High-risk plaques in non-culprit lesions and clinical outcome after NSTEMI vs. STEMI
Bibliographic record
Abstract
AIMS: Complete non-culprit (NC) revascularisation may help reduce recurrent events after non-ST-segment elevation myocardial infarction (NSTEMI), especially if NC lesions would harbour high-risk plaque (HRP) features similar to ST-segment elevation myocardial infarction (STEMI). This study aimed to assess differences in fractional flow reserve (FFR)-negative NC plaque morphology in patients presenting with NSTEMI vs. STEMI and assess the association of HRP morphology and clinical outcome. METHODS AND RESULTS: In the prospective PECTUS-obs study, 438 patients presenting with myocardial infarction (MI) underwent optical coherence tomography (OCT) of all FFR-negative intermediate NC lesions. The primary endpoint was the occurrence of major adverse cardiovascular events (MACE, composite of all-cause mortality, non-fatal MI or unplanned revascularisation) at 2-year follow-up. Four hundred and twenty patients had at least one analysable OCT, including 203 (48.3%) with NSTEMI and 217 (51.7%) with STEMI. The prevalence of HRPs, including thin-cap fibroatheromas, plaque rupture, and thrombus, was comparable between groups. MACE occurred in 29 (14.3%) NSTEMI patients and 16 (7.4%) STEMI patients (Puni-variable = 0.025 and Pmulti-variable = 0.270). Incidence of MACE was numerically higher among patients with HRP, irrespective of the clinical presentation at index (Pinteraction = 0.684). Among HRP criteria, plaque rupture was associated with MACE in both NSTEMI (P < 0.001) and STEMI (P = 0.020). CONCLUSION: Presence of NC HRP is comparable between NSTEMI and STEMI and leads to numerically higher event rates in both. These results call for additional research on complete revascularisation in NSTEMI and treatment of HRP. CLINICAL TRIAL REGISTRATION: NCT03857971.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".