Atherosclerotic plaque characteristics improve diagnosis of ischemia for non-obstructive coronary artery lesions: a direct comparison to fractional flow reserve
Bibliographic record
Abstract
Purpose: Fractional flow reserve (FFR) at the time of invasive coronary angiography (ICA) is the gold standard for determining lesion-specific ischemia, and identifies ischemia in a significant proportion of lesions considered anatomically non-obstructive. Beyond luminal stenosis severity, coronary CT angiography (CT) enables evaluation of atherosclerotic plaque characteristics (APCs) that include positive remodeling (PR), low attenuation plaque (LAP) and spotty intra-plaque calcification (SC). The relationship of these APCs to ischemia in non-obstructive coronary lesions has not been evaluated to date. Methods: 252 patients from 17 centers in 5 countries were prospectively enrolled. Patients underwent CT and ICA, with clinically indicated FFR performed for 407 coronary lesions. CTs were evaluated by an independent core laboratory in blinded fashion, with ≥50% and <50% stenosis considered obstructive and non-obstructive, respectively. Presence of APCs within coronary lesions by CT was defined as: (1) PR, maximal lesion diameter/reference diameter ≥1.10; (2) LAP, any intra-plaque voxel <30 HU; and (3) SC, nodular calcified plaque ≤3 mm. Coronary lesion-specific ischemia was defined by an FFR ≤0.8. Results: For FFR-interrogated coronary lesions, 195 of 407 (48%) were non-obstructive by CT. FFR-defined ischemia was present in 33 of 195 (17%) lesions, with a mean FFR value of 0.75±0.07. Amongst non-obstructive lesions that caused ischemia, 24 (73%), 9 (27%) and 8 (24%) exhibited PR, LAP and SC, respectively; with at least 1 APC present in 24 (73%) of lesions. In multivariable analyses, the presence of PR [Odds ratio (OR) 6.6, 95% confidence interval (CI) 2.4-17.9, p<0.0001)] was associated with lesion-specific ischemia while LAP (OR 1.0, 95% CI 0.3-3.2, p=0.9) and SC (OR 1.4, 95% CI 0.5-4.5, p=0.5) were not. A dose-response relationship was observed for increasing risk of ischemia for non-obstructive coronary lesions possessing 1 (OR 4.5, p=0.006), 2 (OR 11.8, p<0.001) and 3 (OR 4.0, p=0.1) APCs. Conclusion: The presence of positive arterial remodeling and increasing numbers of APCs enhances diagnosis of non-obstructive coronary lesions that cause ischemia.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".