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

Abstract

Abstract Background For 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. Methods We 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). Results A 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. Conclusions Our 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.

Keywords

• Acute kidney injury
• Continuous renal replacement therapy
• Machine learning
• Prediction algorithm