ArticleJournal of the American Heart Association2025
Murray Law-Based Quantitative Flow Ratio Proficiency in Clinical Practice: Validating Routine Diagnostic Performance and Analyzing Learning Curve Thresholds.
Article in Journal of the American Heart Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- VERMONT μQFR: Diagnostic Accuracy of Murray Law-Based 2D-μQFR Compared to 3D-μQFR and vFFR.Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions · 2026Article
- Prognostic value of a novel indicator integrating C-reactive protein-triglyceride glucose index and coronary physiological burden in patients undergoing PCI: a prospective cohort study.Frontiers in nutrition · 2026Article
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Authors and funding
16 authors.
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Abstract
backgroundAlthough second-generation Murray law-based quantitative flow ratio uses artificial intelligence-driven automation to improve angiography-derived physiological assessment, the impact of operator learning curves on its diagnostic reliability in routine practice remains unclear.
methodsConsecutive patients with suspected myocardial ischemia (May 2021 to July 2024) underwent online Murray law-based quantitative flow ratio computation from angiographic images, followed by fractional flow reserve measurement. Diagnostic accuracy was evaluated using receiver operating characteristic analysis. The number of vessels needed to achieve high performance (area under the curve [AUC] ≥0.90) was determined. Learning curves were assessed by sequentially expanding the data set starting from the first 11 vessels. First-order AUC differences (ΔAUC
resultsMurray law-based quantitative flow ratio showed excellent diagnostic accuracy: Vessel-level AUC was 0.92 (95% CI, 0.89-0.94), and patient-level AUC was 0.91 (95% CI, 0.88-0.94). An AUC ≥0.90 was achieved after analyzing 170 vessels, with continued improvement thereafter. Peak performance and stability were reached at 441 vessels, where incremental ΔAUC values showed no significant deviation from 0 (Mann-Whitney
conclusionsOperator experience significantly affects Murray law-based quantitative flow ratio accuracy. Analyzing at least 170 vessels ensures high reliability, with optimal performance achieved around 441 cases. Structured training is essential for integrating artificial intelligence-enhanced coronary physiology tools into clinical practice.
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