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ABSTRACT BACKGROUND. Continuous renal replacement therapy (CRRT) is a primary form of renal support for patients with acute kidney injury in an intensive care unit. Making an accurate decision of discontinuation is crucial for the prognosis of patients. Previous research has mostly focused on the univariate and multivariate analysis of factors in CRRT, without the capacity to capture the complexity of the decision-making process. The present study thus developed a dynamic, interpretable decision model for CRRT discontinuation. METHODS. The study adopted a cohort of 1234 adult patients admitted to an intensive care unit in the MIMIC-IV database. We used the extreme gradient boosting (XGBoost) machine learning algorithm to construct dynamic discontinuation decision models across four time points. Shapley additive explanation (SHAP) analysis was conducted to show the contribution of an individual feature to the model output. RESULTS. Of the 1234 included patients with CRRT, 596 (48.3%) successfully discontinued CRRT. The dynamic prediction by the XGBoost model produced an area under the curve of 0.848 and accuracy, sensitivity, and specificity of 0.782, 0.786, and 0.776, respectively. The XGBoost model was thus far superior to other test models. SHAP demonstrated that the features that contributed most to the model results were the sequential organ failure assessment score, serum lactate level, and 24-hour urine output. CONCLUSIONS. Dynamic decision models supported by machine learning are capable of dealing with complex factors in CRRT and effectively predicting the outcome of discontinuation. KEYWORDS: acute kidney injury, continuous renal replacement therapy, discontinuation, machine learning

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