Prognostic Machine Learning Models for First-Year Mortality in Incident Hemodialysis Patients: Development and Validation Study

BackgroundThe first-year survival rate among patients undergoing hemodialysis remains poor. Current mortality risk scores for patients undergoing hemodialysis employ regression techniques and have limited applicability and robustness. ObjectiveWe aimed to develop...

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Main Authors: Sheng, Kaixiang, Zhang, Ping, Yao, Xi, Li, Jiawei, He, Yongchun, Chen, Jianghua
Format: Article
Language:English
Published: JMIR Publications 2020-10-01
Series:JMIR Medical Informatics
Online Access:http://medinform.jmir.org/2020/10/e20578/
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spelling doaj-7d0d701776df4fda9972c83db7e02e892021-05-03T01:42:27ZengJMIR PublicationsJMIR Medical Informatics2291-96942020-10-01810e2057810.2196/20578Prognostic Machine Learning Models for First-Year Mortality in Incident Hemodialysis Patients: Development and Validation StudySheng, KaixiangZhang, PingYao, XiLi, JiaweiHe, YongchunChen, Jianghua BackgroundThe first-year survival rate among patients undergoing hemodialysis remains poor. Current mortality risk scores for patients undergoing hemodialysis employ regression techniques and have limited applicability and robustness. ObjectiveWe aimed to develop a machine learning model utilizing clinical factors to predict first-year mortality in patients undergoing hemodialysis that could assist physicians in classifying high-risk patients. MethodsTraining and testing cohorts consisted of 5351 patients from a single center and 5828 patients from 97 renal centers undergoing hemodialysis (incident only). The outcome was all-cause mortality during the first year of dialysis. Extreme gradient boosting was used for algorithm training and validation. Two models were established based on the data obtained at dialysis initiation (model 1) and data 0-3 months after dialysis initiation (model 2), and 10-fold cross-validation was applied to each model. The area under the curve (AUC), sensitivity (recall), specificity, precision, balanced accuracy, and F1 score were used to assess the predictive ability of the models. ResultsIn the training and testing cohorts, 585 (10.93%) and 764 (13.11%) patients, respectively, died during the first-year follow-up. Of 42 candidate features, the 15 most important features were selected. The performance of model 1 (AUC 0.83, 95% CI 0.78-0.84) was similar to that of model 2 (AUC 0.85, 95% CI 0.81-0.86). ConclusionsWe developed and validated 2 machine learning models to predict first-year mortality in patients undergoing hemodialysis. Both models could be used to stratify high-risk patients at the early stages of dialysis.http://medinform.jmir.org/2020/10/e20578/
collection DOAJ
language English
format Article
sources DOAJ
author Sheng, Kaixiang
Zhang, Ping
Yao, Xi
Li, Jiawei
He, Yongchun
Chen, Jianghua
spellingShingle Sheng, Kaixiang
Zhang, Ping
Yao, Xi
Li, Jiawei
He, Yongchun
Chen, Jianghua
Prognostic Machine Learning Models for First-Year Mortality in Incident Hemodialysis Patients: Development and Validation Study
JMIR Medical Informatics
author_facet Sheng, Kaixiang
Zhang, Ping
Yao, Xi
Li, Jiawei
He, Yongchun
Chen, Jianghua
author_sort Sheng, Kaixiang
title Prognostic Machine Learning Models for First-Year Mortality in Incident Hemodialysis Patients: Development and Validation Study
title_short Prognostic Machine Learning Models for First-Year Mortality in Incident Hemodialysis Patients: Development and Validation Study
title_full Prognostic Machine Learning Models for First-Year Mortality in Incident Hemodialysis Patients: Development and Validation Study
title_fullStr Prognostic Machine Learning Models for First-Year Mortality in Incident Hemodialysis Patients: Development and Validation Study
title_full_unstemmed Prognostic Machine Learning Models for First-Year Mortality in Incident Hemodialysis Patients: Development and Validation Study
title_sort prognostic machine learning models for first-year mortality in incident hemodialysis patients: development and validation study
publisher JMIR Publications
series JMIR Medical Informatics
issn 2291-9694
publishDate 2020-10-01
description BackgroundThe first-year survival rate among patients undergoing hemodialysis remains poor. Current mortality risk scores for patients undergoing hemodialysis employ regression techniques and have limited applicability and robustness. ObjectiveWe aimed to develop a machine learning model utilizing clinical factors to predict first-year mortality in patients undergoing hemodialysis that could assist physicians in classifying high-risk patients. MethodsTraining and testing cohorts consisted of 5351 patients from a single center and 5828 patients from 97 renal centers undergoing hemodialysis (incident only). The outcome was all-cause mortality during the first year of dialysis. Extreme gradient boosting was used for algorithm training and validation. Two models were established based on the data obtained at dialysis initiation (model 1) and data 0-3 months after dialysis initiation (model 2), and 10-fold cross-validation was applied to each model. The area under the curve (AUC), sensitivity (recall), specificity, precision, balanced accuracy, and F1 score were used to assess the predictive ability of the models. ResultsIn the training and testing cohorts, 585 (10.93%) and 764 (13.11%) patients, respectively, died during the first-year follow-up. Of 42 candidate features, the 15 most important features were selected. The performance of model 1 (AUC 0.83, 95% CI 0.78-0.84) was similar to that of model 2 (AUC 0.85, 95% CI 0.81-0.86). ConclusionsWe developed and validated 2 machine learning models to predict first-year mortality in patients undergoing hemodialysis. Both models could be used to stratify high-risk patients at the early stages of dialysis.
url http://medinform.jmir.org/2020/10/e20578/
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