Epicardial Adipose Tissue Assessed by Computed Tomography and Echocardiography Are Associated With Adverse Cardiovascular Outcomes: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Epicardial adipose tissue (EAT) has garnered attention as a prognostic and risk stratification factor for cardiovascular disease. This study, via meta-analyses, evaluates the associations between EAT and cardiovascular outcomes stratified across imaging modalities, ethnic groups, and study protocols. Methods: Medline and Embase databases were searched without date restriction on May 2022 for articles that examined EAT and cardiovascular outcomes. The inclusion criteria were (1) studies measuring EAT of adult patients at baseline and (2) reporting follow-up data on study outcomes of interest. The primary study outcome was major adverse cardiovascular events. Secondary study outcomes included cardiac death, myocardial infarction, coronary revascularization, and atrial fibrillation. Results: Twenty-nine articles published between 2012 and 2022, comprising 19 709 patients, were included in our analysis. Increased EAT thickness and volume were associated with higher risks of cardiac death (odds ratio, 2.53 [95% CI, 1.17–5.44]; P =0.020; n=4), myocardial infarction (odds ratio, 2.63 [95% CI, 1.39–4.96]; P =0.003; n=5), coronary revascularization (odds ratio, 2.99 [95% CI, 1.64–5.44]; P <0.001; n=5), and atrial fibrillation (adjusted odds ratio, 4.04 [95% CI, 3.06–5.32]; P <0.001; n=3). For 1 unit increment in the continuous measure of EAT, computed tomography volumetric quantification (adjusted hazard ratio, 1.74 [95% CI, 1.42–2.13]; P <0.001) and echocardiographic thickness quantification (adjusted hazard ratio, 1.20 [95% CI, 1.09–1.32]; P <0.001) conferred an increased risk of major adverse cardiovascular events. Conclusions: The utility of EAT as an imaging biomarker for predicting and prognosticating cardiovascular disease is promising, with increased EAT thickness and volume being identified as independent predictors of major adverse cardiovascular events. Registration: URL: https://www.crd.york.ac.uk/prospero ; Unique identifier: CRD42022338075.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".