Machine Learning-Based Prediction Model for ICU Mortality After Continuous Renal Replacement Therapy Initiation in Children

Abstract

Background: Continuous renal replacement therapy (CRRT) is the favored renal replacement therapy in critically ill patients. Predicting clinical outcomes for CRRT patients is difficult due to population heterogeneity, varying clinical practices, and limited sample sizes.

Objective: We aimed to predict survival to ICUs and hospital discharge in children and young adults receiving CRRT using machine learning (ML) techniques.

Derivation cohort: Patients less than 25 years of age receiving CRRT for acute kidney injury and/or volume overload from 2015 to 2021 (80%).

Validation cohort: Internal validation occurred in a testing group of patients from the dataset (20%).

Prediction model: Retrospective international multicenter study utilizing an 80/20 training and testing cohort split, and logistic regression with L2 regularization (LR), decision tree, random forest (RF), gradient boosting machine, and support vector machine with linear kernel to predict ICU and hospital survival. Model performance was determined by the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) due to the imbalance in the dataset.

Results: Of the 933 patients included in this study, 538 (54%) were male with a median age of 8.97 years and interquartile range (1.81-15.0 yr). The ICU mortality was 35% and hospital mortality was 37%. The RF had the best performance for predicting ICU mortality (AUROC, 0.791 and AUPRC, 0.878) and LR for hospital mortality (AUROC, 0.777 and AUPRC, 0.859). The top two predictors of ICU survival were Pediatric Logistic Organ Dysfunction-2 score at CRRT initiation and admission diagnosis of respiratory failure.

Conclusions: These are the first ML models to predict survival at ICU and hospital discharge in children and young adults receiving CRRT. RF outperformed other models for predicting ICU mortality. Future studies should expand the input variables, conduct a more sophisticated feature selection, and use deep learning algorithms to generate more precise models.

Figure 1.

Receiver operating characteristic (ROC) curve and precision-recall curve for ICU survival. AUC = area under the curve, CRRT = continuous renal replacement therapy, SVM = support vector machine.

Figure 2.

SHapley Additive exPlanations (SHAP) plot showing the 20 most important variables and the contribution of each variable to our model’s prediction if ICU survival. BSA = body surface area, CRRT = continuous renal replacement therapy, PELOD-2 = Pediatric Logistic Organ Dysfunction-2, VIS = Vasoactive-Inotropic Score.

Figure 3.

Receiver operating characteristic (ROC) curve and precision-recall curve for hospital survival. AUC = area under the curve, CRRT = continuous renal replacement therapy, SVM = support vector machine.

Figure 4.

SHapley Additive exPlanations (SHAP) plot showing the 20 most important variables and the contribution of each variable to our model’s prediction of hospital survival. CRRT = continuous renal replacement therapy, PELOD-2 = Pediatric Logistic Organ Dysfunction-2, VIS = Vasoactive-Inotropic Score.