ArticleAmerican journal of preventive cardiology2026
AI-CVD-HF: A heart failure risk prediction model based on coronary artery calcium scans compared with PREVENT-HF.
Article in American journal of preventive cardiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- From coronary calcium to heart failure risk: Expanding the role of CT in preventive cardiology.American journal of preventive cardiology · 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
31 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The AI-CVD initiative seeks to extract actionable information from coronary artery calcium (CAC) scans beyond the CAC score. We aimed to develop a heart failure (HF) prediction model, AI-CVD-HF, based on AI-derived features from non-contrast CAC scans, and compare it with PREVENT-HF. Method: AI features from CAC scans of 6743 asymptomatic participants in the Multi-Ethnic Study of Atherosclerosis (MESA) and Framingham Heart Study-Offspring (FHS-O) (mean age 62.3 ± 10.1; 47.2% male; median follow-up 17.1 years; 429 HF events) were analyzed. Features were selected using random forest and modeled with random survival forest. Performance was assessed against the base PREVENT-HF using 5-fold cross-validation for area under the receiver-operating-characteristic curve (AUC), area under the precision-recall curve (AUPRC), net-benefit, and calibration. External validation experiment was conducted to assess generalizability. Results: Selected features for AI-CVD-HF model included AI-CAC score, left-to-right ventricular volume ratio, left atrial volume, left ventricular mass, visceral fat volume, skeletal muscle mean density, thoracic aortic calcification, age and sex. The 10-year AUC of AI-CVD-HF was 0.83 (95% CI:0.81-0.85), compared with PREVENT-HF (0.79, 95% CI:0.75-0.83; Conclusions: AI-CVD-HF, using AI-derived features from CAC scans, demonstrated more favorable discrimination and calibration than PREVENT-HF for HF prediction, extending the utility of CAC scans beyond coronary artery disease risk assessment.
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.