Virtual physiological analysis of non-culprit disease in patients with STEMI and multivessel disease: a substudy of the COMPLETE trial
Bibliographic record
Abstract
Abstract Aims In the complete revascularization with multivessel PCI for myocardial infarction (COMPLETE) trial, staged complete revascularization in patients with ST-segment-elevation myocardial infarction (MI) reduced major adverse cardiovascular events compared with culprit-only revascularization. Inclusion was based on angiographic criteria. Objectives We modelled non-culprit virtual fractional flow reserve (vFFR) and investigated interactions between physiological lesion severity and the benefits of complete revascularization in COMPLETE. Methods and results All suitable angiograms from COMPLETE underwent software-based 3-dimensional (3D) arterial reconstruction and analysis of 3D-quantitative coronary angiography (QCA) and vFFR using computational fluid dynamics software. Physiological lesion significance was defined as vFFR ≤0.80 and was compared with operators’ visual angiographic analysis, 2D-QCA and 3D-QCA. vFFR was computed in 635 patients (710 lesions). 302 patients (48%) had ≥1 physiologically significant lesion and 333 (52%) had none. 321 (45%) lesions were physiologically significant and 389 (55%) were not. There was no statistically significant interaction between physiological lesion significance and any of the trial co-primary or key secondary clinical outcomes, or an exploratory outcome of ischaemia-driven revascularization without preceding MI (all interaction P > 0.30). 3D-QCA predicted vFFR significance more accurately than visual and 2D-QCA (concordance 73% vs. 49% vs. 59%, respectively). Conclusion In this virtual physiological substudy of the COMPLETE trial, 52% of patients lacked any physiologically significant lesions and the benefits of complete revascularization appeared to be independent of physiological lesion significance. 3D-QCA was a better predictor of physiological significance than either 2D-QCA or operator visual analysis. Further research is warranted to compare angiography-guided and physiology-guided complete revascularization strategies.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".