Development and Validation of a Nomogram Prediction Model for Coronary Heart Disease in Diabetic Patients: A Study Based on the 2011-2020 NHANES Database

Objective: To analyze the risk and influencing factors of coronary heart disease (CHD) in patients with diabetes (DM), and to develop and validate a nomogram prediction model, providing a basis for the early diagnosis and individualized intervention of DM with CHD. Methods: This study is based on data from the National Health and Nutrition Examination Survey (NHANES). A total of 2,141 diabetic patients from 2011 to 2020 were included, randomly divided into a training set (n=149) and a validation set (n=642) with a 7:3 ratio. The least absolute shrinkage and selection operator (Lasso) regression analysis was used to screen risk factors, and a multivariate logistic regression model was developed to construct the DM-CHD nomogram prediction model. Model performance was internally validated using Receiver Operating Characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: Univariate analysis identified 14 factors as risk factors for DM-related CHD. Lasso regression further selected 7 key predictors: age (OR 1.06, CI 1.05-1.08, P<0.001), gender (OR 0.47, CI 0.36-0.63, P<0.001), hypertension (OR 1.85, CI 1.33-2.57, P<0.001), weight-adjusted waist circumference index (OR 1.50, CI 1.25-1.81, P<0.001), neutrophils (OR 1.09, CI 1.02-1.17, P=0.009), platelets (OR 0.99, CI 0.99-0.99, P<0.001), and triglycerides (OR 1.18, CI 1.08-1.30, P<0.001). The area under the curve (AUC) of ROC for the nomogram model was 0.758 (95% CI 0.728-0.789) in the training set and 0.747 (95% CI 0.699-0.796) in the validation set. Calibration curves and DCA indicated that the model had good predictive performance. The model’s reliability and clinical net benefit were further validated. Conclusion: The nomogram model developed in this study, based on multiple clinical indicators (age, gender, hypertension, weight-adjusted waist circumference index, neutrophils, platelets, and triglycerides), demonstrated high calibration performance and clinical net benefit in the validation set. This model has strong clinical applicability and can provide scientific evidence for the early diagnosis and individualized intervention of DM with CHD, with the potential to reduce the risk of CHD in diabetic patients.