Shi Y-C, Zheng Z, Wang P, Wu Y-X, Cheng Z-C, Jian W, Liu Y-C, Liu J-H
Center for Coronary Artery Disease (CCAD), Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing, China.
Eur Rev Med Pharmacol Sci. 2022 Nov;26(21):8011-8021. doi: 10.26355/eurrev_202211_30155.
Some previous studies have analyzed potential predictors related to the high incidence rate of coronary artery disease (CAD) and established a relevant nomogram for CAD in patients before coronary angiography (CAG). Nevertheless, there are still few models to predict chronic total occlusion (CTO). In this study, we aimed to construct a risk model and nomogram that could effectively predict the probability of CTO before CAG.
In total, the derivation set (n=1,105) and the validation set (n=368), which included patients with CAG diagnosis of CTO, were collected. A statistical difference test was performed for clinical, demography, echocardiography, medication history, laboratory indexes, and angiography. Univariate and multivariate logistic regression analysis were performed to determine the independent risk factors that affect the diagnosis of CTO. A nomogram was established and validated based on the independent predictors. The area under the curve (AUC), the calibration curve, and the decision curve analysis (DCA) were used to evaluate the nomogram.
The incidence of CTO within CAD was 21.5%. Univariate and multivariate logistic regression analysis revealed that risk factors for gender (male), neutrophil percentage (NE%), hematocrit (HCT), total cholesterol (TC), high-density lipoprotein cholesterol (HDL), ejection fraction (EF), troponin I (TnI), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) were independent predictors of CTO. A nomogram was constructed incorporating these independent predictors with good discrimination (0.746 in the C-index) and external validation (0.741 in the C-index). The calibration curves and the DCA showed the reliability and accuracy of this clinical prediction model.
The nomogram, composed of gender, NE%, HCT, TC, HDL, EF, TnI, and NT-proBNP, can be used for the prediction of CTO in CAD patients, which opens a great possibility of enriching the means to predict the prognosis of these patients in clinical practice. More studies are needed to validate the effectiveness of this nomogram in other populations.
既往一些研究分析了与冠状动脉疾病（CAD）高发病率相关的潜在预测因素，并在冠状动脉造影（CAG）前为CAD患者建立了相关列线图。然而，仍鲜有模型可用于预测慢性完全闭塞（CTO）。在本研究中，我们旨在构建一个能有效预测CAG前CTO发生概率的风险模型和列线图。
共收集了推导集（n = 1105）和验证集（n = 368），其中包括经CAG诊断为CTO的患者。对临床、人口统计学、超声心动图、用药史、实验室指标及血管造影结果进行统计学差异检验。进行单因素和多因素逻辑回归分析以确定影响CTO诊断的独立危险因素。基于独立预测因素建立并验证列线图。采用曲线下面积（AUC）、校准曲线及决策曲线分析（DCA）对列线图进行评估。
CAD患者中CTO的发生率为21.5%。单因素和多因素逻辑回归分析显示，性别（男性）、中性粒细胞百分比（NE%）、血细胞比容（HCT）、总胆固醇（TC）、高密度脂蛋白胆固醇（HDL）、射血分数（EF）、肌钙蛋白I（TnI）及N末端B型利钠肽原（NT-proBNP）等危险因素是CTO的独立预测因素。构建了包含这些独立预测因素的列线图，其具有良好的区分度（C指数为0.746）和外部验证性（C指数为0.741）。校准曲线和DCA显示了该临床预测模型的可靠性和准确性。
由性别、NE%、HCT、TC、HDL、EF、TnI和NT-proBNP组成的列线图可用于预测CAD患者的CTO，这为在临床实践中丰富预测这些患者预后的手段开辟了很大可能性。需要更多研究来验证该列线图在其他人群中的有效性。
