Gál Róbert, Csanádi Bettina, Ferenci Tamás, Bora Noémi, Piróth Zsolt
Gottsegen National Cardiovascular Center, 1096 Budapest, Hungary.
Károly Rácz Doctoral School of Clinical Medicine, Semmelweis University, 1085 Budapest, Hungary.
J Clin Med. 2025 Aug 22;14(17):5946. doi: 10.3390/jcm14175946.
The diagnostic value of Quantitative Flow Ratio (QFR) with respect to Fractional Flow Reserve (FFR) in real-world settings is not well described, and neither are the factors influencing the bias of QFR versus FFR well understood. The learning curve associated with QFR calculation has not been thoroughly investigated. Hence, we sought to evaluate the association between the QFR and FFR, to investigate the influence of clinical parameters on both values and their difference, and to analyze the learning curve associated with QFR measurement in a real-world setting. : All patients who underwent FFR and QFR measurements in 2023 at our tertiary-care center were included. The bias was characterized using a Bland-Altman plot and multivariable regression was used to uncover its potential predictors. : QFR calculation was successful in 73% of 595 patients with 778 vessels with FFR measurement results. Median bias of QFR was 0.011, but in 7% of the cases, the difference between the two exceeded 0.10. A good correlation was found between the two indices. Receiver operating characteristic curve analysis showed that the area under the curve of QFR for predicting FFR ≤ 0.80 was 0.912. FFR and QFR values were lower in the left anterior descending artery; acute coronary syndrome indication was associated with higher QFR values. Right coronary artery localization was associated with a greater bias of QFR, whereas female gender and aortic stenosis were associated with a lower bias of QFR. Both measurement time and bias decreased in a non-linear fashion with increasing experience. : Clinical and angiographic factors affect the bias of QFR versus FFR. QFR has a short learning curve with growing experience leading to shorter measurement time and less bias.
在实际临床环境中,定量血流比(QFR)相对于血流储备分数(FFR)的诊断价值尚未得到充分描述,影响QFR与FFR偏差的因素也未被充分理解。与QFR计算相关的学习曲线也未得到深入研究。因此,我们试图评估QFR与FFR之间的关联,研究临床参数对两者值及其差异的影响,并分析在实际临床环境中与QFR测量相关的学习曲线。:纳入了2023年在我们三级医疗中心接受FFR和QFR测量的所有患者。使用Bland-Altman图对偏差进行特征描述,并使用多变量回归来揭示其潜在预测因素。:在595例患者的778支血管中,73%的患者成功进行了QFR计算并获得了FFR测量结果。QFR的中位数偏差为0.011,但在其中7%的病例中,两者差异超过0.10。发现这两个指标之间具有良好的相关性。受试者工作特征曲线分析表明,QFR预测FFR≤0.80时的曲线下面积为0.912。左前降支血管的FFR和QFR值较低;急性冠状动脉综合征指征与较高的QFR值相关。右冠状动脉定位与QFR的较大偏差相关,而女性性别和主动脉瓣狭窄与QFR的较小偏差相关。随着经验的增加,测量时间和偏差均呈非线性下降。:临床和血管造影因素会影响QFR相对于FFR的偏差。QFR具有较短的学习曲线,随着经验的增加,测量时间会缩短,偏差也会减小。