Construction and validation of coronary heart disease risk prediction model for general hospitals in Tacheng Prefecture, Xinjiang, China - Xu, Yikang; Ma, Jingru; Yang, Yang; Liu, Limin; Zhao, Xinran; Wang, Yu; Mijiti, Alimu; Cheng, Qiangru; Ma, Jun (Frontiers in Cardiovascular Medicine, 2024)
ObjectiveTo analyze the risk factors for coronary heart disease (CHD) in patients hospitalized in general hospitals in the Tacheng Prefecture, Xinjiang, and to construct and verify the nomogram prediction model for the risk of CHD.MethodsFrom June 2022 to June 2023, 489 CHD patients (CHD group) and 520 non-CHD individuals (control group) in Tacheng, Xinjiang, were retrospectively selected. Using a 7:3 ratio, patients were divided into a training group (706 cases) and a validation group (303 cases). General clinical data were compared, and key variables were screened using logistic regression (AIC). A CHD risk nomogram for Tacheng was constructed. Model performance was assessed using ROC AUC, calibration curves, and DCA.ResultsIn the training group, non-Han Chinese (OR = 2.93, 95% CI: 2.0–4.3), male (OR = 1.65, 95% CI: 1.0–2.7), alcohol consumption (OR = 1.82, 95% CI: 1.2–2.9), hyperlipidemia (OR = 2.41, 95% CI: 1.7–3.5), smoking (OR = 1.61, 95% CI: 1.0–2.6), diabetes mellitus (OR = 1.62, 95% CI: 1.1–2.4), stroke (OR = 2.39, 95% CI: 1.6–3.7), older age (OR = 1.08, 95% CI: 1.1–1.2), and larger waist circumference (OR = 1.04, 95% CI: 1.0–1.1) were the risk factors for coronary heart disease (all P < 0.05). The area under the curve (AUC) of the work characteristics of the subjects in the training group and the validation group were 0.80 (95% CI: 0.8–0.8) and 0.82 (95% CI: 0.8–0.9), respectively. The Hosmer-Lemeshow test indicated P = 0.325 for the training group and P = 0.130 for the validation group, with calibration curves closely fitting the ideal curve. The predicted values aligned well with actual values, and decision curve analysis results suggest that the model offers a net clinical benefit.ConclusionThe CHD risk prediction model developed in this study for general hospitals in Tacheng Prefecture, Xinjiang, demonstrates strong predictive performance and serves as a simple, user-friendly, cost-effective tool for medical personnel to identify high-risk groups for CHD.