Prospective Validation of Neurophysiologic Outcome Prediction in Acute Brain Injury

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PROJECT SUMMARY Acute brain injury causes over 163,000 deaths in the US annually and leaves many more patients with long- term disability. Preventing secondary brain injury is critical to improving neurologic outcomes in these patients. Pathological brain electrical activity (measured via EEG) following acute brain injury contributes to long-term disability via seizures in the acute phase and epilepsy in the chronic phase. EEG is the primary tool for monitoring aberrant brain activity, yet it is underutilized due to uncertainty regarding the clinical significance of EEG findings and the high workload associated with interpreting high volumes of EEG data. Our group has made progress toward addressing these short-comings by developing three novel machine learning algorithms: 1) a seizure forecasting model for hospitalized patients (“2HELPS2B”); 2) a model that measures the Burden of Epileptiform Activity (EA—seizures and highly epileptiform patterns such as lateralized periodic discharges) to predict neurologic outcomes (“BEACON”); and 3) A model that uses EA in the acute phase of brain injury to predict the risk of developing epilepsy. This proposal is the culminating step required to translate preliminary studies into actionable clinical tools. In this prospective multicenter observational study, we will collect clinical and EEG data on 3000 patients with acute brain injury to further develop and validate these models. Our study leverages an existing multicenter bioinformatics infrastructure and established collaborations between Yale University, University of Wisconsin, and Massachusetts General Hospital, to create the scale and quality of data needed to address three specific aims: SA1: Develop and prospectively validate an automated in-hospital seizure-forecasting model for use in acute brain injury, based on our previously developed 2HELPS2B score—termed auto-2HELPS2B; SA2: Prospectively validate the BEACON model for the impact of prolonged epileptiform activity on functional and clinical outcomes in critical illness at discharge, 3 months, 6 months, 1-year, and 2-years, and estimate the optimal anti-seizure drug administration strategy (indications and intensity of drugs) to mitigate detrimental effects of EA on functional and clinical outcomes; SA3: Prospectively validate EAs as a biomarker of 1- and 2- year epilepsy risk after acute brain injury, evaluate effects of anti-seizure drugs on acute phase EAs and subsequent development of epilepsy, and combine EEG with radiographic and clinical information to further improve our current epilepsy forecasting risk model. Upon completing these aims, we will have 1. A large and representative database of high-quality EEG and clinical data on brain-injured patients with 2-year of outcomes data. 2. Validated EEG tools to guide care in determining acute seizure risk, prognosis for neurological recovery, and the likelihood of developing epilepsy.