• Mohammad M Ghassemi, Edilberto Amorim, Tuka Alhanai, Jong W Lee, Susan T Herman, Adithya Sivaraju, Nicolas Gaspard, Lawrence J Hirsch, Benjamin M Scirica, Siddharth Biswal, Valdery Moura Junior, Sydney S Cash, Emery N Brown, Roger G Mark, M Brandon Westover, and Critical Care Electroencephalogram Monitoring Research Consortium.
• Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA.
• Crit. Care Med. 2019 Oct 1; 47 (10): 1416-1423.

ObjectivesElectroencephalogram features predict neurologic recovery following cardiac arrest. Recent work has shown that prognostic implications of some key electroencephalogram features change over time. We explore whether time dependence exists for an expanded selection of quantitative electroencephalogram features and whether accounting for this time dependence enables better prognostic predictions.DesignRetrospective.SettingICUs at four academic medical centers in the United States.PatientsComatose patients with acute hypoxic-ischemic encephalopathy.InterventionsNone.Measurements And Main ResultsWe analyzed 12,397 hours of electroencephalogram from 438 subjects. From the electroencephalogram, we extracted 52 features that quantify signal complexity, category, and connectivity. We modeled associations between dichotomized neurologic outcome (good vs poor) and quantitative electroencephalogram features in 12-hour intervals using sequential logistic regression with Elastic Net regularization. We compared a predictive model using time-varying features to a model using time-invariant features and to models based on two prior published approaches. Models were evaluated for their ability to predict binary outcomes using area under the receiver operator curve, model calibration (how closely the predicted probability of good outcomes matches the observed proportion of good outcomes), and sensitivity at several common specificity thresholds of interest. A model using time-dependent features outperformed (area under the receiver operator curve, 0.83 ± 0.08) one trained with time-invariant features (0.79 ± 0.07; p < 0.05) and a random forest approach (0.74 ± 0.13; p < 0.05). The time-sensitive model was also the best-calibrated.ConclusionsThe statistical association between quantitative electroencephalogram features and neurologic outcome changed over time, and accounting for these changes improved prognostication performance.

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