Cardiovascular Institute, Azienda Ospedaliero-Universitaria di Ferrara, Via Aldo Moro 8, 44124, Cona, FE, Italy.
Central Arkansas VA Health System, Little Rock, AR, USA.
Cardiovasc Drugs Ther. 2022 Aug;36(4):645-653. doi: 10.1007/s10557-021-07162-6. Epub 2021 Apr 8.
Wire-based coronary physiology pullback performed before percutaneous coronary intervention (PCI) discriminates coronary artery disease (CAD) distribution and extent, and is able to predict functional PCI result. No research investigated if quantitative flow ratio (QFR)-based physiology assessment is able to provide similar information.
In 111 patients (120 vessels) treated with PCI, QFR was measured both before and after PCI. Pre-PCI QFR trace was used to discriminate functional patterns of CAD (focal, serial lesions, diffuse disease, combination). Functional CAD patterns were identified analyzing changes in the QFR virtual pullback trace (qualitative method) or after computation of the QFR virtual pullback index (QVP) (quantitative method). QVP calculation was based on the maximal QFR drop over 20 mm and the length of epicardial coronary segment with QFR most relevant drop. Then, the ability of the different functional patterns of CAD to predict post-PCI QFR value was tested.
By qualitative method, 51 (43%), 20 (17%), 15 (12%), and 34 (28%) vessels were classified as focal, serial focal lesions, diffuse disease, and combination, respectively. QVP values >0.71 and ≤0.51 predicted focal and diffuse patterns, respectively. Suboptimal PCI result (post-PCI QFR value ≤0.89) was present in 22 (18%) vessels. Its occurrence differed across functional patterns of CAD (focal 8% vs. serial lesions 15% vs. diffuse disease 33% vs. combination 29%, p=0.03). Similarly, QVP was correlated with post-PCI QFR value (r=0.62, 95% CI 0.50-0.72).
Our results suggest that functional patterns of CAD based on pre-PCI QFR trace can predict the functional outcome after PCI.
ClinicalTrials.gov , number NCT02811796. Date of registration: June 23, 2016.
在经皮冠状动脉介入治疗(PCI)之前进行基于导丝的冠状动脉生理学回撤能够区分冠状动脉疾病(CAD)的分布和程度,并能够预测功能 PCI 的结果。目前尚无研究调查基于定量血流比(QFR)的生理学评估是否能够提供类似的信息。
在 111 例(120 支血管)接受 PCI 治疗的患者中,在 PCI 前后测量了 QFR。使用 PCI 前的 QFR 迹线来区分 CAD 的功能模式(局灶性、连续病变、弥漫性疾病、联合)。通过分析 QFR 虚拟回撤迹线的变化(定性方法)或计算 QFR 虚拟回撤指数(QVP)(定量方法)来识别功能 CAD 模式。QVP 计算基于 20mm 内 QFR 的最大下降和 QFR 最相关下降的心肌外冠状动脉段的长度。然后,测试不同 CAD 功能模式预测 PCI 后 QFR 值的能力。
通过定性方法,51 支(43%)、20 支(17%)、15 支(12%)和 34 支(28%)血管分别被归类为局灶性、连续局灶性病变、弥漫性疾病和联合病变。QVP 值>0.71 和≤0.51 分别预测局灶性和弥漫性病变。亚最佳 PCI 结果(PCI 后 QFR 值≤0.89)发生在 22 支(18%)血管中。CAD 的不同功能模式之间的发生率不同(局灶性 8% vs. 连续病变 15% vs. 弥漫性疾病 33% vs. 联合病变 29%,p=0.03)。同样,QVP 与 PCI 后 QFR 值相关(r=0.62,95%CI 0.50-0.72)。
我们的结果表明,基于 PCI 前 QFR 迹线的 CAD 功能模式可以预测 PCI 后的功能结果。
ClinicalTrials.gov,编号 NCT02811796。注册日期:2016 年 6 月 23 日。