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.
van Osch, Frits
Aries, Marcel |
| Dokumenttyp: |
Artikel |
| Erscheinungsdatum: |
2021 |
| Reihe/Periodikum: |
Fleuren , L M , Tonutti , M , 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 , Dutch ICU Data Sharing Against Covid-19 Collaborators , van Osch , F & Aries , M 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 |
| Schlagwörter: |
COVID-19
/ Mortality prediction
/ Risk factors
/ Machine learning
/ SCORE |
| Sprache: |
Englisch |
| Permalink: |
https://search.fid-benelux.de/Record/base-29020774 |
| Datenquelle: |
BASE;
Originalkatalog |
| Powered By: |
BASE |
| Link(s) :
|
https://cris.maastrichtuniversity.nl/en/publications/44d7f08e-277a-4ce9-816e-20ea70ce8a7f |
