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Published on: August 9, 2024
Explainable machine learning model of coronary artery disease combined with diabetes: development and validation
Xujie Wang1, Shipeng Wang2, Xuhui Liu3
1Department of Emergency ICU, The Affiliated Hospital of Qinghai University, Xining, Qinghai, China.
Insights
Machine learning models accurately predict mortality in patients with coronary artery disease and diabetes. These models utilize clinical parameters for personalized risk stratification, improving patient care.
Area of Science:
- Cardiology
- Diabetology
- Machine Learning in Medicine
Background:
- Coronary artery disease (CAD) and diabetes mellitus share a strong bidirectional relationship, increasing cardiovascular risk and worsening prognosis.
- Accurate prognostication for patients with comorbid CAD and diabetes is crucial for effective therapeutic decisions.
- Predicting adverse outcomes using accessible clinical parameters holds significant clinical value for this patient group.
Purpose of the Study:
- To develop and validate machine learning-based predictive models for assessing prognosis in patients with comorbid CAD and diabetes.
- To identify independent predictors of adverse outcomes in this population.
- To create a clinical tool for individualized risk stratification and decision-making.
Main Methods:
- A retrospective cohort study of 389 patients with comorbid CAD and diabetes.
- Feature selection using LASSO regression followed by backward stepwise Cox regression.
- Development of a nomogram incorporating independent predictors for clinical application.
- Model performance evaluation using discrimination metrics, calibration plots, and decision curve analysis.
Main Results:
- Eight independent predictors were identified: hemoglobin, INR, albumin, NT-proBNP, age, fibrinogen, diuretic use, and digitalis therapy.
- The integrated model demonstrated strong discriminative performance for mortality prediction in both training and validation cohorts (AUCs ranging from 0.798 to 0.846).
- A nomogram was developed, showing reliable performance and clinical utility for individualized risk stratification.
Conclusions:
- A multimodal prognostic model effectively predicts all-cause mortality in patients with CAD and diabetes comorbidity.
- The developed nomogram offers potential utility for personalized risk estimation, aiding clinical decision-making and patient stratification.
Background:
Coronary artery disease (CAD) demonstrates a strong bidirectional association with diabetes mellitus, which not only elevates cardiovascular disease risk but also correlates with poorer clinical prognosis. Prognostication in patients with comorbid CAD and diabetes remains a critical clinical challenge, significantly influencing therapeutic decision-making. Leveraging readily available clinical parameters for predicting adverse outcomes in this population offers substantial clinical value. This investigation employs machine learning algorithms to develop predictive models for prognostic assessment in CAD patients with diabetes comorbidity.
Method:
We conducted a retrospective cohort study of 389 patients with comorbid coronary artery disease (CAD) and diabetes mellitus. The cohort was randomly allocated into a training set (n = 273) and an internal validation set (n = 116). Feature selection utilized LASSO regression followed by backward stepwise Cox regression analysis. A nomogram incorporating independent predictors was developed for clinical application. Model performance was assessed through discrimination metrics, calibration plots, and decision curve analysis (DCA). Random survival forest analysis validated the clinical significance of selected variables.
Result:
Our modeling approach employed a systematic methodology: LASSO regression for initial feature selection followed by backward stepwise Cox regression analysis, yielding eight independent predictors.The final model incorporated hemoglobin, INR, albumin, NT-proBNP, age, fibrinogen, diuretic use, and digitalis therapy. The integrated model demonstrated strong discriminative performance for mortality prediction across both training (AUC = 0.846, 0.838, 0.82) and validation cohorts (AUC = 0.824, 0.813, 0.798) at 3-, 5-, and 8-year intervals. Calibration plots and decision curve analysis confirmed model reliability and clinical utility over time. A nomogram was developed to facilitate individualized risk stratification. Kaplan-Meier analysis showed significant survival stratification by individual predictors, and restricted cubic spline analysis identified non-linear associations between continuous variables and mortality. Random survival forest analysis prioritized five key predictors (hemoglobin, INR, albumin, NT-proBNP, age). Comparative evaluation against the 9-variable model confirmed superior performance of the comprehensive model across all timepoints.
Conclusion:
Our multimodal prognostic model demonstrated robust performance in predicting all-cause mortality among patients with coronary artery disease and diabetes comorbidity. The nomogram's capacity for personalized risk estimation offers potential utility in clinical decision-making and patient stratification.
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