Abstract 12: Volume of Calcium in the Descending Thoracic Aorta Predicts All Cause Mortality Beyond Coronary Artery Calcium: The Multi-Ethnic Study of Atherosclerosis
Bibliographic record
Abstract
Introduction: Coronary artery calcium (CAC) volume and density differentially predict incident cardiovascular disease (CVD), with CAC density inversely associated with these outcomes. Whether similar associations exist between descending thoracic aortic calcium (DTAC) volume and density and all cause mortality (ACM) are unknown. We hypothesized that DTAC volume and density predict ACM independently of CAC. Methods: The Multi-Ethnic Study of Atherosclerosis enrolled 6,814 participants free of clinical CVD at baseline and followed them for incident adverse events. Cardiac CT at baseline visualized the segment of the descending thoracic aorta posterior to the heart. Only participants with prevalent DTAC were included (necessary to evaluate DTAC density). DTAC and CAC volumes were natural log transformed to adjust for skewness. Cox regression models estimated the associations of DTAC volume and density with ACM after adjustment for age, gender, ethnicity, CVD risk factors, statin use, and CAC volume and density. The incremental predictive values of DTAC volume and density were evaluated by area under receiver operating characteristic (AUC) curves. Results: Of the total cohort, 1,850 participants (27%) had prevalent DTAC and 491 deaths occurred over 10.3 years. In separate regression models, DTAC volume was independently associated with ACM after adjustment for CAC volume (HR 1.21 [95% CI 1.09-1.35]) and additional adjustment for CAC density (1.18 ([1.06-1.32]). After the same adjustments, DTAC density was not significantly associated with ACM (0.94 [0.84-1.06]). The AUC for the base Model 1 (risk factors + CAC volume) was 0.706 (0.680-0.732), which increased to 0.716 (0.690-0.742) with the addition of DTAC volume in Model 2 (p=0.03 compared to Model 1). Further addition of DTAC density in Model 3 did not improve the AUC significantly (0.717 [0.692-0.743], p=0.23 compared to Model 2). Conclusions: In a cohort free of baseline clinical CVD, DTAC visualized on cardiac CT was common. When DTAC was present, DTAC volume (but not density) was independently associated with ACM. DTAC volume also significantly improved ACM risk prediction beyond risk factors and CAC volume.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 0.001 |
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".