Development and validation of a novel nomogram to predict chronic total occlusion before coronary angiography

Y.-C. Shi, Z. Zheng, P. Wang, Y.-X. Wu, Z.-C. Cheng, W. Jian, Y.-C. Liu, J.-H. Liu

Center for Coronary Artery Disease (CCAD), Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing, China. liujinghua@vip.sina.com

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• Cardiology

OBJECTIVE: 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.

PATIENTS AND METHODS: 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.

RESULTS: 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.

CONCLUSIONS: 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.

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