Impact of Systematic Use of Fractional Flow Reserve and Optical Coherence Tomography on Percutaneous Coronary Intervention Outcomes in Patients With Diabetes
Bibliographic record
Abstract
Background: Intracoronary imaging and physiology guidance of percutaneous coronary intervention (PCI) have shown significant improvements in clinical outcomes. However, comparable data on the use of these modalities in PCI of patients with diabetes are only sparsely available from South Asia. This study investigated the feasibility and clinical outcomes of systematic use of fractional flow reserve (FFR) and optical coherence tomography (OCT) during PCI in patients with diabetes. Methods: The study enrolled 275 patients (≥ 18 years) from nine centers in India and one from Bangladesh between October 2021 and September 2022. Patients with stable ischemic heart disease, non-ST-elevation myocardial infarction (MI), and unstable angina were included in the study. Angiographically intermediate lesions (diameter stenosis of 40% to 80%) underwent FFR-guided PCI. Lesions with a diameter stenosis of > 80% underwent PCI without FFR evaluation. All PCI procedures were guided by OCT using the MLD-MAX algorithm. Results: At 12 months, the target lesion failure (TLF) rate, a composite of cardiac death, nonfatal MI, and clinically driven target lesion revascularization, was 3.3%. Among the intermediate lesions, PCI was deferred by 70% after the FFR evaluation. Pre- and post-procedural OCT has led to a strategy change in 49.5% and 33.6%, respectively. Conclusions: The study revealed a relatively lower rate of events with FFR and OCT guidance compared to historical data from angiography-guided PCI in patients with diabetes. The strategy of combined use of FFR and OCT in PCI may contribute to improved clinical outcomes in patients with diabetes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".