BACKGROUND: The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV)... Mehr ...
| Verfasser: |
Fleuren, Lucas M
Tonutti, Michele
de Bruin, Daan P
Lalisang, Robbert C A
Dam, Tariq A
Gommers, Diederik
Cremer, Olaf L
Bosman, Rob J
Vonk, Sebastiaan J J
Fornasa, Mattia
Machado, Tomas
van der Meer, Nardo J M
Rigter, Sander
Wils, Evert-Jan
Frenzel, Tim
Dongelmans, Dave A
de Jong, Remko
Peters, Marco
Kamps, Marlijn J A
Ramnarain, Dharmanand
Nowitzky, Ralph
Nooteboom, Fleur G C A
de Ruijter, Wouter
Urlings-Strop, Louise C
Smit, Ellen G M
Mehagnoul-Schipper, D Jannet
Dormans, Tom
de Jager, Cornelis P C
Hendriks, Stefaan H A
Oostdijk, Evelien
Reidinga, Auke C
Festen-Spanjer, Barbara
Brunnekreef, Gert
Cornet, Alexander D
van den Tempel, Walter
Boelens, Age D
Koetsier, Peter
Lens, Judith
Achterberg, Sefanja
Faber, Harald J
Karakus, A
Beukema, Menno
Entjes, Robert
de Jong, Paul
Houwert, Taco
Hovenkamp, Hidde
Noorduijn Londono, Roberto
Quintarelli, Davide
Scholtemeijer, Martijn G
de Beer, Aletta A |
| Dokumenttyp: |
Artikel |
| Erscheinungsdatum: |
2021 |
| Reihe/Periodikum: |
Fleuren , L M , Tonutti , M , Dutch ICU Data Sharing Against Covid-19 Collaborators , de Bruin , D P , Lalisang , R C A , Dam , T A , Gommers , D , Cremer , O L , Bosman , R J , Vonk , S J J , Fornasa , M , Machado , T , van der Meer , N J M , Rigter , S , Wils , E-J , Frenzel , T , Dongelmans , D A , de Jong , R , Peters , M , Kamps , M J A , Ramnarain , D , Nowitzky , R , Nooteboom , F G C A , de Ruijter , W , Urlings-Strop , L C , Smit , E G M , Mehagnoul-Schipper , D J , Dormans , T , de Jager , C P C , Hendriks , S H A , Oostdijk , E , Reidinga , A C , Festen-Spanjer , B , Brunnekreef , G , Cornet , A D , van den Tempel , W , Boelens , A D , Koetsier , P , Lens , J , Achterberg , S , Faber , H J , Karakus , A , Beukema , M , Entjes , R , de Jong , P , Houwert , T , Hovenkamp , H , Noorduijn Londono , R , Quintarelli , D , Scholtemeijer , M G & de Beer , A A 2021 , ' Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients : a multicenter machine learning study with highly granular data from the Dutch Data Warehouse ' , Intensive Care Medicine Experimental , vol. 9 , no. 1 , 32 . https://doi.org/10.1186/s40635-021-00397-5 |
| Sprache: |
Englisch |
| Permalink: |
https://search.fid-benelux.de/Record/base-26684618 |
| Datenquelle: |
BASE;
Originalkatalog |
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BASE |
| Link(s) :
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https://pure.eur.nl/en/publications/b40890e4-d125-41fa-ba9e-940d3010a405 |
