P.082 Using machine-based learning to predict neonatal hypoxic-ischemic encephalopathy (HIE) severity based on patient-specific clinical factors

Abstract

Background: Severity of HIE is based on Sarnat classification; however, it is difficult to predict precise neurodevelopmental outcomes as this only provides a single snapshot in time. We aimed to use machine-based learning to better understand variables contributing towards HIE severity. Methods: Patients with HIE treated with hypothermia were studied between 2014 and 2020 at level 3 NICUs in Calgary, Alberta. Clinical information contained 23 features including specifics of clinical examination, blood work, MRI and EEG findings, and medications. Random forest models were trained to examine features most predictive of HIE severity. Results: Two hundred and six patients were eligible. By grouping patients based on the initial Sarnat score and post-cooling exam, features correctly predicted groups 43% and 73% of the time, respectively. Precision, accuracy, and recall was best for the mild group. Using MRI and day 1 seizures it was 54% and 67% predictive, respectively. Features contributing most included arterial pH, initial lactate, and overall EEG findings. There are ongoing analyses for further classification. Conclusions: Machine-based learning can improve predictive models for patient outcomes. There is benefit in using variables outside of the initial examination to improve classification. We aim to expand this model to include detailed neurodevelopmental outcomes to improve prognostication.