Vulnerable plaque features and adverse events in patients with diabetes mellitus: a post hoc analysis of the COMBINE OCT-FFR trial
Bibliographic record
Abstract
BACKGROUND: Thin-cap fibroatheroma (TCFA) lesions are associated with a high risk of future major adverse cardiovascular events. However, the impact of other optical coherence tomography-detected vulnerability features (OCT-VFs) and their interplay with TCFA in predicting adverse events remains unknown. AIMS: We aimed to evaluate the individual as well as the combined prognostic impact of OCT-VFs in predicting the incidence of the lesion-oriented composite endpoint (LOCE) in non-ischaemic lesions in patients with diabetes mellitus (DM). METHODS: COMBINE OCT-FFR (ClinicalTrials.gov: NCT02989740) was a prospective, double-blind, international, natural history study that included DM patients with ≥1 non-culprit lesions with a fractional flow reserve>0.80 undergoing systematic OCT assessment. OCT-VFs included the following: TCFA, reduced minimal lumen area (r-MLA), healed plaque (HP), and complicated plaque (CP). The primary endpoint, LOCE - a composite of cardiac mortality, target vessel myocardial infarction, or clinically driven target lesion revascularisation up to 5 years - was analysed according to the presence of these OCT-VFs, both individually and in combination. RESULTS: TCFA, r-MLA, HP and CP were identified in 98 (25.3%), 190 (49.0%), 87 (22.4%), and 116 (29.9%) patients, respectively. The primary endpoint rate increased progressively from 6.3% to 55.6% (hazard ratio 15.2, 95% confidence interval: 4.53-51.0; p<0.001) in patients without OCT-VFs as compared to patients with concomitant HP, r-MLA, CP, and TCFA. The coexistence of TCFA with other OCT-VFs resulted in an increased risk of the LOCE at 5 years. CONCLUSIONS: In DM patients with non-ischaemic lesions, TCFA was the strongest predictor of future LOCE events. However, lesions that present additional OCT-VFs are associated with a higher risk of adverse events than OCT-detected TCFA alone. Further randomised studies are warranted to confirm these findings and their potential clinical implications.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".