Predictors for Vulnerable Plaque in Functionally Significant Lesions
Bibliographic record
Abstract
BACKGROUND: Vulnerable plaque presents prognostic implications in addition to functional significance. OBJECTIVES: The aim of this study was to identify relevant features of vulnerable plaque in functionally significant lesions. METHODS: In this multicenter, prospective study conducted across 5 countries, including patients who had invasive fractional flow reserve (FFR) ≤0.80, a total of 95 patients with available pullback pressure gradient (PPG) and plaque analysis on coronary computed tomographic angiography and optical coherence tomography were analyzed. Vulnerable plaque was defined as the presence of plaque rupture or thin-cap fibroatheroma on optical coherence tomography. Among the 25 clinical characteristics, invasive angiographic findings, physiological indexes, and coronary computed tomographic angiographic findings, significant predictors of vulnerable plaque were identified. RESULTS: Mean percentage diameter stenosis, FFR, and PPG were 77.8% ± 14.6%, 0.66 ± 0.13, and 0.65 ± 0.13, respectively. Vulnerable plaque was present in 53 lesions (55.8%). PPG and FFR were identified as significant predictors of vulnerable plaque (P < 0.05 for all). PPG >0.65 and FFR ≤0.70 were significantly related to a higher probability of vulnerable plaque after adjustment for each other (OR: 6.75 [95% CI: 2.39-19.1]; P < 0.001] for PPG >0.65; OR: 4.61 [95% CI: 1.66-12.8]; P = 0.003 for FFR ≤0.70). When categorizing lesions according to combined PPG >0.65 and FFR ≤0.70, the prevalence of vulnerable plaque was 20.0%, 57.1%, 66.7%, and 88.2% in the order of PPG ≤0.65 and FFR >0.70, PPG ≤0.65 and FFR ≤0.70, PPG >0.65 and FFR >0.70, and PPG >0.65 and FFR ≤0.70 (P for trend < 0.001), respectively. CONCLUSIONS: Among low-FFR lesions, the presence of vulnerable plaque can be predicted by PPG combined with FFR without additional anatomical or plaque characteristics. (Precise Percutaneous Coronary Intervention Plan [P3] Study; NCT03782688).
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".