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.