用于诊断血流动力学显著性冠状动脉疾病的自动计算机断层扫描衍生分数血流储备模型:一项前瞻性验证研究。

European heart journal. Imaging methods and practice Pub Date : 2024-09-30 eCollection Date: 2024-07-01 DOI:10.1093/ehjimp/qyae102
Anders T Bråten, Fredrik E Fossan, Lucas O Muller, Arve Jørgensen, Knut H Stensæth, Leif R Hellevik, Rune Wiseth
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摘要

目的:本研究旨在评估一种新型计算机断层扫描衍生分数血流储备(CT-FFR)算法的诊断性能,并比较其在三个预定位点的准确性:(i) 有创 FFR 测量位置(CT-FFRatloc),(ii) 由集成在算法中的自动模块确定的选定位点(CT-FFRauto),(iii) 血管远端(CT-FFRdistal):我们前瞻性地招募了 108 名冠状动脉疾病症状稳定、冠状动脉计算机断层扫描血管造影术(CCTA)至少有一处疑似阻塞性病变的连续患者。CT-FFR 与作为金标准的有创 FFR 进行了验证,用 FFR ≤ 0.80 来定义心肌缺血。CT-FFRatloc 与有创 FFR 显示出良好的相关性(r = 0.67),与 CCTA 相比,在病变[曲线下面积 (AUC) 0.83 vs. 0.65,P < 0.001]和患者水平(AUC 0.87 vs. 0.74,P = 0.007)上都提高了检测心肌缺血的能力。CT-FFRauto 的诊断准确性与 CT-FFRatloc 相似,特异性则比 CT-FFRdistal 明显提高(86% 对 49%,P<0.001)。高端 CT 质量提高了 CT-FFRauto 的诊断性能,其 AUC 为 0.92;同样,中低端冠状动脉钙化评分患者的 CT-FFRauto 诊断性能也有所提高,其 AUC 为 0.88:采用自动模块确定 CT-FFR 评估部位是可行的,而且 CT-FFRauto 与 CT-FFRatloc 相比,诊断准确性相当。与CCTA相比,CT-FFRatloc和CT-FFRauto都提高了诊断性能,与CT-FFRdistal相比,特异性更高。高端 CT 质量和中低钙负荷提高了我们算法的诊断性能:NCT03045601。
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Automated computed tomography-derived fractional flow reserve model for diagnosing haemodynamically significant coronary artery disease: a prospective validation study.

Aims: This study aims to assess the diagnostic performance of a novel computed tomography-derived fractional flow reserve (CT-FFR) algorithm and to compare its accuracy at three predefined sites: (i) at the location of invasive FFR measurements (CT-FFRatloc), (ii) at selected sites determined by an automated module integrated within the algorithm (CT-FFRauto), and (iii) distally in the vessel (CT-FFRdistal).

Methods and results: We prospectively recruited 108 consecutive patients with stable symptoms of coronary artery disease and at least one suspected obstructive lesion on coronary computed tomography angiography (CCTA). CT-FFR was validated against invasive FFR as gold standard using FFR ≤ 0.80 to define myocardial ischaemia. CT-FFRatloc showed good correlation with invasive FFR (r = 0.67) and improved the ability to detect myocardial ischaemia compared with CCTA at both lesion [area under the curve (AUC) 0.83 vs. 0.65, P < 0.001] and patient level (AUC 0.87 vs. 0.74, P = 0.007). CT-FFRauto demonstrated similar diagnostic accuracy to CT-FFRatloc and significantly improved specificity compared with CT-FFRdistal (86% vs. 49%, P < 0.001). High end CT quality improved the diagnostic performance of CT-FFRauto, demonstrating an AUC of 0.92; similarly, the performance was improved in patients with low-to-intermediate coronary artery calcium score with an AUC of 0.88.

Conclusion: Implementing an automated module to determine the site of CT-FFR evaluations was feasible, and CT-FFRauto demonstrated comparable diagnostic accuracy to CT-FFRatloc when assessed against invasive FFR. Both CT-FFRatloc and CT-FFRauto improved the diagnostic performance compared with CCTA and improved specificity compared with CT-FFRdistal. High end CT quality and low-to-intermediate calcium burden improved the diagnostic performance of our algorithm.

Clinicaltrialsgov identifier: NCT03045601.

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