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Prediction of the Mortality Risk in Peritoneal Dialysis Patients using Machine Learning Models: A Nation-wide Prospective Cohort in Korea.


ABSTRACT: Herein, we aim to assess mortality risk prediction in peritoneal dialysis patients using machine-learning algorithms for proper prognosis prediction. A total of 1,730 peritoneal dialysis patients in the CRC for ESRD prospective cohort from 2008 to 2014 were enrolled in this study. Classification algorithms were used for prediction of N-year mortality including neural network. The survival hazard ratio was presented by machine-learning algorithms using survival statistics and was compared to conventional algorithms. A survival-tree algorithm presented the most accurate prediction model and outperformed a conventional method such as Cox regression (concordance index 0.769 vs 0.745). Among various survival decision-tree models, the modified Charlson Comorbidity index (mCCI) was selected as the best predictor of mortality. If peritoneal dialysis patients with high mCCI (>4) were aged ?70.5 years old, the survival hazard ratio was predicted as 4.61 compared to the overall study population. Among the various algorithm using longitudinal data, the AUC value of logistic regression was augmented at 0.804. In addition, the deep neural network significantly improved performance to 0.841. We propose machine learning-based final model, mCCI and age were interrelated as notable risk factors for mortality in Korean peritoneal dialysis patients.

SUBMITTER: Noh J 

PROVIDER: S-EPMC7198502 | BioStudies | 2020-01-01

SECONDARY ACCESSION(S): 10.23876/j.krcp.19.011

REPOSITORIES: biostudies

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Analysis of mortality risk from Korean hemodialysis registry data 2017.

Jin Dong-Chan DC  

Kidney research and clinical practice 20190601 2


The End-stage Renal Disease Registry Committee of the Korean Society of Nephrology collects data on the dialysis therapy in Korea through an internet-based registry program and reports it annually. In this article, the method and clinical implications of the mortality hazard ratio analyses of various clinical parameters in the 2017 registry report have been described, with the inclusion of data on four additional parameters. The mortality risk based on clinical parameters was analyzed only for h  ...[more]

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