Research Paper Volume 13, Issue 10 pp 14170—14184

Prediction of premature all-cause mortality in patients receiving peritoneal dialysis using modified artificial neural networks

Qiongxiu Zhou1, *, , Xiaohan You2, *, , Haiyan Dong3, , Zhe Lin1, , Yanling Shi1, , Zhen Su1, , Rongrong Shao1, , Chaosheng Chen1, , Ji Zhang1, ,

  • 1 Department of Nephrology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, P.R. China
  • 2 Department of Nephrology, The First Affiliated Hospital of Soochow University, Jiangsu, P.R. China
  • 3 Department of Nephrology, Longgang Renmin Hospital, Wenzhou, Zhejiang, P.R. China
* Equal contribution

Received: January 6, 2021       Accepted: March 27, 2021       Published: May 13, 2021
How to Cite

Copyright: © 2021 Zhou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Premature all-cause mortality is high in patients receiving peritoneal dialysis (PD). The accurate and early prediction of mortality is critical and difficult. Three prediction models, the logistic regression (LR) model, artificial neural network (ANN) classic model and a new structured ANN model (ANN mixed model), were constructed and evaluated using a receiver operating characteristic (ROC) curve analysis. The permutation feature importance was used to interpret the important features in the ANN models. Eight hundred fifty-nine patients were enrolled in the study. The LR model performed slightly better than the other two ANN models on the test dataset; however, in the total dataset, the ANN models fit much better. The ANN mixed model showed the best prediction performance, with area under the ROC curves (AUROCs) of 0.8 and 0.79 for the 6-month and 12-month datasets. Our study showed that age, diastolic blood pressure (DBP), and low-density lipoprotein cholesterol (LDL-c) levels were common risk factors for premature mortality in patients receiving PD. Our ANN mixed model had incomparable advantages in fitting the overall data characteristics, and age is a steady risk factor for premature mortality in patients undergoing PD. Otherwise, DBP and LDL-c levels should receive more attention for all-cause mortality during follow-up.


PD: peritoneal dialysis; LR: logistic regression; ANN: artificial neural network; ROC: receiver operating characteristic; AUROC: area under the receiver operating characteristic curve; DBP: diastolic blood pressure; LDL-c: low-density lipoprotein cholesterol; ESRD: end-stage renal disease; CAPD: continuous ambulatory peritoneal dialysis; CHD: chronic heart disease; DM: diabetes mellitus; SBP: systolic blood pressure; Tg: total triglycerides; Tc: total cholesterol; HDL-c: high-density lipoprotein cholesterol; BUN: blood urea nitrogen; Scr: serum creatinine; iPTH: intact parathyroid hormone; TPR: true positive rate; FPR: false positive rate; SD: standard deviation; IQR: interquartile range.