Machine learning-based prediction of three-year mortality in elderly inpatients with coronary artery disease combined
Shihui Fu1, Zilei Zhao2, Xuhui Liu3
1Department of Cardiology, Hainan Hospital of Chinese PLA General Hospital, Hainan Geriatric Disease Clinical Medical Research Center, Hainan Branch of China Geriatric Disease Clinical Research Center, Sanya, China; Department of Geriatric Cardiology, Chinese PLA General Hospital, Beijing, China.
Insights
Accurate prediction of three-year mortality in elderly patients with coronary artery disease and heart failure is crucial. Logistic Regression demonstrated superior performance in predicting survival outcomes for this high-risk group.
Area of Science:
- Cardiology
- Geriatrics
- Medical Informatics
Background:
- Accurate survival prediction is vital for managing elderly patients with coronary artery disease (CAD) and heart failure (HF).
- Early intervention and optimized treatment depend on reliable prognostic models.
Purpose of the Study:
- To develop and evaluate machine learning models for predicting three-year mortality in elderly inpatients with combined CAD and HF.
- To identify key predictors of mortality in this patient population.
Main Methods:
- A cohort of 987 elderly inpatients with CAD was randomly split into training (70%) and validation (30%) sets.
- Five machine learning algorithms, including Logistic Regression and Random Forest, were employed to build predictive models.
- Feature selection was performed using LASSO and cross-validation to identify significant mortality predictors.
Main Results:
- The three-year mortality rate in the cohort was 56.46%.
- Logistic Regression achieved the best performance with an AUC of 0.9014 and an accuracy of 0.8764.
- Key predictors identified included age, NT-proBNP, albumin, serum creatinine, and interventricular septum thickness.
Conclusions:
- Logistic Regression is a highly effective tool for predicting three-year mortality in elderly patients with CAD and HF.
- The developed model and identified risk factors can aid in clinical decision-making and patient management.
Objective:
Accurate prediction of survival outcome is essential for early intervention and treatment optimization. This study aimed to develop a model utilizing machine learning techniques for predicting three-year mortality in elderly inpatients with coronary artery disease (CAD) combined with heart failure (HF).
Methods:
This study enrolled 987 elderly inpatients with CAD. This cohort was randomly divided into the training and validation datasets in a 7:3 ratio. Five machine learning methods, including Logistic Regression, Random Forest, Support Vector Machine, eXtreme Gradient Boosting, and Gradient Boosting Decision Trees, were implemented to construct predictive models.
Results:
Overall, the median age of this cohort was 85 [81,89] years. Three-year mortality in elderly inpatients with CAD combined with HF was 56.46%. The least absolute shrinkage and selection operator method and five-fold cross-validation identified that ten features were significantly associated with three-year mortality. Logistic Regression showed better performance than other models in the Brier Score, Area Under The Curve, Accuracy, Precision, Recall, and F1 Score of 0.1105, 0.9014, 0.8764, 0.9167, 0.8627, and 0.8889, respectively. The Shapley Additive exPlanations method revealed that age, interventricular septum thickness, gamma gap, serum creatinine, N-terminal pro-B-type natriuretic peptide (NT.proBNP), and neutrophil-to-lymphocyte ratio were identified as risk factors, and mean systolic blood pressure, hemoglobin, albumin, and sodium were protective factors. Age, albumin, and NT.proBNP were three features most associated with three-year mortality. The network application could be available at https://cad-hf-predict.tracebook.org.cn.
Conclusion:
Logistic Regression exhibits excellent predictive performance for predicting three-year mortality in elderly inpatients with CAD combined with HF.
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