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

Abstract

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.

Keywords:

• Peritoneal dialysis
• machine learning
• all-cause mortality
• heart failure
• complications

Author contributions

Liping Xu: data curation, data analysis, and drafting of the article; Fang Cao: searched for literature and collaborative data entry; Lian Wang: data entry; Weihua Liu: data analysis; Meizhu Gao: data analysis; Li Zhang: searched for literature; Fuyuan Hong: data curation; Miao Lin: study supervision, coordination, funding support, and design of this study. All authors agreed to the publication of this work.

Ethics statement

Our study was approved by Clinical Ethics Review Committee in Fujian Provincial Hospital as an exempt study with a waiver of informed consent, allowing a retrospective review of medical records. This study protocol was reviewed and approved by Clinical Ethics Review Committee in Fujian Provincial Hospital, approval number K2021-03-026.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data are not publicly available due to ethical reasons. Further enquiries can be directed to the corresponding author.

Additional information

Funding

This study was supported by grants from Provincial Natural Science Foundation of Fujian Province (No. 2019J01172), Young and Middle-Aged Scholars Program of Fujian Health Commission (No. 2019-ZQN-7) and a Special Grant for Education and Research from Fujian Department of Finance (No. (2022)840). This study was supported by grants from Xiamen Medical and Health Guiding Project (No. 3502Z20224ZD1257).