Including urinary output to define AKI enhances the performance of machine learning models to predict AKI at admission

Purpose: Acute kidney injury (AKI) is a prevalent and detrimental condition in intensive care unit patients. Most AKI predictive models only predict creatinine-triggered AKI (AKI_Cr) and might underperform when predicting urine-output-triggered AKI (AKI_UO). We aimed to describe how admission AKI_Cr prediction models perform in all AKI patients. Materials and methods: Three types of models were trained: 1) pAKI_any, predicting AKI based on creatinine or urine output, 2) pAKI_UO, predicting AKI based only on urine output, and 3) pAKI_Cr, predicting AKI based only on creatinine. We compared model performance and predictive features. Results: The pAKI_any models had the best overall performance (AUROC 0.673–0.716) and the most consistent performance across three patient cohorts grouped by type of AKI trigger (min AUROC of 0.636). The pAKI_Cr models had fair performance in predicting AKI_Cr (AUROCs 0.702–0.748) but poor performance predicting AKI_UO (AUROCs 0.581–0.695). The predictive features for the pAKI_Cr models and pAKI_UO models were distinct, while top features for the pAKI_any models were consistently a combination of those for the pAKI_Cr and pAKI_UO models. Conclusion: Ignoring urine output in the outcome during model training resulted in models that are unlikely to predict AKI_UO adequately and may miss a substantial proportion of patients in practice.