ArticleBMC nephrology2024
Factors and machine learning models for predicting successful discontinuation of continuous renal replacement therapy in critically ill patients with acute kidney injury: a retrospective cohort study based on MIMIC-IV database.
Article in BMC nephrology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Article
- Prediction model for successful liberation from continuous renal replacement therapy in patients with acute kidney injury: development and temporal validation.Frontiers in medicine · 2026Article
- Factors associated with successful transition from continuous renal replacement therapy in critically ill patients: a retrospective cohort study.Renal failure · 2025Article
- Diagnostic Codes in AI Prediction Models and Label Leakage of Same-Admission Clinical Outcomes.JAMA network open · 2025Article
- Development and validation of an interpretable predictive machine learning model for successful weaning of continuous renal replacement therapy.Scientific reports · 2025Observational
- Liberation from continuous renal replacement therapy due to renal recovery in adults and children: a literature review and Delphi consensus on clinical practice.Critical care (London, England) · 2025Review
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Authors and funding
12 authors.
Funding
Abstract
backgroundFor critically ill patients with acute kidney injury (AKI), there remains controversy regarding the predictive factors affecting the discontinuation of continuous renal replacement therapy (CRRT). This study aims to explore factors associated with successful CRRT discontinuation in AKI patients and to develop predictive models for successful discontinuation.
methodsWe conducted a retrospective study on adult patients with AKI who received CRRT, sourced from the Medical Information Mart for Intensive Care (MIMIC-IV) database. Successful discontinuation of CRRT was defined as no CRRT requirement within 72 h after stopping CRRT. Predictive factors for successful discontinuation of CRRT were analyzed. Additionally, we utilized machine learning algorithms to develop predictive models, including logistic regression (LR), decision tree (DT), random forest (RF), XGBoost, and K-nearest neighbor (KNN).
resultsA total of 599 patients were included, of whom 475 (79.3%) successfully discontinued CRRT. Urine output, non-renal SOFA score, bicarbonate, systolic blood pressure, and blood urea nitrogen were identified as risk factors for successful CRRT discontinuation. The KNN model exhibited the highest area under the receiver operating characteristic curve (AUC) (0.870), followed by LR (0.739), DT (0.691), RF (0.847), and XGBoost (0.830). When incorporating all available variables, the AUCs for the LR, DT, RF, XGBoost, and KNN models were 0.708, 0.674, 0.875, 0.866, and 0.816, respectively. Considering the performance of the models in both scenarios, the ensemble learning models (RF and XGBoost) were demonstrated superior performance.
conclusionsOur results identified factors associated with successful discontinuation of CRRT in AKI patients. Additionally, we developed promising machine learning models which provided a reference for future research.
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