Wu Haoyu, Liang Lei, Qiu Fuyu, Han Wenqi, Yang Zheng, Qi Jie, Deng Jizhao, Tang Yida, Shou Xiling, Chen Haichao
Department of Cardiology, Shaanxi Provincial People's Hospital, 710068 Xi'an, Shaanxi, China.
Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 310018 Hangzhou, Zhejiang, China.
Rev Cardiovasc Med. 2024 Jan 10;25(1):20. doi: 10.31083/j.rcm2501020. eCollection 2024 Jan.
The noninvasive computed tomography angiography-derived fractional flow reserve (CT-FFR) can be used to diagnose coronary ischemia. With advancements in associated software, the diagnostic capability of CT-FFR may have evolved. This study evaluates the effectiveness of a novel deep learning-based software in predicting coronary ischemia through CT-FFR.
In this prospective study, 138 subjects with suspected or confirmed coronary artery disease were assessed. Following indication of 30%-90% stenosis on coronary computed tomography (CT) angiography, participants underwent invasive coronary angiography and fractional flow reserve (FFR) measurement. The diagnostic performance of the CT-FFR was determined using the FFR as the reference standard.
With a threshold of 0.80, the CT-FFR displayed an impressive diagnostic accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), and negative predictive value (NPV) of 97.1%, 96.2%, 97.7%, 0.98, 96.2%, and 97.7%, respectively. At a 0.75 threshold, the CT-FFR showed a diagnostic accuracy, sensitivity, specificity, AUC, PPV, and NPV of 84.1%, 78.8%, 85.7%, 0.95, 63.4%, and 92.8%, respectively. The Bland-Altman analysis revealed a direct correlation between the CT-FFR and FFR ( 0.001), without systematic differences ( = 0.085).
The CT-FFR, empowered by novel deep learning software, demonstrates a strong correlation with the FFR, offering high clinical diagnostic accuracy for coronary ischemia. The results underline the potential of modern computational approaches in enhancing noninvasive coronary assessment.
无创计算机断层扫描血管造影衍生的血流储备分数（CT-FFR）可用于诊断冠状动脉缺血。随着相关软件的进步，CT-FFR的诊断能力可能已经发生了演变。本研究评估了一种基于深度学习的新型软件通过CT-FFR预测冠状动脉缺血的有效性。
在这项前瞻性研究中，对138名疑似或确诊为冠状动脉疾病的受试者进行了评估。在冠状动脉计算机断层扫描（CT）血管造影显示30%-90%狭窄后，参与者接受了有创冠状动脉造影和血流储备分数（FFR）测量。以FFR作为参考标准确定CT-FFR的诊断性能。
阈值为0.80时，CT-FFR的诊断准确性、敏感性、特异性、受试者操作特征曲线下面积（AUC）、阳性预测值（PPV）和阴性预测值（NPV）分别为97.1%、96.2%、97.7%、0.98、96.2%和97.7%。在阈值为0.75时，CT-FFR的诊断准确性、敏感性、特异性、AUC、PPV和NPV分别为84.1%、78.8%、85.7%、0.95、63.4%和92.8%。Bland-Altman分析显示CT-FFR与FFR之间存在直接相关性（r = 0.001），无系统差异（P = 0.085）。
由新型深度学习软件赋能的CT-FFR与FFR显示出很强的相关性，为冠状动脉缺血提供了较高的临床诊断准确性。结果强调了现代计算方法在增强无创冠状动脉评估方面的潜力。
