Cargando…
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...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8136725/ https://www.ncbi.nlm.nih.gov/pubmed/34015009 http://dx.doi.org/10.1371/journal.pone.0251932 |
_version_ | 1783695485958094848 |
---|---|
author | Herzog, Anna Laura von Jouanne-Diedrich, Holger K. Wanner, Christoph Weismann, Dirk Schlesinger, Tobias Meybohm, Patrick Stumpner, Jan |
author_facet | Herzog, Anna Laura von Jouanne-Diedrich, Holger K. Wanner, Christoph Weismann, Dirk Schlesinger, Tobias Meybohm, Patrick Stumpner, Jan |
author_sort | Herzog, Anna Laura |
collection | PubMed |
description | 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/m(2) 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. |
format | Online Article Text |
id | pubmed-8136725 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-81367252021-06-02 COVID-19 and the kidney: A retrospective analysis of 37 critically ill patients using machine learning Herzog, Anna Laura von Jouanne-Diedrich, Holger K. Wanner, Christoph Weismann, Dirk Schlesinger, Tobias Meybohm, Patrick Stumpner, Jan PLoS One Research Article 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/m(2) 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. Public Library of Science 2021-05-20 /pmc/articles/PMC8136725/ /pubmed/34015009 http://dx.doi.org/10.1371/journal.pone.0251932 Text en © 2021 Herzog et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Herzog, Anna Laura von Jouanne-Diedrich, Holger K. Wanner, Christoph Weismann, Dirk Schlesinger, Tobias Meybohm, Patrick Stumpner, Jan COVID-19 and the kidney: A retrospective analysis of 37 critically ill patients using machine learning |
title | COVID-19 and the kidney: A retrospective analysis of 37 critically ill patients using machine learning |
title_full | COVID-19 and the kidney: A retrospective analysis of 37 critically ill patients using machine learning |
title_fullStr | COVID-19 and the kidney: A retrospective analysis of 37 critically ill patients using machine learning |
title_full_unstemmed | COVID-19 and the kidney: A retrospective analysis of 37 critically ill patients using machine learning |
title_short | COVID-19 and the kidney: A retrospective analysis of 37 critically ill patients using machine learning |
title_sort | covid-19 and the kidney: a retrospective analysis of 37 critically ill patients using machine learning |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8136725/ https://www.ncbi.nlm.nih.gov/pubmed/34015009 http://dx.doi.org/10.1371/journal.pone.0251932 |
work_keys_str_mv | AT herzogannalaura covid19andthekidneyaretrospectiveanalysisof37criticallyillpatientsusingmachinelearning AT vonjouannediedrichholgerk covid19andthekidneyaretrospectiveanalysisof37criticallyillpatientsusingmachinelearning AT wannerchristoph covid19andthekidneyaretrospectiveanalysisof37criticallyillpatientsusingmachinelearning AT weismanndirk covid19andthekidneyaretrospectiveanalysisof37criticallyillpatientsusingmachinelearning AT schlesingertobias covid19andthekidneyaretrospectiveanalysisof37criticallyillpatientsusingmachinelearning AT meybohmpatrick covid19andthekidneyaretrospectiveanalysisof37criticallyillpatientsusingmachinelearning AT stumpnerjan covid19andthekidneyaretrospectiveanalysisof37criticallyillpatientsusingmachinelearning |