Prediction of Early Functional Outcome After Acute Ischemic Stroke Using Real-World Clinical Data in Vietnam and Indonesia: Retrospective Cohort Study

Accurate prediction of early functional outcome after acute ischemic stroke is critical for clinical decision-making. This retrospective cohort study developed and externally validated a machine learning model using routine clinical data from two settings. A total of 11,911 ischemic patients from the 2023 Stroke Care Quality (RES-Q) registry in 52 hospitals across Vietnam and 83 patients from UGM Academic Hospital, Indonesia, were included. The primary outcome was discharge modified Rankin Scale (mRS≤2=favorable; >2 = poor). The XGBoost model achieved strong internal discrimination (AUC=0.905, F1=0.836) and maintained robust external performance (AUC=0.888, F1=0.810). SHAP interpretability identified pre-stroke mRS, admission NIHSS score, first-day glucose check, and age as the strongest predictors of poor functional outcome, while early dysphagia screening, physiotherapy evaluation, and small-vessel etiology were associated with better recovery. These results demonstrate that a data-driven AI approach using routine clinical parameters can achieve reliable cross-regional generalization and may support early prognostication in diverse stroke care settings.