Head-to-Head Comparison of Learning Curves Between QFR and FFRangio Software Users.

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Tác giả: Stephane Fournier, Mehdi Ali Gadiri, Sarah Mauler-Wittwer, David Meier, Anais Metzinger, Joanne Muller, Olivier Muller, Nathalie Noirclerc, Adil Salihu, Ioannis Skalidis, Aurelia Zimmerli, Jade Zulauff

Ngôn ngữ: eng

Ký hiệu phân loại:

Thông tin xuất bản: United States : Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 160330

 BACKGROUND: 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. AIMS: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
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