ArticleKidney3602022
A Machine Learning Model for Predicting Mortality within 90 Days of Dialysis Initiation.
Article in Kidney360, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mortality Risk Prediction Models for People With Kidney Failure: A Systematic Review.JAMA network open · 2025Pooled it
- Discrete Time Neural Network Models to Address Time-Varying Predictor Importance: An Illustration in Predicting Mortality Over Different Time Horizons.IEEE journal of biomedical and health informatics · 2026Article
- Machine learning identifies clusters of multimorbidity among decedents with inflammatory bowel disease.Communications medicine · 2025Article
- Machine learning models for predicting short-term progression in patients with stage 4 chronic kidney disease: a multi-center validation study.Scientific reports · 2025Observational
- Interpretable machine learning model for cardiovascular disease risk prediction: a feature decomposition-based study.BMC public health · 2025Article
- A machine-learning method for predicting the 1-year risk of death in maintenance hemodialysis patients based on continuous compliance with dialysis quality indicators.BMC nephrology · 2025Article
- Predicting mortality in elderly chronic kidney disease patients using algorithmic risk assessment: a Southeast Asian perspective.BMC nephrology · 2025Article
- The role of socio-cultural-demographics in long-term haemodialysis survival.Bioinformation · 2025Article
- Predicting early mortality in hemodialysis patients: a deep learning approach using a nationwide prospective cohort in South Korea.Scientific reports · 2024Article
- Development of a diagnostic model for biliary atresia based on MMP7 and serological tests using machine learning.Pediatric surgery international · 2024Article
- Designing an Implementable Clinical Prediction Model for Near-Term Mortality and Long-Term Survival in Patients on Maintenance Hemodialysis.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2024Article
- External validation of the 2-year mortality prediction tool in hemodialysis patients developed using a Bayesian network.Clinical kidney journal · 2024Article
- An Approach for Personalized Dynamic Assessment of Chronic Kidney Disease Progression Using Joint Model.Biomedicines · 2024Article
- Machine learning models to predict end-stage kidney disease in chronic kidney disease stage 4.BMC nephrology · 2023Article
- Using machine learning models to predict the surgical risk of children with pancreaticobiliary maljunction and biliary dilatation.Surgery today · 2023Article
- Systemic Immune Inflammation Index as a Key Predictor of Dialysis in Pediatric Chronic Kidney Disease with the Use of Random Forest Classifier.Journal of clinical medicine · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The first 90 days after dialysis initiation are associated with high morbidity and mortality in end-stage kidney disease (ESKD) patients. A machine learning-based tool for predicting mortality could inform patient-clinician shared decision making on whether to initiate dialysis or pursue medical management. We used the eXtreme Gradient Boosting (XGBoost) algorithm to predict mortality in the first 90 days after dialysis initiation in a nationally representative population from the United States Renal Data System. Methods: A cohort of adults initiating dialysis between 2008-2017 were studied for outcome of death within 90 days of dialysis initiation. The study dataset included 188 candidate predictors prognostic of early mortality that were known on or before the first day of dialysis and was partitioned into training (70%) and testing (30%) subsets. XGBoost modeling used a complete-case set and a dataset obtained from multiple imputation. Model performance was evaluated by c-statistics overall and stratified by subgroups of age, sex, race, and dialysis modality. Results: The analysis included 1,150,195 patients with ESKD, of whom 86,083 (8%) died in the first 90 days after dialysis initiation. The XGBoost models discriminated mortality risk in the nonimputed ( Conclusions: The XGBoost-based model developed in this study discriminated risk of early mortality after dialysis initiation with excellent calibration and performed well across key subgroups.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.