Abstract Body: Objective: Approximately 10%-17% of patients with a recent small subcortical infarction (RSSI) experience an adverse functional prognosis at three months. Identifying risk factors for poor prognosis in these patients and developing a predictive model are of significant clinical relevance.
Methods: We conducted a prospective cohort study involving 630 patients with recent small subcortical infarction (RSSI). These patients were divided into training (70%) and internal validation (30%) cohorts. Additionally, for external validation, 103 patients from two other hospitals were included. To predict functional outcomes at three months, we employed eight different machine learning algorithms: Support Vector Machine, Decision Tree, Random Forest, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting Machine, Cat Boosting, and Logistic Regression. The interpretability of the best-performing model was enhanced with SHapley Additive exPlanations(SHAP).
Results: Among the 630 patients included in the study, the average age of participants was 59.0 ±13.0 years and 65 (10.3%) exhibited poor functional outcomes at three months. The Cat Boosting model showed the highest performance among eight machine learning models. The final model could accurately predict outcome in both internal (AUC = 0.968) and external (AUC = 0.856) validations. SHAP analysis indicated that the initial NIHSS score was the most important variable. The web application is available at https://yrssiml.streamlit. app.
Conclusions: Our research developed a predictive model using the Cat Boosting algorithm to forecast poor functional outcomes at three months in patients with RSSI. This model enables clinicians to identify patients at high risk and implement targeted interventions more effectively.