Fractional Flow Reserve and Quality-of-Life Improvement After Percutaneous Coronary Intervention in Patients With Stable Coronary Artery Disease
Bibliographic record
Abstract
BACKGROUND: Whether the benefit in quality of life (QOL) after percutaneous coronary intervention depends on the severity of the stenosis as determined by fractional flow reserve (FFR) remains unknown. This study sought to investigate the relationship between FFR values and improvement in QOL. METHODS: From the FAME 1 and 2 trials (Fractional Flow Reserve Versus Angiography for Multivessel Evaluation), we identified 706 stable patients with coronary artery disease who had at least 1 lesion with an FFR≤0.80 that was treated with percutaneous coronary intervention and 185 patients with coronary artery disease who had no lesion with an FFR≤0.80 and were treated medically who served as a reference group. QOL was assessed by the European Quality of Life-5 Dimensions index at baseline, 1 month, and 1 year. We assessed the relationship between QOL improvement (defined as the change in European Quality of Life-5 Dimensions index from baseline) and FFR as a continuous value and according to abnormal FFR tertile. RESULTS: QOL improved significantly after percutaneous coronary intervention in each abnormal FFR tertile, whereas it did not change in the reference group. The lowest abnormal FFR subgroup had the greatest improvement in QOL at 1 month ( P<0.001). In mixed-effects models for repeated measures, lower FFR ( P=0.002 for 1 month and 0.049 for 1 year), greater delta FFR ( P=0.021 for 1 month and 0.025 for 1 year), and higher angina class ( P=0.001 for 1 month and <0.001 for 1 year) were associated with the greatest magnitude of QOL improvement at both 1 month and 1 year. CONCLUSIONS: Among patients with stable coronary artery disease, FFR and angina severity predict QOL improvement after percutaneous coronary intervention. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov . Unique identifiers: NCT00267774 and NCT01132495.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".