Yang Shuai, Leng Shuang, Wang Zhouchi, Fam Jiang Ming, Low Adrian Fatt Hoe, Tan Ru-San, Chai Ping, Teo Lynette, Chin Chee Yang, Allen John C, Chan Mark Yan-Yee, Yeo Khung Keong, Wong Aaron Sung Lung, Lim Soo Teik, Wu Qinghua, Zhong Liang
Department of Cardiology, Henan Provincial Chest Hospital, Zhengzhou, Henan, China.
National Heart Research Institute Singapore, National Heart Centre Singapore, Singapore, Singapore.
Front Cardiovasc Med. 2025 Jun 20;12:1550550. doi: 10.3389/fcvm.2025.1550550. eCollection 2025.
Quantitative coronary angiography (QCA) has significantly contributed to the diagnosis of coronary artery disease. This study aimed to construct and validate a QCA-based prediction model, represented as a nomogram, for predicting ischemic lesions defined by invasive fractional flow reserve (FFR) ≤ 0.80.
In this multi-centre study, we enrolled 220 patients with 303 interrogated vessels who underwent FFR measurements during clinically indicated invasive coronary angiography. QCA predictors for ischemic lesions were extracted to construct a nomogram model using Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis of the development set ( = 113 patients). An external validation ( = 107 patients) was performed to assess the nomogram model's discrimination and consistency.
Lesion length, minimal lumen diameter, stenosis flow reserve, percent diameter stenosis by visual estimation, and weight were included as predictors in the nomogram. The nomogram yielded an area under the curve (AUC) of 0.922 and 0.912 at per-vessel and per-patient levels, respectively, in the development set. In the validation set, it achieved an AUC of 0.915 and 0.912 at per-vessel and per-patient levels, respectively. Per-vessel accuracy, sensitivity, and specificity derived from the nomogram were 86.5%, 88.2%, 86.2% in the development cohort and 84.2%, 85.5%, and 83.1% in the validation cohort. For per-patient analysis, the corresponding values were 85.8%, 85.7%, 86.0% in the development cohort and 82.2%, 83.3%, 81.1% in the validation cohort.
The nomogram may be useful for predicting ischemic lesions using QCA measurements and the LASSO regression algorithm, with external validation indicating potential predictive value in cardiology care settings.
定量冠状动脉造影（QCA）对冠状动脉疾病的诊断有显著贡献。本研究旨在构建并验证一种基于QCA的预测模型，以列线图表示，用于预测由有创血流储备分数（FFR）≤0.80定义的缺血性病变。
在这项多中心研究中，我们纳入了220例患者的303条受检血管，这些患者在临床指征的有创冠状动脉造影期间接受了FFR测量。提取缺血性病变的QCA预测因子，使用最小绝对收缩和选择算子（LASSO）回归分析开发集（n = 113例患者）构建列线图模型。进行外部验证（n = 107例患者）以评估列线图模型的辨别力和一致性。
病变长度、最小管腔直径、狭窄血流储备、视觉估计的直径狭窄百分比和权重被纳入列线图的预测因子。在开发集中，列线图在每血管和每患者水平的曲线下面积（AUC）分别为0.922和0.912。在验证集中，它在每血管和每患者水平的AUC分别为0.915和0.912。列线图得出的每血管准确性、敏感性和特异性在开发队列中分别为86.5%、88.2%、86.2%，在验证队列中分别为84.2%·、85.5%和83.1%。对于每患者分析，开发队列中的相应值分别为85.8%、85.7%、86.0%，验证队列中为82.2%、83.3%、81.1%。
该列线图可能有助于使用QCA测量和LASSO回归算法预测缺血性病变，外部验证表明其在心脏病护理环境中具有潜在的预测价值。
