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.

This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).