Hierarchical Clustering Analysis for Predicting 1-Year Mortality After Starting Hemodialysis.
end-stage renal disease
hemodialysis
hierarchical clustering
machine learning
renal replacement therapy
risk prediction
Journal
Kidney international reports
ISSN: 2468-0249
Titre abrégé: Kidney Int Rep
Pays: United States
ID NLM: 101684752
Informations de publication
Date de publication:
Aug 2020
Aug 2020
Historique:
received:
10
01
2020
revised:
05
05
2020
accepted:
11
05
2020
entrez:
11
8
2020
pubmed:
11
8
2020
medline:
11
8
2020
Statut:
epublish
Résumé
For patients with end-stage renal disease (ESRD), due to the heterogeneity of the population, appropriate risk assessment approaches and strategies for further follow-up remain scarce. We aimed to conduct a pilot study for better risk stratification, applying machine learning-based classification to patients with ESRD who newly started maintenance hemodialysis. We prospectively studied 101 patients with ESRD, who were new to maintenance hemodialysis therapy, between August 2016 and March 2018. Baseline values of variables such as blood and urine tests were obtained before the initiation of hemodialysis. Agglomerative hierarchical clustering was conducted with the collected continuous data. The resulting clusters were followed up for the primary outcome of 1-year mortality, as analyzed by the Kaplan-Meier survival curve with log-rank test and the Cox proportional hazard model. The participants were divided into 3 clusters (cluster 1, In this proof-of-concept study, hierarchical clustering discovered a subgroup with a higher 1-year mortality at the initiation of hemodialysis. Applying machine learning-derived classification to patients with ESRD may contribute to better risk stratification.
Identifiants
pubmed: 32775818
doi: 10.1016/j.ekir.2020.05.007
pii: S2468-0249(20)31268-7
pmc: PMC7403509
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1188-1195Informations de copyright
© 2020 International Society of Nephrology. Published by Elsevier Inc.
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