Quantitative flow ratio in predicting residual angina: is it better than the eye?
Bibliographic record
Abstract
Abstract Background/Introduction Percutaneous coronary intervention (PCI) guided by functional coronary stenosis severity has been associated with fewer clinical adverse events compared to plain coronary angiography. Quantitative flow ratio (QFR) has proven to be a reliable tool for the functional assessment of coronary lesions. Purpose To investigate the predictive role of disagreement between plain coronary angiography and QFR in PCI guiding regarding symptom relief. Methods We performed an offline QFR analysis in all vessels of consecutive patients who underwent coronary angiography and prospectively followed up the patients for a median follow-up period of 30.5 (26.4-33.7) months. Patients were divided into two groups according to the concordance or discordance of the two methods. Patients with at least one vessel with QFR value ≥ 0.80 treated with PCI and/or at least one vessel with QFR value < 0.80 not treated with PCI were included in the discordance group. The remaining patients formed the concordance group. Endpoint was the presence of angina stratified by the Canadian Cardiovascular Society (CCS) angina score at follow-up the composite outcome of cardiovascular death, myocardial infraction and ischemia-driven revascularization. Results Overall, we included 549 patients in the study. Concordance between plain coronary angiography and QFR was present in 404 (73.6%) patients, while discordance between the two methods was found in 145 patients (26.4%). Baseline patient characteristics are displayed in Figure 1. Patients in the discordance group were older, with more extended coronary artery disease and higher SYNTAX score. More patients in the discordance group reported severe angina (CCS 3-4) compared to the concordance group (22.1% vs 14.4% p=0.036). Furthermore, discordance between the two methods was an independent risk factor for major clinical adverse events (6.9% vs 23.4% OR: 2.975 95%CI 1.782-4.967, p<0.001). Conclusion In our study, discordance between plain coronary angiography and QFR in revascularization guidance was associated with a higher rate of severe angina and clinical adverse events, suggesting proper functional selection of patients to undergo PCI is crucial for both symptom relief and improved clinical outcomes.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".