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Risk prediction of cardiovascular events in peritoneal dialysis patients
Liang Liu1, Liu Zhang2, Daohai Zhang1
1Department of Nephrology, Chongqing Key Laboratory of Prevention and Treatment of Kidney Disease, Chongqing Clinical Research Center of Kidney and Urology Diseases, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, 400037, China.
Insights
Cardiovascular events are a major concern for peritoneal dialysis patients. A machine learning model effectively predicts these events using 12 key variables, aiding early intervention.
Area of Science:
- Nephrology and Cardiovascular Medicine
- Artificial Intelligence in Healthcare
- Predictive Analytics in Clinical Settings
Background:
- Cardiovascular events (CVEs) are the leading cause of death in peritoneal dialysis (PD) patients, accounting for nearly 40% of mortality.
- Early identification of patients at high risk for CVEs is crucial for reducing mortality and improving patient outcomes.
- Machine learning offers a powerful approach to identify complex patterns in clinical data for accurate CVE prediction in PD patients.
Purpose of the Study:
- To develop and validate a machine learning model for predicting cardiovascular events (CVEs) in patients undergoing peritoneal dialysis (PD).
- To identify key clinical variables that are most predictive of CVEs in the PD population.
- To create a practical tool for healthcare providers to facilitate proactive interventions for high-risk PD patients.
Main Methods:
- A cohort of 251 PD patients was analyzed, with an additional 42 for external validation.
- Lasso regression reduced 37 initial variables to 25. Six machine learning algorithms were evaluated, with XGBoost selected as the optimal model.
- The final XGBoost model utilized 12 selected variables and was validated both internally and externally. A web application was developed.
Main Results:
- The XGBoost model achieved an AUC of 0.94 during 5-fold cross-validation.
- A simplified 12-variable model showed strong predictive performance (AUC 0.88 internally, 0.78 externally).
- Key predictors included age at catheterization, height, HDL, gender, and hemoglobin. Older age, shorter height, male gender, higher HDL, and lower hemoglobin were associated with increased CVE risk.
Conclusions:
- A 12-variable machine learning model effectively predicts cardiovascular events in peritoneal dialysis patients.
- The developed model enables early identification of high-risk individuals, facilitating timely interventions.
- The predictive model has been successfully integrated into a user-friendly web application for clinical use.
Background:
Cardiovascular events (CVEs), which refer to a spectrum of conditions including heart attacks, stroke and peripheral vascular disease, are the primary cause of death among peritoneal dialysis (PD) patients, accounting for nearly 40% of deaths. Early identification of high-risk individuals is essential to lessen this burden. Machine learning is particularly suited for this task due to its ability to discern complex, non-linear relationships between various clinical variables, which is essential for accurately predicting CVEs in the context of PD. Our study aimed to develop a predictive machine learning model to identify PD patients at risk of CVEs, offering healthcare providers a tool for proactive intervention.
Methods:
A total of 251 PD patients were enrolled in the study, with an additional 42 patients included for external validation. Initially, 37 variables were collected but reduced to 25 via Lasso regression. Six supervised machine learning algorithms were evaluated, and XGBoost was chosen as the optimal model based on AUC. Both internal and external validation confirmed the model's efficacy, and a web application was developed using the final XGBoost model, which utilized 12 selected variables.
Results:
Among the 251 patients, 40 (15.94%) developed CVEs. The XGBoost model demonstrated an AUC of 0.94 in 5-fold cross-validation. A simplified XGBoost model using 12 variables demonstrated robust prediction capabilities with an AUC of 0.88 in 5-fold cross-validation and 0.78 in external validation. The top five predictors of CVEs were age at catheterization, height, HDL, gender and hemoglobin. According to the SHAP summary plot, older age at catheterization, shorter height, male gender, higher serum HDL and lower hemoglobin levels correlated with increased CVEs risk in PD patients.
Conclusions:
The machine learning model, based on 12 key variables, offers an effective tool for predicting CVEs in PD patients, enabling early identification of high-risk cases. This model has been integrated into a web application.
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