Prediction of Acute Kidney Injury in the Intensive Care Unit: Preliminary Findings in a European Open Access Database

Michael Fujarski, Christian Porschen, Lucas Plagwitz, Alexander Brenner, Narges Ghoreishi, Patrick Thoral, Harm-Jan de Grooth, Paul Elbers, Raphael Weiss, Melanie Meersch, Alexander Zarbock, Thilo Caspar von Groote, Julian Varghese

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)


Acute kidney injury (AKI) is a common complication in critically ill patients and is associated with long-term complications and an increased mortality. This work presents preliminary findings from the first freely available European intensive care database released by Amsterdam UMC. A machine learning (ML) model was developed to predict AKI in the intensive care unit 12 hours before the actual event. Main features of the model included medications and hemodynamic parameters. Our models perform with an accuracy of 81.8% on moderate to severe AKI and 79.8% on all AKI patients. Those results can compete with models reported in the literature and introduce an ML model for AKI based on European patient data.

Original languageEnglish
Pages (from-to)139-140
Number of pages2
JournalStudies in health technology and informatics
Publication statusPublished - 25 May 2022


  • Acute Kidney Injury
  • AmsterdamUMCdb
  • ICU
  • Predictive Modeling

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