Prediction of Mortality and Major Adverse Kidney Events in Critically Ill Patients With Acute Kidney Injury.

RATIONALE & OBJECTIVE Risk-prediction tools for assisting acute kidney injury (AKI) management have focused on AKI onset but have infrequently addressed kidney recovery. We developed clinical models for risk-stratification of mortality and major adverse kidney events in critically ill patients with incident AKI. STUDY DESIGN Multicenter cohort study. SETTING & PARTICIPANTS 9,587 adult patients admitted to heterogenous ICUs (March 2009 to February 2017) who developed AKI within the first 3 days of their ICU stays. PREDICTORS Multimodal clinical data consisting of 71 features collected in first 3 days of ICU stay. OUTCOMES 1) Hospital mortality and 2) major adverse kidney events (MAKE), defined as the composite of death, dependence on renal replacement therapy and a drop in eGFR ≥50% from baseline up to 120 days from hospital discharge. ANALYTICAL APPROACH Four machine learning algorithms (logistic regression, random forest, support vector machine, and extreme gradient boosting) and the SHapley Additive exPlanations (SHAP) framework were used for feature selection and interpretation. Model performance was evaluated by 10-fold cross validation and external validation. RESULTS One developed model including 15 features outperformed the SOFA score for the prediction of hospital mortality: AUC (95%CI) 0.79 (0.79-0.80) vs. 0.71 (0.71-0.71) in the development cohort and 0.74 (0.73-0.74) vs. 0.71 (0.71-0.71) in the validation cohort, p<0.001 for both. A second developed model including 14 features outperformed KDIGO AKI severity staging for the prediction of MAKE: 0.78 (0.78-0.78) vs. 0.66 (0.66-0.66) in the development cohort and 0.73 (0.72-0.74) vs. 0.67 (0.67-0.67) in the validation cohort, p<0.001 for both. LIMITATIONS The models are only applicable to critically ill adult patients with incident AKI within the first 3 days of an ICU stay. CONCLUSIONS The reported clinical models exhibited better performance for mortality and kidney recovery prediction compared to standard scoring tools commonly used in critically ill patients with AKI in the ICU. Additional validation is needed to support the utility and implementation of these models.