AI-quantified epicardial adipose tissue and prediction of future myocardial infarction in patients with cardiometabolic disease: a post-hoc analysis from the SCOT-HEART trial
Bibliographic record
Abstract
BACKGROUND: Epicardial adipose tissue is gaining increasing interest as a cardiometabolic imaging biomarker, but its exact role in coronary artery disease is not fully understood. This study aimed to investigate the relationship between epicardial adipose tissue, coronary plaque characteristics, and risk of myocardial infarction in patients with suspected coronary artery disease, and in those with concomitant cardiometabolic disease. METHODS: In a post-hoc analysis of the SCOT-HEART trial, epicardial adipose tissue volume and attenuation were quantified automatically from computed tomography (CT) angiography using deep-learning. Quantitative and high-risk coronary plaque characteristics were also assessed. The primary endpoint was fatal or non-fatal myocardial infarction. RESULTS: The study population consisted of 1770 patients (58 ± 9 years, 56% males) of whom 313 (18%) with cardiometabolic disease. Epicardial adipose tissue volume was higher in patients withcardiometabolic disease (123 ± 44 versus 88 ± 36 mL, p < 0.001), and increased with the coronary calcium score (0: 82 ± 35 mL, 1-400: 97 ± 38 mL, > 400: 113 ± 44 mL; p < 0.001), and low-attenuation plaque burden (burden ≤ 4%: 85 ± 36mL, burden > 4%: 103 ± 41mL; p < 0.001), while there were no interactions between these features and epicardial adipose tissue attenuation (p > 0.05 for all). During a median follow-up of 8.6 years, 82 (4.6%) patients experienced myocardial infarction. In the total study cohort, epicardial adipose tissue volume predicted myocardial infarction both in univariable analysis, and after adjustment for established markers of cardiovascular risk. In patients with cardiometabolic disease, epicardial adipose tissue volume independently predicted myocardial infarction after adjustment for clinical risk factors and plaque features but this relationship was not found in those without cardiometabolic disease. CONCLUSIONS: CT-derived Epicardial adipose tissue volume correlates with quantitative and high-risk plaque features, and independently predicts risk of myocardial infarction in patients with cardiometabolic disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".