Guangdong Academy Research on VR Industry, Foshan University, #18 Jiangwan 1st Road, Foshan, 528000, Guangdong, China.
Medical Imaging Center, The First Affiliated Hospital of Jinan University, No 613 Huangpu Dadao West, Guangzhou, 510630, China.
Eur Radiol. 2021 Sep;31(9):7039-7046. doi: 10.1007/s00330-021-07771-7. Epub 2021 Feb 25.
This study aims to investigate the safety and feasibility of using a deep learning algorithm to calculate computed tomography angiography-based fractional flow reserve (DL-FFRCT) as an alternative to invasive coronary angiography (ICA) in the selection of patients for coronary intervention.
Patients (N = 296) with symptomatic coronary artery disease identified by coronary computed tomography angiography (CTA) with stenosis over 50% were retrospectively enrolled from a single centre in this study. ICA-guided interventions were performed in patients at admission, and DL-FFRCT was conducted retrospectively. The influences on decision-making by using DL-FFRCT and the clinical outcome were compared to those of ICA-guided care for symptomatic CAD at the 2-year follow-up evaluation.
Two hundred forty-three patients were evaluated. Up to 72% of diagnostic ICA studies could have been avoided by using a DL-FFRCT value > 0.8 as a cut-off for intervention. A similar major adverse cardiovascular event (MACE) rate was observed in patients who underwent revascularisation with a DL-FFRCT value ≤ 0.8 (2.9%) compared to that of ICA-guided interventions (3.3%) (stented lesions with ICA stenosis > 75%) (p = 0.838).
DL-FFRCT can reduce the need for diagnostic coronary angiography when identifying patients suitable for coronary intervention. A low MACE rate was found in a 2-year follow-up investigation.
• Seventy-two percent of diagnostic ICA studies could have been avoided by using a DL-FFRCT value > 0.8 as a cut-off for intervention. • Coronary artery stenting based on the diagnosis by using a 320-detector row CT scanner and a positive DL-FFRCT value could potentially be associated with a lower occurrence rate of major adverse cardiovascular events (2.9%) within the first 2 years. • A low event rate was found when intervention was performed in tandem lesions with haemodynamic significance based on DL-FFRCT < 0.8 as a cut-off value.
本研究旨在探讨使用深度学习算法计算基于计算机断层扫描血管造影的分数血流储备（DL-FFRCT）作为替代有创冠状动脉造影（ICA）在选择冠状动脉介入治疗患者中的安全性和可行性。
回顾性纳入单中心 296 例经冠状动脉计算机断层扫描血管造影（CTA）发现狭窄超过 50%的有症状冠状动脉疾病患者。入院时对患者进行 ICA 引导的介入治疗，并进行回顾性 DL-FFRCT。比较使用 DL-FFRCT 对决策的影响和临床结果与 ICA 指导的有症状 CAD 治疗在 2 年随访评估中的结果。
243 例患者接受评估。以 DL-FFRCT 值>0.8 作为介入的截断值，可避免 72%的诊断性 ICA 研究。在接受 DL-FFRCT 值≤0.8 的血运重建的患者中，观察到类似的主要不良心血管事件（MACE）发生率（2.9%）与 ICA 指导介入（3.3%）（ICA 狭窄>75%的支架病变）相比（p=0.838）。
DL-FFRCT 可减少识别适合冠状动脉介入治疗的患者进行诊断性冠状动脉造影的需求。在 2 年随访调查中发现，MACE 发生率较低。
使用 DL-FFRCT 值>0.8 作为介入的截断值，可避免 72%的诊断性 ICA 研究。
基于 320 层 CT 扫描仪和阳性 DL-FFRCT 值诊断进行冠状动脉支架置入术，在最初 2 年内可能与主要不良心血管事件（MACE）发生率较低（2.9%）相关。
在根据 DL-FFRCT<0.8 作为截断值对有血流动力学意义的串联病变进行干预时，发现事件发生率较低。
