Outcomes with revascularization vs. medical therapy according to plaque burden from coronary computed tomography angiography
Bibliographic record
Abstract
AIMS: We aimed to investigate whether plaque burden from coronary computed tomography angiography (CCTA) could be used to identify patients potentially benefitting from revascularization. METHODS AND RESULTS: We assessed consecutive patients undergoing CCTA and selective 15O-water perfusion positron emission tomography for evaluation of coronary artery disease (CAD) at two tertiary care centres in Finland and The Netherlands. Per-patient percent atheroma volume (PAV) and maximum per-vessel PAV in each patient was quantified by artificial intelligence-guided quantitative computed tomography (AI-QCT). We constructed a Cox regression for death, myocardial infarction (MI), or unstable angina pectoris (uAP) including continuous PAV, revascularization, and their interaction, adjusted for calcium score, ischaemia, cardiovascular risk factors, symptoms, and medication in a subcohort of 2233 patients (206 events; median follow-up 6.8 years). There was significant interaction between revascularization and continuous PAV on patient-level (p-interaction = 0.042) and vessel-level (p-interaction = 0.026). Revascularization was associated with a significantly lower event rate at per-patient PAV 22% (HR 0.70, 95% CI 0.43-0.98) and per-vessel PAV 22% (HR 0.64, 95% CI 0.29-0.99) or higher. In subgroup analyses, after adjustment for age, sex, cardiovascular risk factors, ischaemia, anti-platelet, and lipid-lowering drugs, revascularization in patients with per-vessel PAV ≥22% was associated with a significantly reduced event rate (HR 0.50, 95% CI 0.27-0.91, P = 0.024) (p-interaction = 0.016), whereas patient-level results remained non-significant (HR 0.62, 95% CI 0.35-1.10, P = 0.104) (p-interaction < 0.001). CONCLUSIONS: In this cohort study of patients referred for CCTA, revascularization on top of medical therapy was associated with a lower rate of long-term death, MI, or uAP from per-vessel PAV of 22% upwards.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".