Novel CAC Dispersion and Density Score to Predict Myocardial Infarction and Cardiovascular Mortality
Bibliographic record
Abstract
BACKGROUND: Coronary artery calcification (CAC) provides robust prediction for major adverse cardiovascular events (MACE), but current techniques disregard plaque distribution and protective effects of high CAC density. We investigated whether a novel CAC-dispersion and density (CAC-DAD) score will exhibit superior prognostic value compared with the Agatston score (AS) for MACE prediction. METHODS: We conducted a multicenter, retrospective, cross-sectional study of 961 patients (median age, 67 years; 61% men) who underwent cardiac computed tomography for cardiovascular or perioperative risk assessment. Blinded analyzers applied deep learning algorithms to noncontrast scans to calculate the CAC-DAD score, which adjusts for the spatial distribution of CAC and assigns a protective weight factor for lesions with ≥1000 Hounsfield units. Associations were assessed using frailty regression. RESULTS: Over a median follow-up of 30 (30–460) days, 61 patients experienced MACE (nonfatal myocardial infarction or cardiovascular mortality). An elevated CAC-DAD score (≥2050 based on optimal cutoff) captured more MACE than AS ≥400 (74% versus 57%; P =0.002). Univariable analysis revealed that an elevated CAC-DAD score, AS ≥400 and AS ≥100, age, diabetes, hypertension, and statin use predicted MACE. On multivariable analysis, only the CAC-DAD score (hazard ratio, 2.57 [95% CI, 1.43–4.61]; P =0.002), age, statins, and diabetes remained significant. The inclusion of the CAC-DAD score in a predictive model containing demographic factors and AS improved the C statistic from 0.61 to 0.66 ( P =0.008). CONCLUSIONS: The fully automated CAC-DAD score improves MACE prediction compared with the AS. Patients with a high CAC-DAD score, including those with a low AS, may be at higher risk and warrant intensification of their preventative therapies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".