Association of thoracic aortic calcium with incident cardiovascular disease and all-cause mortality across the spectrum of coronary artery calcium burden
Bibliographic record
Abstract
Calcification of the ascending and/or descending thoracic aorta is easily measured via non-contrast cardiac computed tomography (CT), commonly performed for quantification of coronary artery calcium (CAC). We assessed whether thoracic aortic calcium (TAC) further improves long-term cardiovascular disease (CVD) risk stratification beyond CAC alone. Cardiac CT was performed among 6,783 asymptomatic Multi-Ethnic Study of Atherosclerosis participants at baseline. Cox proportional hazards regression assessed the association of TAC with incident CVD and all-cause mortality over a median follow-up of 17.7 years, adjusting for CVD risk factors and CAC. The mean age was 62.1 years old, 53% were female, and 28% had TAC. Over a median follow-up of 17.7 years, 48% of participants with TAC ≥500 experienced CVD and 72% died. Compared to TAC=0, TAC ≥500 was significantly associated with an increased risk of CVD (HR=1.28, 95% CI:1.06-1.54) and all-cause mortality (HR=1.44, 95% CI:1.25-1.65), with the strongest association among persons with CAC=0 (CVD HR=1.79, 95% CI: 1.04-3.07; all-cause mortality HR=1.82, 95% CI: 1.29-2.56). The addition of TAC to traditional risk factors and CAC did not improve CVD discrimination (ΔC-statistic=+0.002, p=0.12), but incrementally improved prediction of all-cause mortality (CVD: ΔC-statistic=+0.002, p=0.02). Participants with TAC ≥500 had a high long-term risk for CVD and all-cause mortality. TAC primarily improved risk stratification among persons with CAC=0.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".