Validation of a predictive model for coronary artery disease in patients with diabetes

BackgroundNo reliable model can currently be used for predicting coronary artery disease (CAD) occurrence in patients with diabetes. We developed and validated a model predicting the occurrence of CAD in these patients.MethodsWe retrospectively enrolled patients with diabetes at Henan Provincial People's Hospital between 1 January 2020 and 10 June 2020, and collected data including demographics, physical examination results, laboratory test results, and diagnostic information from their medical records. The training set included patients (n = 1152) enrolled before 15 May 2020, and the validation set included the remaining patients (n = 238). Univariate and multivariate logistic regression analyses were performed in the training set to develop a predictive model, which were visualized using a nomogram. The model's performance was assessed by area under the receiver-operating characteristic curve (AUC) and Brier scores for both data sets.ResultsSex, diabetes duration, low-density lipoprotein, creatinine, high-density lipoprotein, hypertension, and heart rate were CAD predictors in diabetes patients. The model's AUC and Brier score were 0.753 [95% confidence interval (CI) 0.727-0.778] and 0.152, respectively, and 0.738 (95% CI 0.678-0.793) and 0.172, respectively, in the training and validation sets, respectively.ConclusionsOur model demonstrated favourable performance; thus, it can effectively predict CAD occurrence in diabetes patients.

Automated summary

A model was developed and validated to predict the occurrence of coronary artery disease (CAD) in patients with diabetes, incorporating predictors such as sex, diabetes duration, lipoprotein levels, creatinine, hypertension, and heart rate.