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QFR与FFRangio软件用户学习曲线的直接比较。

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

作者信息

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

机构信息

Department of Cardiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.

MicroBioRobotic Systems Laboratory, Institute of Mechanical Engineering, EPFL, Lausanne, Switzerland.

出版信息

Catheter Cardiovasc Interv. 2025 Feb;105(3):692-697. doi: 10.1002/ccd.31384. Epub 2024 Dec 24.

Abstract

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.

摘要

背景

定量血流比(QFR)和FFRangio是基于血管造影的技术,用于从血管造影图像中对冠状动脉病变进行功能评估,已在多项临床研究中得到验证。关于每种技术相关的学习曲线的信息有限。

目的

本研究旨在比较QFR和FFRangio在评估冠状动脉狭窄方面的学习曲线,重点关注与有创测量相比分析速度和准确性的变化。

方法

由五名盲法研究者组成的团队,包括两名护士、一名医学生和一名实习医生,对这两种技术都接受了相同的标准化培训。记录每次分析所需的时间以及计算出的FFR值,并与有创金标准进行比较。

结果

共回顾性分析了270个病变(44例患者中的54个冠状动脉病变)。有创FFR值的中位数为0.88[四分位间距0.5,0.9]。使用QFR和FFRangio进行分析的中位数时间分别为245[四分位间距62,319]秒和252[四分位间距82,315]秒(p = 0.171)。随着经验的增加,QFR和FFRangio分析所需的时间均显著减少(p < 0.01)。在准确性方面,QFR和FFRangio与有创FFR的中位数差异分别为0.06[四分位间距:0,0.12]和0.06[四分位间距:0,0.12](p = 0.620)。两种技术在早期就达到了性能平台期,在整个研究过程中表现出相当的结果。

结论

对QFR和FFRangio的初始培训能够快速达到最佳性能,但进一步的练习主要提高分析速度,同时保持两种软件的准确性。

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