ArticleJournal of the American Heart Association2025
Development and Internal Validation of a Clinical Imaging-Based Stroke Prediction Model in a Community Cohort.
Article in Journal of the American Heart Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Differential risk profiles for geriatric depression, anxiety, and sleep disturbances in rural China: insights from the Taierzhuang cohort.Frontiers in public health · 2026Article
- Development and internal validation of a spatiotemporal gait parameter-based diagnostic model for cerebral small vessel disease.Frontiers in aging neuroscience · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundExisting 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.
methodsA 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.
resultsDuring 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.
conclusionsThe 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.