{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Fleuren LM"],"funding":["Corona Research Fund","ZonMw","Zorgverzekeraars Nederland"],"pagination":["32"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8236316"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["9(1)"],"pubmed_abstract":["<h4>Background</h4>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) in COVID-19 patients.<h4>Methods</h4>The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible f"],"journal":["Intensive care medicine experimental"],"pubmed_title":["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."],"pmcid":["PMC8236316"],"funding_grant_id":["10430012010003"],"pubmed_authors":["Beukema M","Faber HJ","Bhulai S","Labout J","Gommers D","van Bommel J","Dutch ICU Data Sharing Against COVID-19 Collaborators","Kullberg B","Elbers PWG","Berend J","Heunks L","de Beer AA","van Manen L","van Tienhoven AJ","Rademaker E","de Neree Tot Babberich M","Scholtemeijer MG","Vonk SJJ","Driessen R","Silva VC","Thoral PJ","Grady BPX","Tonutti M","Herter WE","van Geest E","Simons KS","Fornasa M","Fijen JW","van den Bogaard B","Festen-Spanjer B","Entjes R","de Kruif M","Frenzel T","van Diggelen F","Montenij L","Rettig TCD","Meinders AJ","Brunnekreef G","de Bruin DP","Mehagnoul-Schipper DJ","van den Akker R","Arnold KS","van de Gaauw B","Guzman DR","Achterberg S","Aries M","van der Heiden E","Koetsier P","van der Heiden P","Wagemakers L","Zijlstra GJ","Ramnarain D","Girbes ARJ","de Ruijter W","Ouweneel D","Hendriks SHA","Dijkstra A","Slooter A","van den Tempel W","Reuland MC","Beudel M","Hendriks T","Quintarelli D","Thijssens O","Simons B","Houwert T","van Tellingen M","Klausch T","Hoogendoorn M","Reidinga AC","Machado T","Dongelmans DA","Dam TA","van Gemeren CCW","van Koesveld JJM","Peters M","Karakus A","Oostdijk E","Pickkers P","de Bruin M","Nooteboom FGCA","Spijkstra JJ","de Jager CPC","Noorduijn Londono R","de Man A","Lalisang RCA","Schroten N","van Osch FHM","van der Spoel H","Lens J","Wils EJ","Geutjes D","Rigter S","Boersma WG","Boelens AD","Rijkeboer AA","Hana A","van Raalte R","van der Meer NJM","Urlings-Strop LC","Lutisan J","Cremer OL","Juffermans N","Dormans T","Peppink J","Smit EGM","Bosman RJ","van der Pol HGA","de Grooth HJ","Nowitzky R","de Jong R","de Jong P","van den Oever NCG","Hovenkamp H","Cornet AD","Roggeveen L","de Vries H","Hassouni AE","Cina G","Arbous S","de Keizer NF","Fleuren LM","van den Berg R","Kamps MJA","Kuiper M","Beishuizen A"],"additional_accession":[]},"is_claimable":false,"name":"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.","description":"<h4>Background</h4>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) in COVID-19 patients.<h4>Methods</h4>The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible f","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Jun","modification":"2025-04-18T21:04:51.035Z","creation":"2022-02-10T18:48:38.47Z"},"accession":"S-EPMC8236316","cross_references":{"pubmed":["34180025"],"doi":["10.1186/s40635-021-00397-5"]}}