Machine Learning-Based Risk Stratification for Symptomatic Intracranial Hemorrhage and 3-Month Prognosis following...

Intravenous thrombolysis using tissue-type plasminogen activator (tPA) is widely accepted as a fundamental therapy for acute ischemic stroke. However, its clinical benefit is counterbalanced by the risk of symptomatic intracranial hemorrhage (sICH), a serious complication that can substantially worsen patient outcomes and increase mortality. Functional outcome after stroke is most commonly assessed using the modified Rankin Scale at 3 months. In this study, we sought to construct predictive models for sICH and 3-month outcomes after tPA and to identify key prognostic variables that may support individualized treatment decisions.

We analyzed data from 434 patients with ischemic stroke who received intravenous tPA at a tertiary medical center over a 5.5-year period. Three supervised classification models were constructed and validated using five-fold cross-validation. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, recall, and precision, and the results were compared with those of six commonly used clinical scoring systems for predicting post-tPA sICH.

All three machine learning models showed strong discriminatory performance for sICH prediction, with AUC values of 0.87 for logistic regression, 0.82 for random forest, and 0.89 for XGBoost. Each model consistently outperformed the six conventional scoring tools. Among all variables, the 24-h NIHSS score contributed most strongly to the prediction of both sICH and 3-month functional outcomes. In addition, a prior history of stroke and male sex were associated with an increased risk of sICH, whereas older age was closely linked to worse functional outcomes at 3 months.

The proposed machine learning models achieved high predictive performance for post-tPA sICH and 3-month outcomes and identified key clinical variables with substantial prognostic importance. Notably, the 24-h NIHSS score emerged as the most influential predictor across models. Compared with existing sICH scoring systems, these models demonstrated superior performance, supporting their potential role as clinical decision-support tools in post-thrombolysis management.