Machine learning model and nomogram to predict the risk of heart failure hospitalization in peritoneal dialysis patients

Introduction: The study presented here aimed to establish a predictive model for heart failure (HF) and all-cause mortality in peritoneal dialysis (PD) patients with machine learning (ML) algorithm.

Methods: We retrospectively included 1006 patients who initiated PD from 2010 to 2016. XGBoost, random forest (RF), and AdaBoost were used to train models for assessing risk for 1-year and 5-year HF hospitalization and mortality. The performance was validated using fivefold cross-validation. The optimal ML algorithm was used to construct the models to predictive the risk of the HF and all-cause mortality. The prediction performance of ML methods and Cox regression was compared.

Results: Over a median follow-up of 49 months. Two hundred and ninety-eight patients developed HF required hospitalization; 199 patients died during the follow-up. The RF model (AUC = 0.853) was the best performing model for predicting HF, and the XGBoost model (AUC = 0.871) was the best model for predicting mortality. Baseline moderate or severe renal disease, systolic blood pressure (SBP), body mass index (BMI), age, Charlson Comorbidity Index (CCI) score were strongly associated with HF hospitalization, whereas age, CCI score, creatinine, age, high-density lipoprotein cholesterol (HDL-C), total cholesterol, baseline estimated glomerular filtration rate (eGFR) were the most significant predictors of mortality. For all the above endpoints, the ML models demonstrated better discrimination than Cox regression.

Conclusions: We developed and validated a novel method to predict the risk factors of HF and all-cause mortality that integrates readily available clinical, laboratory, and electrocardiographic variables to predict the risk of HF among PD patients.