Machine learning–based prediction of 3–6-month post-stroke cognitive impairment using acute-phase clinical data: a two-center retrospective prognostic modeling study

Post-stroke cognitive impairment (PSCI) is common and disabling, but identifying patients at risk early remains difficult. We developed and externally validated a retrospective machine-learning prognostic model using acute-phase predictors collected within 1 week after stroke onset to estimate PSCI risk assessed at 3–6 months after stroke. Two inpatient rehabilitation cohorts were retrospectively analyzed: Center 1 ( n = 1,369; training/internal test) and Center 2 ( n = 341; external validation). Cognitive status at 3–6 months was the outcome. Features were selected with Boruta and LASSO; eight algorithms were trained with 10-fold cross-validation. Performance was assessed by AUROC, average precision (AP), accuracy, sensitivity, specificity, precision, F1, calibration (Brier score, calibration intercept, calibration slope and plots), and decision-curve analysis (DCA). SHAP was used for interpretability, and a web-based proof-of-concept prototype was developed to display model-derived risk estimates. Ten predictors were ultimately selected. In external validation, XGBoost achieved the highest AUROC (0.827), with an average precision of 0.6668, accuracy of 0.8328, sensitivity of 0.6250, specificity of 0.9241, precision of 0.7831, and an F1-score of 0.6952. Calibration was acceptable (Brier score = 0.1366; calibration intercept = − 0.027; calibration slope = 0.916), and DCA suggested positive net benefit within the 15%–40% threshold-probability range. The largest SHAP contributors to the XGBoost model output included diabetes, NIHSS, Hgb, Hcy, apoA1, TG, ALB, WMH, smoking, and age. This model may support PSCI risk stratification in post-stroke patients admitted for inpatient rehabilitation. However, further validation in broader and more representative stroke populations is required before wider application.