Relationship between coronary volume, myocardial mass, and post‐PCI fractional flow reserve
Bibliographic record
Abstract
BACKGROUND: Fractional flow reserve (FFR) measured after percutaneous coronary intervention (PCI) carries prognostic information. Yet, myocardial mass subtended by a stenosis influences FFR. We hypothesized that a smaller coronary lumen volume and a large myocardial mass might be associated with lower post-PCI FFR. AIM: We sought to assess the relationship between vessel volume, myocardial mass, and post-PCI FFR. METHODS: This was a subanalysis with an international prospective study of patients with significant lesions (FFR ≤ 0.80) undergoing PCI. Territory-specific myocardial mass was calculated from coronary computed tomography angiography (CCTA) using the Voronoi's algorithm. Vessel volume was extracted from quantitative CCTA analysis. Resting full-cycle ratio (RFR) and FFR were measured before and after PCI. We assessed the association between coronary lumen volume (V) and its related myocardial mass (M), and the percent of total myocardial mass (%M) with post-PCI FFR. RESULTS: We studied 120 patients (123 vessels: 94 left anterior descending arteries, 13 left Circumflex arteries, 16 right coronary arteries). Mean vessel-specific mass was 61 ± 23.1 g (%M 39.6 ± 11.7%). The mean post-PCI FFR was 0.88 ± 0.06 FFR units. Post-PCI FFR values were lower in vessels subtending higher mass (0.87 ± 0.05 vs. 0.89 ± 0.07, p = 0.047), and with lower V/M ratio (0.87 ± 0.06 vs. 0.89 ± 0.07, p = 0.02). V/M ratio correlated significantly with post-PCI RFR and FFR (RFR r = 0.37, 95% CI: 0.21-0.52, p < 0.001 and FFR r = 0.41, 95% CI: 0.26-0.55, p < 0.001). CONCLUSION: Post-PCI RFR and FFR are associated with the subtended myocardial mass and the coronary volume to mass ratio. Vessels with higher mass and lower V/M ratio have lower post-PCI RFR and FFR.
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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 | 0.000 | 0.001 |
| 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".