Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning

Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning

Abstract Background For mechanically ventilated critically ill COVID-19 patients, prone positioning has quickly become an important treatment strategy, however, prone positioning is labor intensive and comes with potential adverse effects. Therefore, identifying which critically ill intubated COVID-...

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Bibliographic Details
Main Authors: Tariq A. Dam, Luca F. Roggeveen, Fuda van Diggelen, Lucas M. Fleuren, Ameet R. Jagesar, Martijn Otten, Heder J. de Vries, Diederik Gommers, Olaf L. Cremer, Rob J. Bosman, Sander Rigter, Evert-Jan Wils, Tim Frenzel, Dave A. Dongelmans, Remko de Jong, Marco A. A. Peters, Marlijn J. A. Kamps, Dharmanand Ramnarain, Ralph Nowitzky, Fleur G. C. A. Nooteboom, Wouter de Ruijter, Louise C. Urlings-Strop, Ellen G. M. Smit, D. Jannet Mehagnoul-Schipper, Tom Dormans, Cornelis P. C. de Jager, Stefaan H. A. Hendriks, Sefanja Achterberg, Evelien Oostdijk, Auke C. Reidinga, Barbara Festen-Spanjer, Gert B. Brunnekreef, Alexander D. Cornet, Walter van den Tempel, Age D. Boelens, Peter Koetsier, Judith Lens, Harald J. Faber, A. Karakus, Robert Entjes, Paul de Jong, Thijs C. D. Rettig, Sesmu Arbous, Sebastiaan J. J. Vonk, Tomas Machado, Willem E. Herter, Harm-Jan de Grooth, Patrick J. Thoral, Armand R. J. Girbes, Mark Hoogendoorn, Paul W. G. Elbers, The Dutch ICU Data Sharing Against COVID-19 Collaborators
Format: Article
Language:English
Published: SpringerOpen 2022-10-01
Series:Annals of Intensive Care
Subjects:
COVID-19
Mechanical ventilation
Acute respiratory distress syndrome
Online Access:https://doi.org/10.1186/s13613-022-01070-0

Internet

https://doi.org/10.1186/s13613-022-01070-0

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