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Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort

Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinica...

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Main Authors: Magunia, Harry (Author) , Lederer, Simone (Author) , Verbuecheln, Raphael (Author) , Gilot, Bryant (Author) , Koeppen, Michael (Author) , Häberle, Helene (Author) , Mirakaj, Valbona (Author) , Hofmann, Pascal (Author) , Marx, Gernot (Author) , Bickenbach, Johannes (Author) , Nohe, Boris Alexander (Author) , Lay, Michael (Author) , Spies, Claudia D. (Author) , Edel, Andreas (Author) , Schiefenhövel, Fridtjof (Author) , Rahmel, Tim (Author) , Putensen, Christian (Author) , Sellmann, Timur (Author) , Koch, Thea (Author) , Brandenburger, Timo (Author) , Kindgen-Milles, Detlef (Author) , Brenner, Thorsten (Author) , Berger, Marc (Author) , Zacharowski, Kai (Author) , Adam, Elisabeth (Author) , Posch, Matthias Jakob (Author) , Mörer, Onnen (Author) , Scheer, Christian S. (Author) , Sedding, Daniel (Author) , Weigand, Markus A. (Author) , Fichtner, Falk (Author) , Nau, Carla (Author) , Prätsch, Florian (Author) , Wiesmann, Thomas (Author) , Koch, Christian (Author) , Schneider, Gerhard (Author) , Lahmer, Tobias (Author) , Straub, Andreas (Author) , Meiser, Andreas (Author) , Weiss, Manfred (Author) , Jungwirth, Bettina (Author) , Wappler, Frank (Author) , Meybohm, Patrick (Author) , Herrmann, Johannes Bernd (Author) , Malek, Nisar Peter (Author) , Kohlbacher, Oliver (Author) , Biergans, Stephanie (Author) , Rosenberger, Peter (Author)
Format: Article (Journal)
Language:English
Published: AUG 17 2021
In: Critical care
Year: 2021, Volume: 25, Pages: 1-14
ISSN:1466-609X
DOI:10.1186/s13054-021-03720-4
Online Access:Resolving-System, kostenfrei: https://doi.org/10.1186/s13054-021-03720-4
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Author Notes:Harry Magunia, Simone Lederer, Raphael Verbuecheln, Bryant Joseph Gilot, Michael Koeppen, Helene A. Haeberle, Valbona Mirakaj, Pascal Hofmann, Gernot Marx, Johannes Bickenbach, Boris Nohe, Michael Lay, Claudia Spies, Andreas Edel, Fridtjof Schiefenhövel, Tim Rahmel, Christian Putensen, Timur Sellmann, Thea Koch, Timo Brandenburger, Detlef Kindgen-Milles, Thorsten Brenner, Marc Berger, Kai Zacharowski, Elisabeth Adam, Matthias Posch, Onnen Moerer, Christian S. Scheer, Daniel Sedding, Markus A. Weigand, Falk Fichtner, Carla Nau, Florian Prätsch, Thomas Wiesmann, Christian Koch, Gerhard Schneider, Tobias Lahmer, Andreas Straub, Andreas Meiser, Manfred Weiss, Bettina Jungwirth, Frank Wappler, Patrick Meybohm, Johannes Herrmann, Nisar Malek, Oliver Kohlbacher, Stephanie Biergans and Peter Rosenberger
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Summary:Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes.
Item Description:Gesehen am 17.11.2021
Physical Description:Online Resource
ISSN:1466-609X
DOI:10.1186/s13054-021-03720-4

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