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

doi:https://doi.org/10.3233/SHTI220419

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 journal › Article › Academic › peer-review

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Abstract

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 language	English
Pages (from-to)	139-140
Number of pages	2
Journal	Studies in health technology and informatics
Volume	294
DOIs	https://doi.org/10.3233/SHTI220419
Publication status	Published - 25 May 2022

Keywords

Acute Kidney Injury
AmsterdamUMCdb
ICU
Predictive Modeling

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Fujarski, M., Porschen, C., Plagwitz, L., Brenner, A., Ghoreishi, N., Thoral, P., de Grooth, H.-J., Elbers, P., Weiss, R., Meersch, M., Zarbock, A., von Groote, T. C., & Varghese, J. (2022). Prediction of Acute Kidney Injury in the Intensive Care Unit: Preliminary Findings in a European Open Access Database. Studies in health technology and informatics, 294, 139-140. https://doi.org/10.3233/SHTI220419

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abstract = "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.",

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note = "Publisher Copyright: {\textcopyright} 2022 European Federation for Medical Informatics (EFMI) and IOS Press.",

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doi = "https://doi.org/10.3233/SHTI220419",

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Fujarski, M, Porschen, C, Plagwitz, L, Brenner, A, Ghoreishi, N, Thoral, P , de Grooth, H-J , Elbers, P, Weiss, R, Meersch, M, Zarbock, A, von Groote, TC & Varghese, J 2022, 'Prediction of Acute Kidney Injury in the Intensive Care Unit: Preliminary Findings in a European Open Access Database', Studies in health technology and informatics, vol. 294, pp. 139-140. https://doi.org/10.3233/SHTI220419

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T1 - Prediction of Acute Kidney Injury in the Intensive Care Unit

T2 - Preliminary Findings in a European Open Access Database

AU - Fujarski, Michael

AU - Porschen, Christian

AU - Plagwitz, Lucas

AU - Brenner, Alexander

AU - Ghoreishi, Narges

AU - Thoral, Patrick

AU - de Grooth, Harm-Jan

AU - Elbers, Paul

AU - Weiss, Raphael

AU - Meersch, Melanie

AU - Zarbock, Alexander

AU - von Groote, Thilo Caspar

AU - Varghese, Julian

PY - 2022/5/25

Y1 - 2022/5/25

N2 - 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.

AB - 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.

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KW - AmsterdamUMCdb

KW - ICU

KW - Predictive Modeling

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SN - 0926-9630

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EP - 140

JO - Studies in health technology and informatics

JF - Studies in health technology and informatics

ER -

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

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Keywords

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