The search result changed since you submitted your search request. Documents might be displayed in a different sort order.
  • search hit 9 of 193
Back to Result List

COVID-19 and the kidney: A retrospective analysis of 37 critically ill patients using machine learning

  • Introduction There is evidence that SARS-CoV2 has a particular affinity for kidney tissue and is often associated with kidney failure. Methods 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. Results 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. Conclusions 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.

Download full text files

Export metadata

Additional Services

Search Google Scholar
Metadaten
Author:Anna Laura Herzog, Holger K. von Jouanne-Diedrich, Christoph Wanner, Dirk Weismann, Tobias Schlesinger, Patrick Meybohm, Jan Stumpner
URL:https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0251932
DOI:https://doi.org/https://doi.org/10.1371/journal.pone.0251932
Parent Title (English):Plos one
Editor:Raffaele Serra
Document Type:Article
Language:English
Year of Completion:2021
Date of first Publication:2021/05/20
Release Date:2021/07/05
GND Keyword:COVID-19; Maschinelles Lernen; Niereninsuffizienz
Volume:2021
Issue:Online/2021
First Page:1
Last Page:15
Urheberrecht:1
research focus :Wissensmanagement und Strukturwandel / Business Transformation and Innovation Management
Licence (German):Keine Lizenz - es gilt das deutsche Urheberrecht
Einverstanden ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.