Head-to-Head Comparison of Learning Curves Between QFR and FFRangio Software Users.
Details
Serval ID
serval:BIB_C5F7B62F6104
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Head-to-Head Comparison of Learning Curves Between QFR and FFRangio Software Users.
Journal
Catheterization and cardiovascular interventions
ISSN
1522-726X (Electronic)
ISSN-L
1522-1946
Publication state
Published
Issued date
02/2025
Peer-reviewed
Oui
Volume
105
Number
3
Pages
692-697
Language
english
Notes
Publication types: Journal Article ; Comparative Study
Publication Status: ppublish
Publication Status: ppublish
Abstract
Quantitative flow ratio (QFR) and FFRangio are angiography-based technologies used to perform functional assessment of coronary lesions from angiographic images, validated across multiple clinical studies. There is limited information on the learning curves associated with each technology.
This study aims to compare the learning curves of QFR and FFRangio in evaluating coronary stenoses, focusing on changes in analysis speed and accuracy compared to invasive measurements.
A team of five blinded investigators, including two nurses, one medical student, and one physician in training, underwent identical standardized training on both technologies. The time taken for each analysis and the computed FFR values were documented and compared against the invasive gold standard.
A total of 270 lesions (54 coronary lesions in 44 patients) were retrospectively analyzed. The median invasive FFR value was 0.88 [IQR 0.5, 0.9]. The median time for analysis with QFR and FFRangio was 245 [IQR 62, 319] and 252 [IQR 82, 315] s, respectively (p = 0.171). Both QFR and FFRangio demonstrated a significant reduction in the time required for analysis as experience increased (p < 0.01). Regarding accuracy, the median difference with invasive FFR for QFR and FFRangio was 0.06 [IQR: 0, 0.12] and 0.06 [IQR: 0, 0.12], respectively (p = 0.620). Both technologies reached a performance plateau early on, exhibiting comparable results throughout the study.
Initial training in QFR and FFRangio enables quick attainment of maximal performance, but further practice primarily enhances analysis speed while maintaining accuracy, for both software.
This study aims to compare the learning curves of QFR and FFRangio in evaluating coronary stenoses, focusing on changes in analysis speed and accuracy compared to invasive measurements.
A team of five blinded investigators, including two nurses, one medical student, and one physician in training, underwent identical standardized training on both technologies. The time taken for each analysis and the computed FFR values were documented and compared against the invasive gold standard.
A total of 270 lesions (54 coronary lesions in 44 patients) were retrospectively analyzed. The median invasive FFR value was 0.88 [IQR 0.5, 0.9]. The median time for analysis with QFR and FFRangio was 245 [IQR 62, 319] and 252 [IQR 82, 315] s, respectively (p = 0.171). Both QFR and FFRangio demonstrated a significant reduction in the time required for analysis as experience increased (p < 0.01). Regarding accuracy, the median difference with invasive FFR for QFR and FFRangio was 0.06 [IQR: 0, 0.12] and 0.06 [IQR: 0, 0.12], respectively (p = 0.620). Both technologies reached a performance plateau early on, exhibiting comparable results throughout the study.
Initial training in QFR and FFRangio enables quick attainment of maximal performance, but further practice primarily enhances analysis speed while maintaining accuracy, for both software.
Keywords
Humans, Learning Curve, Retrospective Studies, Coronary Angiography, Male, Female, Fractional Flow Reserve, Myocardial, Coronary Stenosis/physiopathology, Coronary Stenosis/diagnostic imaging, Reproducibility of Results, Time Factors, Middle Aged, Predictive Value of Tests, Aged, Cardiac Catheterization, Clinical Competence, Software, Coronary Artery Disease/diagnostic imaging, Coronary Artery Disease/physiopathology, Coronary Vessels/diagnostic imaging, Coronary Vessels/physiopathology, Radiographic Image Interpretation, Computer-Assisted, Observer Variation, FFRangio, QFR, angiographic based FFR, learning curves
Pubmed
Web of science
Create date
05/01/2025 15:09
Last modification date
22/02/2025 7:06