https://socictopen.socict.org/files/original/b6305808e06ca5620e58181334b1d293.pdf 9015d21963956bdc5fb4b8678b757d11 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource COVID-19 and the kidney: A retrospective analysis of 37 critically ill patients using machine learning. Creator An entity primarily responsible for making the resource Anna Laura Herzog, Holger K von Jouanne-Diedrich, Christoph Wanner, Dirk Weismann, Tobias Schlesinger, Patrick Meybohm, Jan Stumpner Description An account of the resource There is evidence that SARS-CoV2 has a particular affinity for kidney tissue and is often associated with kidney failure. We assessed whether proteinuria can be predictive of kidney failure, the development of chronic kidney disease, and mortality in 37 critically ill COVID-19 patients. We used machine learning (ML) methods as decision trees and cut-off points created by the OneR package to add new aspects, even in smaller cohorts. Among a total of 37 patients, 24 suffered higher-grade renal failure, 20 of whom required kidney replacement therapy. More than 40% of patients remained on hemodialysis after intensive care unit discharge or died (27%). Due to frequent anuria proteinuria measured in two-thirds of the patients, it was not predictive for the investigated endpoints; albuminuria was higher in patients with AKI 3, but the difference was not significant. ML found cut-off points of >31.4 kg/m2 for BMI and >69 years for age, constructed decision trees with great accuracy, and identified highly predictive variables for outcome and remaining chronic kidney disease. Different ML methods and their clinical application, especially decision trees, can provide valuable support for clinical decisions. Presence of proteinuria was not predictive of CKD or AKI and should be confirmed in a larger cohort. Date A point or period of time associated with an event in the lifecycle of the resource 2021 Identifier An unambiguous reference to the resource within a given context 10.1371/journal.pone.0251932 Source A related resource from which the described resource is derived PloS one