Machine Learning-Aided Decision-Making Model for the Discontinuation of Continuous Renal Replacement Therapy.

Abstract

Introduction: 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.

Method: The study adopted a cohort of 1,234 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 4 time points. SHapley Additive exPlanation (SHAP) analysis was conducted to exhibit the contributions of individual features to the model output.

Result: Of the 1,234 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, with accuracy, sensitivity, and specificity of 0.782, 0.786, and 0.776, respectively. The performance of the XGBoost model was 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-h urine output.

Conclusion: Dynamic decision models supported by machine learning are capable of dealing with complex factors in CRRT and effectively predicting the outcome of discontinuation.