Seizure prediction in 1117 neonates leveraging EMR-embedded standardized EEG reporting

Abstract

Background Accurate prediction of seizures can help direct resource-intense continuous EEG (CEEG) monitoring to high-risk neonates. We aimed to use data extracted from standardized EEG reports to generate seizure prediction models for vulnerable neonates. Methods In 2018, we implemented a novel CEEG reporting system in the electronic medical record (EMR) that incorporated standardized terminology. We developed seizure prediction models using logistic regression, decision tree, and random forest models for neonates and specifically, neonates with hypoxic-ischemic encephalopathy (HIE), using EEG features on day 1 to predict future seizures. Findings We evaluated 1117 neonates, including 150 neonates with HIE, with CEEG data reported using standardized templates. Implementation of a consistent EEG reporting system, which documents discrete and standardized EEG variables, resulted in >95% reporting of key EEG features. Several EEG features were highly correlated, and patients could be clustered based on specific features. However, no simple combination of features adequately predicted seizure risk. We therefore applied computational models to complement clinical identification of high-risk neonates. Random forest models incorporating background features performed with classification accuracies of up to 90% for all neonates and 97% for neonates with HIE, and recall (sensitivity) of up to 97% for all neonates and >99% for neonates with HIE. Interpretation Using data extracted from the standardized EEG report on the first day of CEEG, we predict the presence or absence of neonatal seizures on subsequent days with classification performances of >90%. This information, incorporated into routine care, can guide decisions about the necessity of continuing CEEG beyond the first day and thereby improve the allocation of limited CEEG resources. Additionally, this analysis illustrates the benefits of standardized clinical data collection which can drive learning health system approaches to personalized CEEG utilization.