Machine learning for predicting functional outcomes in acute ischemic stroke: insights from a nationwide stroke registry

Abstract

Accurately predicting the prognosis of patients with acute ischemic stroke at discharge remains highly challenging after active treatment. The aim of this retrospective nationwide registry-based study was to identify key predictors associated with favorable outcomes and to develop machine learning models for patient outcome prediction. Analysis of a comprehensive dataset of 40,586 patients revealed younger age (odds ratio [OR]: 0.975; 95% confidence interval [CI]: 0.972-0.977; p < 0.001), lower initial National Institutes of Health Stroke Scale score (OR: 0.862; 95% CI: 0.855-0.868; p < 0.001), mechanical thrombectomy (OR: 2.617; 95% CI: 2.185-3.134; p < 0.001), and rehabilitation therapy (OR: 2.765; 95% CI: 2.530-3.022; p < 0.001) as significant predictors of good functional outcome. We developed three machine learning models-random forest (RF), support vector machine (SVM), and logistic regression-to predict favorable functional outcomes (modified Rankin Scale score, ≤ 2) at discharge. Among these, the RF model revealed superior predictive performance, achieving an area under the curve (AUC) of 0.87, compared to the SVM and logistic regression, each achieving an AUC of 0.80. This study underscores the transformative potential of machine learning in stroke management, predicting and improving patient outcomes and streamlining healthcare delivery.

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