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

Autor: 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., Cinà, Giovanni, Beudel, Martijn, de Keizer, Nicolet F., Hoogendoorn, Mark, Girbes, Armand R.J., Herter, Willem E., Elbers, Paul W.G., Thoral, Patrick J., Rettig, Thijs C.D., Reuland, M. C., van Manen, Laura, Montenij, Leon, van Bommel, Jasper, van den Berg, Roy, van Geest, Ellen, Hana, Anisa, Boersma, W. G., van den Bogaard, B., Pickkers, Peter, van der Heiden, Pim, van Gemeren, Claudia C.W., Meinders, Arend Jan, de Bruin, Martha, Rademaker, Emma, van Osch, Frits H.M., de Kruif, Martijn, Schroten, Nicolas, Arnold, Klaas Sierk, Fijen, J. W., van Koesveld, Jacomar J.M., Simons, Koen S., Labout, Joost, van de Gaauw, Bart, Kuiper, Michael, Beishuizen, Albertus, Geutjes, Dennis, Lutisan, Johan, Grady, Bart P.X., van den Akker, Remko, Simons, Bram, Rijkeboer, A. A., Arbous, Sesmu, Aries, Marcel, van den Oever, Niels C.Gritters, van Tellingen, Martijn, Dijkstra, Annemieke, van Raalte, Rutger, Roggeveen, Luca, van Diggelen, Fuda, Hassouni, Ali el, Guzman, David Romero, Bhulai, Sandjai, Ouweneel, Dagmar, Driessen, Ronald, Peppink, Jan, de Grooth, H. J., Zijlstra, G. J., van Tienhoven, A. J., van der Heiden, Evelien, Spijkstra, Jan Jaap, van der Spoel, Hans, de Man, Angelique, Klausch, Thomas, de Vries, Heder, de Neree tot Babberich, Michael, Thijssens, Olivier, Wagemakers, Lot, van der Pol, Hilde G.A., Hendriks, Tom, Berend, Julie, Silva, Virginia Ceni, Kullberg, Bob, Heunks, Leo, Juffermans, Nicole, Slooter, Arjan

Publikováno v: Intensive Care Medicine Experimental, 9(1):32. Springer Science + Business Media
Intensive Care Medicine Experimental
Intensive Care Medicine Experimental, 9, 1
on behalf of Dutch ICU Data Sharing Against COVID-19 Collaborators 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, 32, pp. 1-15 . https://doi.org/10.1186/s40635-021-00397-5
Intensive Care Medicine Experimental, Vol 9, Iss 1, Pp 1-15 (2021)
Intensive Care Medicine Experimental, 9:32, 1-15. Springer Science + Business Media
on behalf of Dutch ICU Data Sharing Against COVID-19 Collaborators 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, pp. 32 . https://doi.org/10.1186/s40635-021-00397-5
Intensive Care Medicine Experimental, 9
Intensive Care Medicine Experimental, 9(1):32. Springer Open
Intensive Care Medicine Experimental, 9(1):32. SpringerOpen

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 det

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e970d1243bf53f19182dbe780357cc5e
https://research.vumc.nl/en/publications/acd67208-aea1-4251-a441-c6600515b1ec

Predicting pneumococcal community-acquired pneumonia in the emergency department.

Autor: Huijts, S. M.¹, Boersma, W. G.², Grobbee, D. E.¹, Gruber, W. C.³, Jansen, K. U.⁴, Kluytmans, J. A. J. W.⁵, Kuipers, B. A. F.⁶, Palmen, F.⁷, Pride, M. W.⁴, Webber, C.³, Bonten, M. J. M.^1,8

Publikováno v: Clinical Microbiology & Infection. Dec2014, Vol. 20 Issue 12, p1316-1322. 7p.

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Guideline adherence for empirical treatment of pneumonia and patient outcome. Treating pneumonia in the Netherlands

Autor: Huijts, S. M., Werkhoven, C. H., Boersma, W. G., Buijs, J., Buunk, G., Compaijen, C. J., Elden, L. J. R., Gisolf, J. E. H., Kamp, R., Kluytmans, J. A. J. W., Kuipers, B. A. F., Mager, J. J., Oppedijk, B., Palmen, F., Prins, J. M., Reemst, B., Silbermann, M. H., Tiel, F. H., Wall, E., Tjip S van der Werf, Bonten, M. J. M.

Publikováno v: Scopus-Elsevier

According to the Dutch guidelines, severity of community acquired pneumonia (CAP) (mild, moderate-severe, severe) should be based on either PSI, CURB65 or a 'pragmatic' classification. In the last mentioned, the type of ward of admission, as decided

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid_dedup__::0758bfdcc285c44a2c25da6d6cb5ad86
https://pubmed.ncbi.nlm.nih.gov/24394734