Abstract 17494: The Incremental Risk Prediction of Cardiac CTA-Derived Quantitative Atherosclerosis in the International Advance Registry
Bibliographic record
Abstract
Background: We sought to assess the incremental prognostic utility of AI-enabled quantitative coronary plaque assessment (AI-QCPA) beyond stenosis severity and FFR CT , for the prediction of late revascularization and MACE in the large prospective international ADVANCE registry. Methods: 4737 participants were submitted for AI-QCPA evaluation. Total plaque volume (TPV), calcified plaque volume (CPV), non-calcified plaque volume (NCPV), and low attenuated plaque volume (LAPV) were quantified. In addition, total percent plaque volume (TPAV) and its components were calculated (PV/Vessel Volume * 100). Demographics, stenosis severity, lowest FFR CT , and Delta FFR CT were also recorded. To explore the relationship between plaque measures and MACE (death, myocardial infarction, and unplanned hospitalization leading to revascularization) and late revascularization (>90 days) we performed Kaplan Meier event free outcomes analyses following adjustments for stenosis severity, FFR CT and Delta FFR CT . Results: AI-QCPA was available in 4430 subjects. Mean (SD) TPV was 542.4 ± 522.5 mm 3 . Optimal cutpoints of plaque metrics were predictive of MACE and/or late revascularization when adjusted-TPV (HR- 1.45 CI- 1.06-1.98; P=0.02); CPV (HR- 1.65 CI- 1.21- 2.26; p=0.002); NCPV (HR 1.43 CI- 1.02-1.99; P=0.04) and LAPV (HR- 2.23 CI 1.62-3.07; P=0.03 . Stronger risk prediction was achieved by plaque measures adjusted for vessel volume: TPAV (HR 1.92 CI 1.40- 2.64 P<0.001). This incremental risk discrimination of quantitative plaque measures remained significant as a predictor of MACE alone- TPV- (HR 1.84 CI 1.02- 3.29 P=0.04) TPAV (HR 3.33 CI- 1.85- 6.02 P<0.0001) (Figure 1). Conclusions: In this large prospective international registry, AI-QCPA provided incremental risk discrimination for MACE and late revascularization beyond stenosis severity and FFR CT .
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".