Associations of Pericardial Fat Area Determined by Routine Chest Computed Tomography With Coronary Risk Factors and Coronary Artery Disease
Bibliographic record
Abstract
Background: Pericardial fat has been reported to be associated with coronary risk factors and severity of coronary arterial lesions. Previous studies had measured pericardial fat volume (PFV) by using contrast-enhanced cardiac computed tomography (CT) when pericardial fat was quantified. We determined pericardial fat area (PFA) in the cross section with the height of sternal angle by using routine chest CT. Methods: We picked up 252 patients who underwent chest and abdominal CT, and we selected patients whose coronary arteries were evaluated by coronary angiography or coronary CT. Coronary artery disease (CAD) was defined as more than 75% lumen stenosis. PFA was defined as any pixel with CT attenuation of -150 to -30 Hounsfield Unit (HU) within the pericardial sac at the sternal angle level. Results: Fifty-three patients were eligible. PFA was significantly larger in men than in women. Serum high-density lipoprotein (HDL)-cholesterol level was significantly and negatively correlated with PFA. Hemoglobin A1c and carotid arterial intima-media thickness tended to be positively correlated with PFA. PFA was significantly and nearly 50% larger in patients with CAD than in patients without CAD. The cut-off value of PFA was 10.4 cm 2 , and sensitivity and specificity of PFA for CAD were 53.8% and 88.0%, respectively. Conclusions: Present study is the first to show a significant association of PFA with gender and CAD. PFA can be determined by routine chest CT, and is simpler and more reproducible, and PFA is more available in a greater number of medical institutes as compared with PFV. Present study also showed a discriminatory value of PFA for CAD comparable to PFV. J Clin Med Res. 2019;11(4):289-296 doi: https://doi.org/10.14740/jocmr3782
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".