connectivity measures and estimate additional predictive values.
METHODS: We performed a prospective multicenter cohort study on continuous EEG for outcome prediction of comatose patients after cardiac arrest. We calculated Link Rates (LR) and Link Durations (LD) in the α, δ, and θ band, based on similarity of instantaneous frequencies in five-minute EEG epochs, hourly, during 3 days after cardiac arrest. We studied associations of LR and LD with good (Cerebral Performance Category (CPC) 1-2) or poor outcome (CPC 3-5) with univariate analyses. With random forest classification, we established EEG-based predictive models. We used receiver operating characteristics to estimate additional values of dynamic connectivity measures for outcome prediction.
RESULTS: Of 683 patients, 369 (54%) had poor outcome. Patients with poor outcome had significantly lower LR and longer LD, with largest differences 12 h after cardiac arrest (LRθ 1.87 vs. 1.95 Hz and LDα 91 vs. 82 ms). Adding these measures to a model with classical EEG features increased sensitivity for reliable prediction of poor outcome from 34% to 38% at 12 h after cardiac arrest.
CONCLUSION: Poor outcome is associated with lower dynamics of connectivity after cardiac arrest.
SIGNIFICANCE: Dynamic functional connectivity analysis may improve EEG based outcome prediction.
METHODS: We performed a prospective multicenter cohort study on continuous EEG for outcome prediction of comatose patients after cardiac arrest. We calculated Link Rates (LR) and Link Durations (LD) in the α, δ, and θ band, based on similarity of instantaneous frequencies in five-minute EEG epochs, hourly, during 3 days after cardiac arrest. We studied associations of LR and LD with good (Cerebral Performance Category (CPC) 1-2) or poor outcome (CPC 3-5) with univariate analyses. With random forest classification, we established EEG-based predictive models. We used receiver operating characteristics to estimate additional values of dynamic connectivity measures for outcome prediction.
RESULTS: Of 683 patients, 369 (54%) had poor outcome. Patients with poor outcome had significantly lower LR and longer LD, with largest differences 12 h after cardiac arrest (LRθ 1.87 vs. 1.95 Hz and LDα 91 vs. 82 ms). Adding these measures to a model with classical EEG features increased sensitivity for reliable prediction of poor outcome from 34% to 38% at 12 h after cardiac arrest.
CONCLUSION: Poor outcome is associated with lower dynamics of connectivity after cardiac arrest.
SIGNIFICANCE: Dynamic functional connectivity analysis may improve EEG based outcome prediction.