Department of Cardiology, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China.
Department of Cardiology, Beijing Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, People's Republic of China.
Adv Ther. 2023 Mar;40(3):975-989. doi: 10.1007/s12325-022-02400-1. Epub 2022 Dec 30.
Factors affecting the angiographic outcomes of coronary de novo lesions treated with drug-coated balloons (DCBs) have not been well illustrated. The aim of the study is to establish a nomogram for predicting the risk of suboptimal diameter stenosis (DS) at angiographic follow-up.
A retrospective analysis was performed on a cohort of patients who underwent DCB intervention for coronary de novo lesions with angiographic follow-up data. Multivariable logistic regression analysis was applied to determine the independent predictors of DS ≥ 30% at follow-up, and then a nomogram model was established and validated.
A total of 196 patients (313 lesions) were divided into the suboptimal (DS ≥ 30%) and optimal (DS < 30%) DS groups according to quantitative coronary angiography (QCA) measurements of the target lesions at follow-up. Seven independent factors including calcified lesions, true bifurcation lesions, immediate lumen gain rate (iLG%) < 20%, immediate diameter stenosis (iDS) ≥ 30%, DCB diameter/reference vessel diameter ratio (DCB/RVD) < 1.0, DCB length and mild dissection were identified. The area under the curve (AUC) (95% CI) of the receiver-operating characteristic (ROC) curve of the nomogram was 0.738 (0.683, 0.794). After the internal validation, the AUC (95% CI) was 0.740 (0.685, 0.795). The Hosmer-Lemeshow goodness of fit (GOF) test (χ = 6.57, P = 0.766) and the calibration curve suggested a good predictive consistency of the nomogram.
The well-calibrated nomogram could efficiently predict the suboptimal angiographic outcomes at follow-up. This model may be helpful to optimize lesion preparation to achieve optimal outcomes.
影响药物涂层球囊(DCB)治疗的冠状动脉新发病变血管造影结果的因素尚未得到充分说明。本研究旨在建立预测血管造影随访时亚最佳直径狭窄(DS)风险的列线图。
对接受 DCB 干预的冠状动脉新发病变患者进行回顾性分析,这些患者具有血管造影随访数据。采用多变量逻辑回归分析确定随访时 DS≥30%的独立预测因素,然后建立并验证列线图模型。
根据随访时目标病变的定量冠状动脉造影(QCA)测量值,196 例患者(313 处病变)分为亚最佳(DS≥30%)和最佳(DS<30%)DS 组。通过多变量逻辑回归分析,确定了 7 个独立的预测因素,包括钙化病变、真性分叉病变、即刻管腔获得率(iLG%)<20%、即刻直径狭窄(iDS)≥30%、DCB 直径/参考血管直径比(DCB/RVD)<1.0、DCB 长度和轻度夹层。该列线图的受试者工作特征(ROC)曲线下面积(AUC)(95%可信区间)为 0.738(0.683,0.794)。内部验证后,AUC(95%可信区间)为 0.740(0.685,0.795)。Hosmer-Lemeshow 拟合优度(GOF)检验(χ²=6.57,P=0.766)和校准曲线表明该列线图具有良好的预测一致性。
该校准良好的列线图可有效地预测随访时的亚最佳血管造影结果。该模型可能有助于优化病变准备,以实现最佳结果。