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Updated: Aug 16, 2025
Modeling and Evaluation of Murine Diabetic Cardiomyopathy Model
Published on: November 29, 2024
Validation of a predictive model for coronary artery disease in patients with diabetes
Junhong Xu1, Qiongrui Zhao2, Juan Li3
1Department of Clinical Microbiology, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, People's Hospital of Henan University, Zhengzhou, Henan.
Insights
A new model effectively predicts coronary artery disease (CAD) in diabetes patients. Key predictors include sex, diabetes duration, and lipid levels, offering improved risk assessment for this population.
Area of Science:
- Cardiology
- Diabetology
- Predictive Modeling
Background:
- Current models for predicting coronary artery disease (CAD) in diabetic patients lack reliability.
- Developing a dependable predictive model for CAD in diabetes is a critical unmet need.
Purpose of the Study:
- To develop and validate a predictive model for coronary artery disease (CAD) occurrence specifically in patients with diabetes.
- To identify key clinical and laboratory predictors of CAD in this high-risk population.
Main Methods:
- Retrospective enrollment of 1390 diabetes patients from Henan Provincial People's Hospital.
- Development of a predictive model using univariate and multivariate logistic regression on a training set (n=1152).
- Validation of the model's performance using Area Under the Curve (AUC) and Brier scores on a separate validation set (n=238).
Main Results:
- Identified significant predictors of CAD in diabetes patients: sex, diabetes duration, low-density lipoprotein, creatinine, high-density lipoprotein, hypertension, and heart rate.
- The developed model achieved an AUC of 0.753 in the training set and 0.738 in the validation set.
- Brier scores were 0.152 (training) and 0.172 (validation), indicating favorable model performance.
Conclusions:
- The developed predictive model demonstrates favorable performance in identifying patients with diabetes at risk of CAD.
- This model offers a valuable tool for effectively predicting CAD occurrence in the diabetic population.
- The identified predictors can aid clinicians in risk stratification and early intervention strategies.
Background:
No 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.
Methods:
We 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.
Results:
Sex, 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.
Conclusions:
Our model demonstrated favourable performance; thus, it can effectively predict CAD occurrence in diabetes patients.
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