biostudies-literatureFleuren LMCorona Research FundZonMwZorgverzekeraars Nederland32https://www.ebi.ac.uk/biostudies/studies/S-EPMC8236316biostudies-literatureUnknown9(1)<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 fIntensive care medicine experimentalRisk 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.PMC823631610430012010003Beukema MFaber HJBhulai SLabout JGommers Dvan Bommel JDutch ICU Data Sharing Against COVID-19 CollaboratorsKullberg BElbers PWGBerend JHeunks Lde Beer AAvan Manen Lvan Tienhoven AJRademaker Ede Neree Tot Babberich MScholtemeijer MGVonk SJJDriessen RSilva VCThoral PJGrady BPXTonutti MHerter WEvan Geest ESimons KSFornasa MFijen JWvan den Bogaard BFesten-Spanjer BEntjes Rde Kruif MFrenzel Tvan Diggelen FMontenij LRettig TCDMeinders AJBrunnekreef Gde Bruin DPMehagnoul-Schipper DJvan den Akker RArnold KSvan de Gaauw BGuzman DRAchterberg SAries Mvan der Heiden EKoetsier Pvan der Heiden PWagemakers LZijlstra GJRamnarain DGirbes ARJde Ruijter WOuweneel DHendriks SHADijkstra ASlooter Avan den Tempel WReuland MCBeudel MHendriks TQuintarelli DThijssens OSimons BHouwert Tvan Tellingen MKlausch THoogendoorn MReidinga ACMachado TDongelmans DADam TAvan Gemeren CCWvan Koesveld JJMPeters MKarakus AOostdijk EPickkers Pde Bruin MNooteboom FGCASpijkstra JJde Jager CPCNoorduijn Londono Rde Man ALalisang RCASchroten Nvan Osch FHMvan der Spoel HLens JWils EJGeutjes DRigter SBoersma WGBoelens ADRijkeboer AAHana Avan Raalte Rvan der Meer NJMUrlings-Strop LCLutisan JCremer OLJuffermans NDormans TPeppink JSmit EGMBosman RJvan der Pol HGAde Grooth HJNowitzky Rde Jong Rde Jong Pvan den Oever NCGHovenkamp HCornet ADRoggeveen Lde Vries HHassouni AECina GArbous Sde Keizer NFFleuren LMvan den Berg RKamps MJAKuiper MBeishuizen AfalseRisk 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.<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 f2021-01-01T00:00:00Z2021 Jun2025-04-18T21:04:51.035Z2022-02-10T18:48:38.47ZS-EPMC82363163418002510.1186/s40635-021-00397-5