Assessment of Coronary Stenoses for Percutaneous Coronary Intervention: A Systematic Review and Network Meta-Analysis of Randomized Trials
Bibliographic record
Abstract
Evidence regarding the comparative efficacy of the different methods to determine the significance of coronary stenoses in the catheterization laboratory is lacking. We aimed to compare all available methods guiding the decision to perform percutaneous coronary intervention (PCI). We searched Medline, Embase, and CENTRAL until October 5, 2023. We included trials that randomized patients with greater than 30% stenoses who were considered for PCI and reported major adverse cardiovascular events (MACE). We performed a frequentist random-effects network meta-analysis and assessed the certainty of evidence using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. We included 15 trials with 16,333 participants with a mean weighted follow-up of 34 months. The trials contained a median of 49.3% (interquartile range: 32.6%, 100%) acute coronary syndrome participants. Quantitative flow ratio (QFR) was associated with a decreased risk of MACE compared with coronary angiography (CA) (risk ratio [RR] 0.68, 95% confidence interval [CI] 0.56 to 0.82, high certainty), fractional flow reserve (FFR) (RR 0.73, 95% CI 0.58 to 0.92, moderate certainty), and instantaneous wave-free ratio (iFR) (RR 0.63, 95% CI 0.49 to 0.82, moderate certainty), and ranked first for MACE (88.1% probability of being the best). FFR (RR 0.93, 95% CI 0.82 to 1.06, moderate certainty) and iFR (RR 1.07, 95% CI 0.90 to 1.28, moderate certainty) likely did not decrease the risk of MACE compared with CA. Intravascular imaging may not be associated with a significant decrease in MACE compared with CA (RR 0.85, 95% CI 0.62 to 1.17, low certainty) when used to guide the decision to perform PCI. In conclusion, a decision to perform PCI based on QFR was associated with a decreased risk of MACE compared with CA, FFR, and iFR in a mixed stable coronary disease and acute coronary syndrome population. These hypothesis-generating findings should be validated in large, randomized, head-to-head trials.
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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 | 0.037 | 0.087 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.035 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".