Association of Quantitative Coronary Artery Calcium Density Subtype Volumes With Major Adverse Cardiovascular Events
Bibliographic record
Abstract
BACKGROUND: Growing evidence has demonstrated that low density coronary artery calcification (CAC) is associated with a higher risk of cardiovascular events. OBJECTIVES: We aim to explore the relationship between CAC volumes at predefined densities, assessed by CAC volume according to CAC Hounsfield unit (HU), and major adverse cardiac events (MACEs). METHODS: We evaluated 3 patient groups with no prior coronary artery disease history who underwent an electrocardiogram-gated noncontrast computed tomography scan for CAC scanning (CAC group, n = 2,028) or as part of a cardiac imaging test: single-photon emission computed tomography (SPECT)-myocardial perfusion imaging (SPECT group, n = 2,782), and positron emission tomography (PET)-myocardial perfusion imaging (PET group, n = 2,366). CAC subtype volumes of low, intermediate, and high density based on HU cutoff (low: 130-199 HU, intermediate: 200-399 HU, and high: ≥400 HU). MACE included mortality, myocardial infarction, unstable angina, and late revascularization. RESULTS: During a median 4.3 years (interquartile ranges: 2.6-12.8) follow-up duration, 1,033 MACE occurred (14.4%). In multivariable analysis, low-density CAC volume was independently predictive of MACE (log-transformed; CAC group: HR: 1.65; 95% CI: 1.05-2.60; SPECT group: HR: 1.41, 95% CI: 1.02-1.94; PET group: HR: 1.34, 95% CI: 1.11-1.61; P < 0.05), whereas intermediate and high-density volumes were not (P > 0.05). Density CAC volumes improved discrimination and reclassification among all 3 groups (CAC, SPECT, and PET groups: global chi-square improvement: 9.1, 16.1, and 16.6, respectively, P < 0.01; net reclassification index: 47.3, [95% CI: 33.2-61.4], 49.6 [95% CI: 36.8-62.4] and 15.4, [95% CI: 5.9-24.9], respectively, P < 0.01). CONCLUSIONS: Low-density HU volume was independently associated with an increased MACE risk and improved discrimination and reclassification over conventional approaches in a broad spectrum of individuals undergoing CAC scanning.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".