Combined cCTA and TAVR Planning for Ruling Out Significant CAD

医学 冠状动脉疾病 放射科 心脏病学 内科学 阀门更换 部分流量储备 血管造影 计算机辅助设计 狭窄 计算机断层血管造影 冠状动脉造影 心肌梗塞 工程制图 工程类
作者
Robin F. Gohmann,Konrad Pawelka,Patrick Seitz,Nicolas Majunke,Linda Heiser,Katharina Renatus,Steffen Desch,Philipp Lauten,David Holzhey,Thilo Noack,Johannes Wilde,Philipp Kiefer,Christian Krieghoff,Christian Lücke,Sebastian Gottschling,Sebastian Ebel,Michael A. Borger,Hölger Thiele,Christoph Panknin,Matthias Horn
出处
期刊:Jacc-cardiovascular Imaging [Elsevier]
卷期号:15 (3): 476-486 被引量:40
标识
DOI:10.1016/j.jcmg.2021.09.013
摘要

The purpose of this study was to analyze the ability of machine-learning (ML)-based computed tomography (CT)-derived fractional flow reserve (CT-FFR) to further improve the diagnostic performance of coronary CT angiography (cCTA) for ruling out significant coronary artery disease (CAD) during pre-transcatheter aortic valve replacement (TAVR) evaluation in patients with a high pre-test probability for CAD.CAD is a frequent comorbidity in patients undergoing TAVR. Current guidelines recommend its assessment before TAVR. If significant CAD can be excluded on cCTA, invasive coronary angiography (ICA) may be avoided. Although cCTA is a very sensitive test, it is limited by relatively low specificity and positive predictive value, particularly in high-risk patients.Overall, 460 patients (age 79.6 ± 7.4 years) undergoing pre-TAVR CT were included and examined with an electrocardiogram-gated CT scan of the heart and high-pitch scan of the vascular access route. Images were evaluated for significant CAD. Patients routinely underwent ICA (388/460), which was omitted at the discretion of the local Heart Team if CAD could be effectively ruled out on cCTA (72/460). CT examinations in which CAD could not be ruled out (CAD+) (n = 272) underwent additional ML-based CT-FFR.ML-based CT-FFR was successfully performed in 79.4% (216/272) of all CAD+ patients and correctly reclassified 17 patients as CAD negative. CT-FFR was not feasible in 20.6% because of reduced image quality (37/56) or anatomic variants (19/56). Sensitivity, specificity, positive predictive value, and negative predictive value were 94.9%, 52.0%, 52.2%, and 94.9%, respectively. The additional evaluation with ML-based CT-FFR increased accuracy by Δ+3.4% (CAD+: Δ+6.0%) and raised the total number of examinations negative for CAD to 43.9% (202/460).ML-based CT-FFR may further improve the diagnostic performance of cCTA by correctly reclassifying a considerable proportion of patients with morphological signs of obstructive CAD on cCTA during pre-TAVR evaluation. Thereby, CT-FFR has the potential to further reduce the need for ICA in this challenging elderly group of patients before TAVR.

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