ArticleTherapeutic apheresis and dialysis : official peer-reviewed journal of the International Society for Apheresis, the Japanese Society for Apheresis, the Japanese Society for Dialysis Therapy2025
Interpretable machine learning models for the prediction of all-cause mortality and time to death in hemodialysis patients.
Article in Therapeutic apheresis and dialysis : official peer-reviewed journal of the International Society for Apheresis, the Japanese Society for Apheresis, the Japanese Society for Dialysis Therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Time-updated explainable machine learning predicts short-term mortality in peritoneal dialysis patients.Renal failure · 2026Article
- A time-updated scoring system derived from a nomogram to predict 3-month mortality in maintenance hemodialysis patients.Frontiers in aging · 2026Article
- Interpretable machine learning models for the prediction of all-cause mortality and time to death in hemodialysis patients.Therapeutic apheresis and dialysis : official peer-reviewed journal of the International Society for Apheresis, the Japanese Society for Apheresis, the Japanese Society for Dialysis Therapy · 2025Article
- Risk prediction for cardiovascular events and all-cause mortality in maintenance hemodialysis patients.Frontiers in medicine · 2025Article
- The Relationship of Certain Diseases and Dietary Inflammatory Index in Older Adults: A Narrative Review.Current nutrition reports · 2024Review
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Authors and funding
8 authors.
Funding
Abstract
introductionThe elevated mortality and hospitalization rates among hemodialysis (HD) patients underscore the necessity for the development of accurate predictive tools. This study developed two models for predicting all-cause mortality and time to death-one using a comprehensive database and another simpler model based on demographic and clinical data without laboratory tests.
methodA retrospective cohort study was conducted from January 2017 to June 2023. Two models were created: Model A with 85 variables and Model B with 22 variables. We assessed the models using random forest (RF), support vector machine, and logistic regression, comparing their performance via the AU-ROC. The RF regression model was used to predict time to death. To identify the most relevant factors for prediction, the Shapley value method was used.
resultsAmong 359 HD patients, the RF model provided the most reliable prediction. The optimized Model A showed an AU-ROC of 0.86 ± 0.07, a sensitivity of 0.86, and a specificity of 0.75 for predicting all-cause mortality. It also had an R
conclusionTwo new interpretable clinical tools have been proposed to predict all-cause mortality and time to death in HD patients using machine learning models. The minimal and readily accessible data on which Model B is based makes it a valuable tool for integrating into clinical decision-making processes.
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