ArticleRenal failure2024
Prediction of successful weaning from renal replacement therapy in critically ill patients based on machine learning.
Article in Renal failure, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Novel biomarkers for predicting successful liberation of renal replacement therapy for acute kidney injury: a systematic review.Critical care (London, England) · 2025Pooled it
- Furosemide stress test predicts discontinuation of continuous renal replacement treatments in critically ill patients with acute kidney injury: a prospective multicenter trial.Renal failure · 2026Trial
- Machine learning prediction models for the popularization and dissemination of medical science popularization videos.Frontiers in public health · 2026Article
- Prediction model for successful liberation from continuous renal replacement therapy in patients with acute kidney injury: development and temporal validation.Frontiers in medicine · 2026Article
- Development and validation of an interpretable predictive machine learning model for successful weaning of continuous renal replacement therapy.Scientific reports · 2025Observational
- Expectations of Intensive Care Physicians Regarding an AI-Based Decision Support System for Weaning From Continuous Renal Replacement Therapy: Predevelopment Survey Study.JMIR medical informatics · 2025Article
- Enhanced prognostic prediction of cancer-specific mortality in elderly bladder cancer patients post-radical cystectomy: an XGBoost model study.Translational cancer research · 2025Article
- Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting the successful weaning of acute kidney injury (AKI) patients from renal replacement therapy (RRT) has emerged as a research focus, and we successfully built predictive models for RRT withdrawal in patients with severe AKI by machine learning.
methodsThis retrospective single-center study utilized data from our general intensive care unit (ICU) Database, focusing on patients diagnosed with severe AKI who underwent RRT. We evaluated RRT weaning success based on patients being free of RRT in the subsequent week and their overall survival. Multiple logistic regression (MLR) and machine learning algorithms were adopted to construct the prediction models.
resultsA total of 976 patients were included, with 349 patients successfully weaned off RRT. Longer RRT duration (7.0
conclusionHigh-risk factors for unsuccessful RRT weaning in severe AKI patients include prolonged RRT duration. Machine learning prediction models, when compared to models based on multivariate logistic regression using these indicators, offer distinct advantages in predictive accuracy.
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Registered trials
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.