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冠状动脉CT血管造影与经导管主动脉瓣置换术(TAVI)规划相结合:CT血流储备分数(CT-FFR)在形态学上排除阻塞性冠状动脉疾病患者中的应用

Combined Coronary CT-Angiography and TAVI Planning: Utility of CT-FFR in Patients with Morphologically Ruled-Out Obstructive Coronary Artery Disease.

作者信息

Gohmann Robin Fabian, Seitz Patrick, Pawelka Konrad, Majunke Nicolas, Schug Adrian, Heiser Linda, Renatus Katharina, Desch Steffen, Lauten Philipp, Holzhey David, Noack Thilo, Wilde Johannes, Kiefer Philipp, Krieghoff Christian, Lücke Christian, Ebel Sebastian, Gottschling Sebastian, Borger Michael A, Thiele Holger, Panknin Christoph, Abdel-Wahab Mohamed, Horn Matthias, Gutberlet Matthias

机构信息

Department of Diagnostic and Interventional Radiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany.

Medical Faculty, University of Leipzig, Liebigstr. 27, 04103 Leipzig, Germany.

出版信息

J Clin Med. 2022 Feb 28;11(5):1331. doi: 10.3390/jcm11051331.

Abstract

: Coronary artery disease (CAD) is a frequent comorbidity in patients undergoing transcatheter aortic valve implantation (TAVI). If significant CAD can be excluded on coronary CT-angiography (cCTA), invasive coronary angiography (ICA) may be avoided. However, a high plaque burden may make the exclusion of CAD challenging, particularly for less experienced readers. The objective was to analyze the ability of machine learning (ML)-based CT-derived fractional flow reserve (CT-FFR) to correctly categorize cCTA studies without obstructive CAD acquired during pre-TAVI evaluation and to correlate recategorization to image quality and coronary artery calcium score (CAC). : In total, 116 patients without significant stenosis (≥50% diameter) on cCTA as part of pre-TAVI CT were included. Patients were examined with an electrocardiogram-gated CT scan of the heart and high-pitch scan of the torso. Patients were re-evaluated with ML-based CT-FFR (threshold = 0.80). The standard of reference was ICA. Image quality was assessed quantitatively and qualitatively. : ML-based CT-FFR was successfully performed in 94.0% (109/116) of patients, including 436 vessels. With CT-FFR, 76/109 patients and 126/436 vessels were falsely categorized as having significant CAD. With CT-FFR 2/2 patients but no vessels initially falsely classified by cCTA were correctly recategorized as having significant CAD. Reclassification occurred predominantly in distal segments. Virtually no correlation was found between image quality or CAC. : Unselectively applied, CT-FFR may vastly increase the number of false positive ratings of CAD compared to morphological scoring. Recategorization was virtually independently from image quality or CAC and occurred predominantly in distal segments. It is unclear whether or not the reduced CT-FFR represent true pressure ratios and potentially signifies pathophysiology in patients with severe aortic stenosis.

摘要

冠状动脉疾病(CAD)是接受经导管主动脉瓣植入术(TAVI)患者中常见的合并症。如果在冠状动脉CT血管造影(cCTA)上能够排除严重CAD,则可避免进行有创冠状动脉造影(ICA)。然而,高斑块负荷可能使CAD的排除具有挑战性,尤其是对于经验不足的阅片者。目的是分析基于机器学习(ML)的CT衍生血流储备分数(CT-FFR)对TAVI术前评估期间获得的无阻塞性CAD的cCTA研究进行正确分类的能力,并将重新分类与图像质量和冠状动脉钙化评分(CAC)相关联。

总共纳入了116例在TAVI术前CT检查中cCTA无明显狭窄(直径≥50%)的患者。患者接受了心电图门控心脏CT扫描和躯干高螺距扫描。使用基于ML的CT-FFR(阈值 = 0.80)对患者进行重新评估。参考标准为ICA。对图像质量进行了定量和定性评估。

94.0%(109/116)的患者成功进行了基于ML的CT-FFR检查,共包括436支血管。使用CT-FFR时,109例患者中的76例和436支血管中的126支被错误分类为患有严重CAD。使用CT-FFR时,最初2/2例患者但cCTA未最初错误分类的血管被正确重新分类为患有严重CAD。重新分类主要发生在远端节段。在图像质量或CAC之间几乎未发现相关性。

与形态学评分相比,不加选择地应用CT-FFR可能会大幅增加CAD假阳性评级的数量。重新分类几乎独立于图像质量或CAC,且主要发生在远端节段。尚不清楚降低的CT-FFR是否代表真正的压力比值,以及是否可能表明重度主动脉瓣狭窄患者的病理生理学情况。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0877/8910873/e694ab88d973/jcm-11-01331-g001.jpg

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