In Annals of neurology ; h5-index 85.0
OBJECTIVE : To determine the dose-response relation between epileptiform activity burden and outcomes in acutely ill patients.
METHODS : Single center retrospective analysis of 1967 neurologic, medical and surgical patients who underwent > 16 hours of continuous EEG between 2011-2017. We developed an AI algorithm to annotate 11.02 terabytes of EEG and quantify epileptiform activity burden within 72 hours of recording. We evaluated burden in 1) the first 24 hours of recording, 2) the 12-hr epoch with highest burden (peak burden), and 3) cumulative through the first 72 hours of monitoring. Machine learning was applied to estimate the effect of epileptiform burden on outcome. Outcome measure was discharge modified Rankin Scale, dichotomized as good (0-4) vs. poor (5-6).
RESULTS : Peak epileptiform burden was independently associated with poor outcomes (p<0.0001). Other independent associations included age, APACHE II, seizure on presentation, and diagnosis of hypoxic ischemic encephalopathy. Model calibration error was calculated across three strata based on the time interval between last EEG measurement (up to 72 hours of monitoring) and discharge: 1) < 5 days between last measurement and discharge: 0.0941 [CI 0.0706, 0.1191); 5-10 days between last measurement and discharge: 0.0946 [CI 0.0631, 0.1290]; > 10 days between last measurement and discharge: 0.0998 [CI 0.0698, 0.1335]. After adjusting for covariates, increase in peak epileptiform activity burden from 0% to 100% increased the probability of poor outcome by 35%.
INTERPRETATION : Automated measurement of peak epileptiform activity burden affords a convenient, consistent, and quantifiable target for future multi-center randomized trials investigating whether suppressing epileptiform activity improves outcomes. This article is protected by copyright. All rights reserved.
Zafar Sahar F, Rosenthal Eric S, Jing Jin, Ge Wendong, Tabaeizadeh Mohammad, Aboul Nour Hassan, Shoukat Maryum, Sun Haoqi, Javed Farrukh, Kassa Solomon, Edhi Muhammad, Bordbar Elahe, Gallagher Justin, Moura Valdery, Ghanta Manohar, Shao Yu-Ping, An Sungtae, Sun Jimeng, Cole Andrew J, Westover M Brandon
Critical care, EEG, Seizures, Status epilepticus, machine learning, outcomes research