Abstract Background and aims Stroke recurrence is clinically heterogeneous, yet prediction models often use binary outcomes that overlook etiologic mechanisms and severity. We aim to develop subtype-specific models for one-year recurrence and a severity-aware framework to predict fatal recurrence. Methods We analyzed data from the CASE-II registry, a prospective, multicenter stroke registry in China. For subtype-specific modeling, we implemented a two-step XGBoost approach with baseline training followed by TOAST subtype fine-tuning. For recurrence severity, we classified five outcomes based on post-recurrence mRS (no, mild, moderate, severe, fatal recurrence mRS 6) by training a multiclass XGBoost model on the same baseline predictors. Key predictors for fatal recurrence were identified via SHAP analysis and integrated into a Fine-Gray competing risk model, which was simplified into a 10-item clinical risk score. Results Among 20,756 patients, 2,919 (14.06%) experienced stroke recurrence within one year. Subtype-specific models significantly outperformed the baseline model (AUC=0.53), achieving AUCs of 0.70(CE), 0.65(LAA), 0.69(SAA), 0.89(SOE), and 0.68(SUE). For recurrence severity prediction, the multiclass model demonstrated good discrimination for fatal recurrence (AUC=0.79). The final 10-item score, incorporating age, NIHSS, and other biomarkers, stratified patients into low-(34.2% of cohort, event rate 0.49%, HR 1.0), intermediate-(51.9%, event rate 2.64%, HR 5.4), and high-(13.9%, event rate 6.96%, HR 14.6) risk groups for fatal recurrence. Conclusions Etiologic subtype stratification improves stroke recurrence prediction. Furthermore, we created a severity-aware 10-item score identifying a high-acuity population with 15-fold increased risk for fatal recurrence, facilitating triage for intensified secondary prevention following ischemic stroke. Conflict of interest
Justin et al. (Fri,) studied this question.