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

Emma Schwager, Stephanie Lanius, Erina Ghosh, Larry Eshelman, Kalyan S. Pasupathy, Erin F. Barreto, Kianoush Kashani

Research output: Contribution to journalArticlepeer-review


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 (AKICr) and might underperform when predicting urine-output-triggered AKI (AKIUO). We aimed to describe how admission AKICr prediction models perform in all AKI patients. Materials and methods: Three types of models were trained: 1) pAKIany, predicting AKI based on creatinine or urine output, 2) pAKIUO, predicting AKI based only on urine output, and 3) pAKICr, predicting AKI based only on creatinine. We compared model performance and predictive features. Results: The pAKIany 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 pAKICr models had fair performance in predicting AKICr (AUROCs 0.702–0.748) but poor performance predicting AKIUO (AUROCs 0.581–0.695). The predictive features for the pAKICr models and pAKIUO models were distinct, while top features for the pAKIany models were consistently a combination of those for the pAKICr and pAKIUO models. Conclusion: Ignoring urine output in the outcome during model training resulted in models that are unlikely to predict AKIUO adequately and may miss a substantial proportion of patients in practice.

Original languageEnglish (US)
Pages (from-to)283-288
Number of pages6
JournalJournal of Critical Care
StatePublished - Apr 2021


  • Acute kidney injury
  • Clinical decision support system

ASJC Scopus subject areas

  • Critical Care and Intensive Care Medicine


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