Differential risk of acute coronary syndrome in main vessels and side branches according to lumen, plaque, and haemodynamic characteristics
Bibliographic record
Abstract
Abstract Background The management of coronary bifurcation lesions remains a challenge in interventional cardiology. While prior studies have demonstrated that upfront side branch (SB) stenting offers limited clinical benefit compared with a provisional approach, the optimal strategy for assessing SB lesion risk remains less well defined. Unlike main vessel (MV) lesions, where the interplay between luminal, plaque, and haemodynamic characteristics has been extensively studied, it remains uncertain whether the same high-risk features carry similar clinical significance in SB lesions. Purpose This study aimed to compare the associations among lumen, plaque, and haemodynamic characteristics in MV and SB lesions and to evaluate differences in their acute coronary syndrome (ACS) risk. Methods Using data from the EMERALD-II trial, we analysed coronary lesions from 351 patients who had undergone coronary computed tomography angiography (CCTA) between one month and three years prior to ACS onset. MV and SB lesions were identified, and the lesion characteristics including degree of stenosis, plaque burden, adverse plaque characteristics (APCs), and ΔFFRCT were assessed. Culprit lesions were determined based on invasive coronary angiography at the time of ACS. Results Among the 2,541 lesions analysed, 2,011 (82.0%) were MV lesions and 440 (18.0%) were SB lesions. The correlations between luminal stenosis, plaque burden, number of APCs, and ΔFFRCT were generally weaker in SB than in MV lesions. Among lesions with comparable high-risk features, the proportion of ACS culprits was consistently lower in SB than in MV lesions (≥50% stenosis: 38.5% vs. 11.1%; ≥70% plaque burden: 23.8% vs. 8.6%; ≥2 APCs: 34.4% vs. 18.2%; ΔFFRCT ≥0.1: 49.4% vs. 19.4%, all p < 0.05). Even when applying more stringent cutoffs for high-risk features in SB lesions, the proportion of ACS culprit lesions remained lower than in MV lesions with predefined high-risk features. Diagnostic performance of all four high-risk features, was lower in SB than in MV lesions, although ΔFFRCT ≥0.1 provided the highest predictive value in both MV and SB. Conclusions MV and SB lesions exhibit different lumen-plaque relationships, leading to differences in ACS incidence under identical conditions. These findings suggest that current MV-derived high-risk criteria may not be directly applicable to SB lesions, highlighting the need for a more tailored approach to SB lesion risk stratification.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".