ArticleJournal of the American College of Cardiology2026
Opportunistic Cardiovascular Risk Assessment Using Routine Head CT in the Emergency Department.
Article in Journal of the American College of Cardiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Multi-task artificial intelligence annotation of echocardiographic images: a retrospective multi-cohort study.medRxiv : the preprint server for health sciences · 2026Article
- Machine learning and multi-omics technologies for precision cardiovascular medicine: advancing diagnosis, risk prediction, and therapeutic guidance.Frontiers in cardiovascular medicine · 2026Review
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9 authors.
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Abstract
backgroundRoutine noncardiac computed tomography (CT) imaging may contain information about cardiovascular risk. Head computed tomography (CTH) is among the most common imaging studies, conducted annually in millions of patients. Its utility for cardiovascular risk assessment has not been studied.
objectivesThe purpose of this study was to develop and validate deep learning models for predicting incident cardiovascular disease (CVD) and estimating coronary artery calcium (CAC) scores from CTH, and assess performance against clinical risk factors.
methodsThis retrospective cohort study used data from the Stanford Health Care Emergency Department from August 2020 to August 2024. The CVD cohort comprised 27,990 adult patients without known CVD who underwent CTH. The CAC cohort included 2,313 patients who underwent both CTH and coronary CT angiography. Imaging features were extracted from CTH using pretrained deep learning models. Other risk factors were extracted from electronic health records. Outcomes were incident CVD complications (myocardial infarction, stroke, heart failure) and CAC scores (0, 1-10, 11-100, 101-400, >400 AU). Performance was evaluated using the concordance index (C-index) and area under the receiver operating characteristic curve, compared against the baseline model using the variables of the American Heart Association PREVENT (Predicting Risk of cardiovascular disease EVENTs) risk model.
resultsFour percent (1,110 of 27,990) of patients (median age 63.0 years, 51.7% female) experienced cardiovascular events. The CTH model achieved a C-index of 0.82 (95% CI: 0.78-0.85) compared with PREVENT (0.75; 95% CI: 0.70-0.79) with difference of 0.07 (95% CI: 0.04-0.10). For CAC estimation (n = 2,313, median age 65.0 years, 53.5% female), the CTH+PREVENT model achieved a C-index of 0.76 (95% CI: 0.72-0.80) and area under the receiver operating characteristic curve of 0.80 (95% CI: 0.73-0.85) for CAC >100. Of the patients, 15.7% were reclassified; higher-risk patients were younger but with higher prevalence of vascular calcifications (30.2% vs 24.8%, P = 0.001) and brain infarcts (20.1% vs 5.8%, P < 0.001).
conclusionsRoutine CTH scans complement traditional risk factors for cardiovascular risk stratification, identifying subclinical disease in younger patients with favorable risk profiles. Clinical integration could improve CVD detection and prevention without additional costs or radiation.
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