Impact of clinical risk characteristics on the prognostic value of high-risk plaques
Bibliographic record
Abstract
BACKGROUND: High-risk coronary plaques (HRPs) are associated with adverse cardiovascular outcomes. However, the clinical practicality of HRP identification is challenged by their modest prevalence and low positive predictive value. AIMS: We aimed to evaluate the association between clinical risk factors and HRPs, as well as the clinical impact of HRPs across different clinical risk profiles. METHODS: This is a pooled analysis of individual patient data from the prospective observational COMBINE (OCT-FFR) and PECTUS-obs studies. A modified version of the Thrombolysis in Myocardial Infarction Risk Score for Secondary Prevention (TRS-2P) was used for risk stratification. The composite endpoint of major adverse cardiovascular events (MACE) was evaluated on a patient level, and target lesion failure (TLF), also a composite endpoint, was evaluated on a lesion level. RESULTS: =0.539 for MACE and 0.337 for TLF). For TLF, the highest event rate per 100 lesion-years was observed in high-risk patients with HRPs (6.28, 95% CI: 3.52-10.36; 13.6% absolute risk at 2 years). CONCLUSIONS: HRPs are associated with a negative clinical outcome, without apparent differences between clinical risk profiles. This highlights the independent value of optical coherence tomography for prognostication beyond clinical risk factors. The high event rates in high-risk patients with HRPs necessitate the search for additional therapeutic strategies to mitigate this risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".