Wenqiang Wang,1 Zonghan Du,2 Peng Xie3 

1Department of Nursing, Beijing Anzhen Nanchong Hospital of Capital Medical University & Nanchong Central Hospital, Nanchong, Sichuan, 637000, People’s Republic of China; 2Department of Gastroenterology, Beijing Anzhen Nanchong Hospital of Capital Medical University & Nanchong Central Hospital, Nanchong, Sichuan, 637000, People’s Republic of China; 3Department of Cardiovascular Medicine, Beijing Anzhen Nanchong Hospital of Capital Medical University & Nanchong Central Hospital, Nanchong, Sichuan, 637000, People’s Republic of China

Correspondence: Peng Xie, Department of Cardiovascular Medicine, Beijing Anzhen Nanchong Hospital of Capital Medical University & Nanchong Central Hospital, Nanchong, Sichuan, 637000, People’s Republic of China, Email billxiewang@163.com

Objective: Constructing a predictive model to evaluate the risk of coronary heart disease (CHD) for early identification of patients with CHD risk based on new metabolic indicators.
Methods: A retrospective analysis was conducted based on NHANES databases. Collect general information, cardiovascular comorbidities, new metabolic indicators (BMI, Triglycerides/Glucose, Waist Circumference-to-Height ratio, Cholesterol/HDL, Triglycerides/HDL, Cardiometabolic index, Neutrophil percentage-to-albumin ratio, etc). The least absolute shrinkage and selection operator (LASSO) regression model and multivariate logistic regression were performed to analyze the risk factors of CHD and develop a CHD risk predictive model using R software.
Results: A total of 3741 individuals were included and 160 (4.3%) individuals had CHD. According to the results of the LASSO regression model and multivariate logistic regression, 9 factors were related to CHD such as Hypertension (Yes), Cardiometabolic index (≥ 0.672), Mean arterial pressure (< 70 mmHg), Gender (male), COPD (Yes), Age (> 69), Neutrophil percentage-to-albumin ratio (≥ 1.465), Thyroid problem (Yes) and Stroke (Yes), which were developed a CHD risk prediction nomogram. The nomogram presented good discrimination with a C-index value of 0.869 (95% confidence interval: 0.82196– 0.91604), AUC (0.868) and good calibration. Based on the maximum point of the Youden index, the individuals with a score greater than 136.5 are at high risk for CHD.
Conclusion: A risk prediction model for CHD has been developed based on new metabolic indicators in this study and boasts a relatively high accuracy in the early identification of patients with CHD risk. It may help clinicians develop strategies to prevent CHD and improve care quality.

Keywords: CHD, risk factors, predictive model, metabolic indicators