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Construction and Verification of a Frailty Risk Prediction Model for Elderly Patients with Coronary Heart Disease
Jiao-Yu Cao1, Li-Xiang Zhang1, Xiao-Juan Zhou1
1Department of Cardiology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, 230001 Hefei, Anhui, China.
Insights
A new machine learning model accurately predicts frailty risk in elderly patients with coronary heart disease (CHD). This tool uses key health indicators to identify individuals needing early intervention for better outcomes.
Area of Science:
- Gerontology
- Cardiology
- Artificial Intelligence in Healthcare
Background:
- Elderly patients with coronary heart disease (CHD) face significant frailty risks.
- Frailty assessment is crucial for managing cardiovascular health in aging populations.
- Early identification of frailty can improve patient outcomes and healthcare management.
Purpose of the Study:
- To develop and validate a machine learning (ML) model for predicting frailty risk in elderly CHD patients.
- To identify key clinical and demographic factors associated with frailty in this cohort.
- To provide a reliable tool for clinical assessment of frailty in cardiology settings.
Main Methods:
- A cohort of 1170 elderly CHD patients was recruited between November 2020 and May 2023.
- Least Absolute Shrinkage and Selection Operator (LASSO) regression identified significant frailty predictors.
- Multiple ML models, including AdaBoost, were trained and validated using receiver operating characteristic (ROC) curve analysis.
Main Results:
- LASSO identified activities of daily living (ADL) score, hemoglobin, lipid profiles (LDL-C, TC), depression, cardiac function, comorbidities (cerebrovascular disease, diabetes), solitary living, and age as key predictors.
- The Adaptive Boosting (AdaBoost) model demonstrated the highest predictive accuracy.
- AdaBoost achieved an Area Under the ROC Curve (AUC) of 0.803 in the validation group, indicating strong predictive performance.
Conclusions:
- The developed AdaBoost ML model effectively predicts frailty risk in elderly patients with CHD.
- The model leverages validated frailty determinants for robust risk assessment.
- This tool offers a reliable method for clinicians to assess and manage frailty in this vulnerable population.
Background:
This study aimed to develop a machine learning-based predictive model for assessing frailty risk among elderly patients with coronary heart disease (CHD).
Methods:
From November 2020 to May 2023, a cohort of 1170 elderly patients diagnosed with CHD were enrolled from the Department of Cardiology of a tier-3 hospital in Anhui Province, China. Participants were randomly divided into a development group and a validation group, each containing 585 patients in a 1:1 ratio. Least absolute shrinkage and selection operator (LASSO) regression was employed in the development group to identify key variables influencing frailty among patients with CHD. These variables informed the creation of a machine learning prediction model, with the most accurate model selected. Predictive accuracy was subsequently evaluated in the validation group through receiver operating characteristic (ROC) curve analysis.
Results:
LASSO regression identified the activities of daily living (ADL) score, hemoglobin, low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), depression, cardiac function classification, cerebrovascular disease, diabetes, solitary living, and age as significant predictors of frailty among elderly patients with CHD in the development group. These variables were incorporated into a logistic regression model and four machine learning models: extreme gradient boosting (XGBoost), random forest (RF), light gradient boosting machine (LightGBM), and adaptive boosting (AdaBoost). AdaBoost demonstrated the highest accuracy in the development group, achieving an area under the ROC curve (AUC) of 0.803 in the validation group, indicating strong predictive capability.
Conclusions:
By leveraging key frailty determinants in elderly patients with CHD, the AdaBoost machine learning model developed in this study has shown robust predictive performance through validated indicators and offers a reliable tool for assessing frailty risk in this patient population.
