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


Journal

Studies in health technology and informatics
ISSN: 1879-8365
Titre abrégé: Stud Health Technol Inform
Pays: Netherlands
ID NLM: 9214582

Informations de publication

Date de publication:
25 May 2022
Historique:
entrez: 25 5 2022
pubmed: 26 5 2022
medline: 27 5 2022
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 35612039
pii: SHTI220419
doi: 10.3233/SHTI220419
doi:

Types de publication

Journal Article

Langues

eng

Pagination

139-140

Auteurs

Michael Fujarski (M)

Institute of Medical Informatics, University of Münster, Germany.

Christian Porschen (C)

Department of Anesthesiology, Intensive Care and Pain Medicine, University Hospital Münster, Münster, Germany.

Lucas Plagwitz (L)

Institute of Medical Informatics, University of Münster, Germany.

Alexander Brenner (A)

Institute of Medical Informatics, University of Münster, Germany.

Narges Ghoreishi (N)

Federal Institute for Risk Assessment, Berlin, Germany.

Patrick Thoral (P)

Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence. Vrije Universiteit, Amsterdam, The Netherlands.

Harm-Jan de Grooth (HJ)

Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence. Vrije Universiteit, Amsterdam, The Netherlands.

Paul Elbers (P)

Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence. Vrije Universiteit, Amsterdam, The Netherlands.

Raphael Weiss (R)

Department of Anesthesiology, Intensive Care and Pain Medicine, University Hospital Münster, Münster, Germany.

Melanie Meersch (M)

Department of Anesthesiology, Intensive Care and Pain Medicine, University Hospital Münster, Münster, Germany.

Alexander Zarbock (A)

Department of Anesthesiology, Intensive Care and Pain Medicine, University Hospital Münster, Münster, Germany.

Thilo Caspar von Groote (TC)

Department of Anesthesiology, Intensive Care and Pain Medicine, University Hospital Münster, Münster, Germany.

Julian Varghese (J)

Institute of Medical Informatics, University of Münster, Germany.

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