Epicardial Adipose Tissue Thickness and Its Association With the Presence and Severity of Coronary Artery Disease in Clinical Setting: A Cross-Sectional Observational Study
Bibliographic record
Abstract
BACKGROUND: Obesity is an important risk factor for atherosclerotic cardiovascular disease (ASCVD). Estimation of visceral adipose tissue is important and several methods are available as its surrogate. Although correlation of epicardial adipose tissue (EAT) with visceral adipose tissue as estimated by magnetic resonance imaging (MRI) and/or CT is excellent, it is costlier and cumbersome. EAT can be accurately measured by two-dimensional (2D) echocardiography. It tends to be higher in patients with acute coronary syndrome than in subjects without coronary artery disease (CAD) and in those with stable angina. It also carries advantage as index of high cardiometabolic risk as it is a direct measure of visceral fat rather than anthropometric measurements. The present study evaluated the relationship of EAT to the presence and severity of CAD in clinical setting. METHODS: In this prospective, single-center study conducted in the Department of Cardiology, LPS Institute of Cardiology, Kanpur, India, 549 consecutive patients with acute coronary syndrome or chronic stable angina were enrolled. Sensitivity, specificity, and receiver operating characteristic (ROC) curve were estimated to find cut-off value of EAT thickness for diagnosing CAD using coronary angiographic findings as gold standard. RESULTS: Patients were diagnosed as CAD group (n = 464, 60.30 ± 8.36 years) and non-CAD group (n = 85, 54.42 ± 11.93 years) after assessing coronary angiograms. The EAT was measured at end-systole from the PLAX views of three cardiac cycles on the free wall of the right ventricle. Lesion was significant if > 50% in left main and > 70% in other coronary arteries. The mean EAT thickness in CAD group was 5.10 ± 1.06 and in non-CAD group was 4.36 ± 1.01 which was significant (P = 0.003). Significant correlation was demonstrated between EAT thickness and presence of CAD (P < 0.003). Higher EAT was associated with severe CAD and presence of multivessel disease. By ROC analysis, EAT > 4.65 mm predicated the presence of significant coronary stenosis by 71.6% sensitivity and 73.1% specificity. CONCLUSION: EAT thickness measured using transthoracic echocardiography (TTE) significantly correlates with the presence and severity of CAD. It is sensitive, easily available, and cost-effective and assists in the risk stratification and may be an additional marker on classical risk factors for CAD.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".