Outcome prognostication in comatose patients after cardiac arrest (CA) remains a major challenge. Here we investigated the prognostic value of combinations of linear and non-linear bivariate EEG synchronization measures.
94 comatose patients with EEG within 24h after CA were included. Clinical outcome was assessed at 3months using the Cerebral Performance Categories (CPC). EEG synchronization between the left and right parasagittal, and between the frontal and parietal brain regions was assessed with 4 different quantitative measures (delta power asymmetry, cross-correlation, mutual information, and transfer entropy). 2/3 of patients were used to assess the predictive power of all possible combinations of these eight features (4 measures×2 directions) using cross-validation. The predictive power of the best combination was tested on the remaining 1/3 of patients.
The best combination for prognostication consisted of 4 of the 8 features, and contained linear and non-linear measures. Predictive power for poor outcome (CPC 3-5), measured with the area under the ROC curve, was 0.84 during cross-validation, and 0.81 on the test set. At specificity of 1.0 the sensitivity was 0.54, and the accuracy 0.81.
Combinations of EEG synchronization measures can contribute to early prognostication after CA. In particular, combining linear and non-linear measures is important for good predictive power.
Quantitative methods might increase the prognostic yield of currently used multi-modal approaches
