Interpretable machine learning model for imaging-based outcome prediction after cardiac arrest

Early identification of brain injury patterns in computerized tomography (CT) imaging is crucial for post-cardiac arrest prognostication. Lack of interpretability of machine learning prediction reduces trustworthiness by clinicians and prevents translation to clinical practice. We aimed to identify CT imaging patterns associated with prognosis with interpretable machine learning.

In this IRB-approved retrospective study, we included consecutive comatose adult patients hospitalized at a single academic medical center after resuscitation from in- and out-of-hospital cardiac arrest between August 2011 and August 2019 who underwent unenhanced CT imaging of the brain within 24 hours of their arrest. We decomposed the CT images into subspaces to identify interpretable and informative patterns of injury, and developed machine learning models to predict patient outcomes (i.e., survival and awakening status) using the identified imaging patterns. Practicing physicians visually examined the imaging patterns to assess clinical relevance. We evaluated machine learning models using 80%-20% random data split and reported AUC values to measure the model performance.

We included 1284 subjects of whom 35% awakened from coma and 34% survived hospital discharge. Our expert physicians were able to visualize decomposed image patterns and identify those believed to be clinically relevant on multiple brain locations. For machine learning models, the AUC was 0.710 ± 0.012 for predicting survival and 0.702 ± 0.053 for predicting awakening, respectively.

We developed an interpretable method to identify patterns of early post-cardiac arrest brain injury on CT imaging and showed these imaging patterns are predictive of patient outcomes (i.e., survival and awakening status).