Comparing Fractional Flow Reserve Versus Intravascular Ultrasound for Percutaneous Coronary Intervention Guidance: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Assessing lesion severity and optimizing percutaneous coronary intervention (PCI) are crucial for improving long-term outcomes in patients with coronary artery disease. Both strategies offer advantages over angiography alone; however, direct comparisons for revascularization decision-making are limited. This study evaluates and compares outcomes of FFR versus intravascular ultrasound (IVUS)-guided PCI strategies. We searched PubMed, Cochrane Central, and ScienceDirect from inception till April 2025. Data for various outcomes were extracted after computing the random-effect model and risk ratio (RR) with a 95% confidence interval (CI). The quality assessment of the included randomized controlled trials and observational studies was conducted using the Cochrane Risk of Bias 2 (ROB-2) and the Newcastle-Ottawa Scale, respectively. Publication bias was assessed visually through funnel plots and statistically through Egger's regression test. We included 5 studies comparing FFR and IVUS in 4714 patients undergoing PCI. The FFR and IVUS groups demonstrated comparable results across all endpoints including major adverse cardiovascular events (RR: 1.05; 95% CI: 0.84-1.30; P = 0.68), all-cause mortality (RR: 0.84; 95% CI: 0.50-1.39; P = 0.49), cardiac death (RR: 1.05; 95% CI: 0.59-1.87; P = 0.87), nonfatal myocardial infarction (RR: 1.31; 95% CI: 0.70-2.44; P = 0.40), and target vessel revascularization (RR: 1.20; 95% CI: 0.78-1.84; P = 0.40). The FFR (either hyperemic or angiography-driven) and IVUS groups showed comparable clinical outcomes in PCI for intermediate coronary lesions. However, FFR adds value with enhanced cost-efficiency and a physiology-driven approach that avoids unnecessary interventions. Selection should depend on patient factors, operator expertise, and institutional resources.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.024 | 0.038 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".