Abstract 4143155: Sex-Specific Coronary Plaque Characteristics and Risk of Cardiovascular Events: Insights from the ISCHEMIA Trial
Bibliographic record
Abstract
Background: Artificial-intelligence-enabled analysis of atherosclerotic plaque characteristics on coronary computed tomography angiography (CCTA) may improve sex-specific risk prediction of cardiovascular (CV) events. Aims: The aim of this study was to examine sex differences in CCTA-derived atherosclerotic plaque analysis in ISCHEMIA participants, and to identify any interaction between sex and the prognostic value of quantitative atherosclerosis parameters in predicting CV events. Methods: In a post-hoc analysis, atherosclerosis imaging quantitative CT (QCT) was performed on all available CCTA scans. Interaction analysis was performed to understand the impact of female sex on the relationship between QCT variables and the outcomes of CV death or myocardial infarction (MI), all-cause death, and MI alone over a median follow-up of 3.3 years. Results: Of the 3645 participants with CCTA available for analysis, mean age was 64 and 21% were female. Females had lower total, calcified, non-calcified, and low-density non-calcified plaque volume than males (p<0.001 for all, Table). Percent atheroma volume (PAV=total plaque volume /vessel volume x 100%) was associated with CV death or MI in all participants, and the association was stronger in females (univariate HR: 2.37, 95% confidence interval (CI) 1.79, 3.14, p<0.001) than men (HR: 1.70, 95% CI 1.45, 2.00, p<0.001). There was a significant interaction showing accentuated risk of CV death or MI associated with greater PAV among females vs. males (HR for female*PAV: 1.42, 95% CI 1.03, 1.95, p=0.032, Figure), mainly driven by risk for MI. Conclusions: In ISCHEMIA, females presenting with moderate or severe ischemia and obstructive CAD had less atherosclerotic plaque burden of all subtypes and less adverse plaque characteristics identified by CCTA than men. The risk associated with greater atherosclerotic burden (PAV) was higher among female participants than males for the composite outcome of CV death or MI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".