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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".