Machine Learning to Predict Delayed Cerebral Ischemia and Outcomes in Subarachnoid Hemorrhage

Objective To determine whether machine learning (ML) algorithms can improve the prediction of delayed cerebral ischemia (DCI) and functional outcomes after subarachnoid hemorrhage (SAH). Methods ML models and standard models (SMs) were trained to predict DCI and functional outcomes with data collected within 3 days of admission. Functional outcomes at discharge and at 3 months were quantified using the modified Rankin Scale (mRS) for neurologic disability (dichotomized as good [mRS <= 3] vs poor [mRS >= 4] outcomes). Concurrently, clinicians prospectively prognosticated 3-month outcomes of patients. The performance of ML, SMs, and clinicians were retrospectively compared. Results DCI status, discharge, and 3-month outcomes were available for 399, 393, and 240 participants, respectively. Prospective clinician (an attending, a fellow, and a nurse) prognostication of 3-month outcomes was available for 90 participants. ML models yielded predictions with the following area under the receiver operating characteristic curve (AUC) scores: 0.75 +/- 0.07 (95% confidence interval [CI] 0.64-0.84) for DCI, 0.85 +/- 0.05 (95% CI 0.75-0.92) for discharge outcome, and 0.89 +/- 0.03 (95% CI 0.81-0.94) for 3-month outcome. ML outperformed SMs, improving AUC by 0.20 (95% CI -0.02 to 0.4) for DCI, by 0.07 +/- 0.03 (95% CI -0.0018 to 0.14) for discharge outcomes, and by 0.14 (95% CI 0.03-0.24) for 3-month outcomes and matched physician's performance in predicting 3-month outcomes. Conclusion ML models significantly outperform SMs in predicting DCI and functional outcomes and has the potential to improve SAH management.