Development and Internal Validation of a Clinical Imaging–Based Stroke Prediction Model in a Community Cohort

Background Existing stroke prediction models tend to overestimate contemporary stroke risk and inadequately incorporate neuroimaging parameters. This study aimed to develop a novel and accurate stroke prediction model for a community‐based population by integrating comprehensive neuroimaging data. Methods A prospective cohort study was conducted involving 1586 eligible participants from northern rural China. Baseline clinical and neuroimaging data were collected, with annual follow‐ups to assess incident stroke. Least absolute shrinkage and selection operator regression was used to identify key predictors, which were subsequently incorporated into a Cox proportional hazards model to develop the final predictive model. Internal validation was performed via 500 times bootstrap resampling. Model performance was evaluated using time‐dependent receiver operating characteristic curves, Brier scores, calibration plots, and decision curve analysis. Results During a mean follow‐up of 8.0 years, 54 incident strokes occurred among 1173 participants (4.6%). The final model incorporating least absolute shrinkage and selection operator–selected predictors (current smoking, diabetes, high cerebral small‐vessel disease burden, and severe intracranial artery stenosis) showed strong discrimination, with area under the curve values of 0.88 (95% CI, 0.83–0.92) for 5‐year and 0.84 (95% CI, 0.79–0.90) for 7‐year prediction. Bootstrap validation confirmed model robustness (area under the curve values, 0.87 and 0.83, respectively). The model was well calibrated (Brier scores, 0.03 at 5 years and 0.04 at 7 years), and decision curve analysis indicated favorable net clinical benefit. A clinically applicable nomogram was developed for individualized risk assessment. Conclusions The newly developed predictive model, combining clinical and neuroimaging features, provides accurate prediction of 5‐year and 7‐year stroke risk. Targeted management of diabetes, smoking, silent cerebral small‐vessel disease, and intracranial artery stenosis is essential for primary stroke prevention in community populations.