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Using dipstick urinalysis to predict development of acute kidney injury in patients with COVID-19
BACKGROUND: Acute kidney injury (AKI) is a common complication in patients hospitalized with COVID-19 and may require renal replacement therapy (RRT). Dipstick urinalysis is frequently obtained, but data regarding the prognostic value of hematuria and proteinuria for kidney outcomes is scarce. METHO...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8805668/ https://www.ncbi.nlm.nih.gov/pubmed/35105331 http://dx.doi.org/10.1186/s12882-022-02677-y |
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author | McAdams, Meredith C. Li, Michael Xu, Pin Gregg, L. Parker Patel, Jiten Willett, Duwayne L. Velasco, Ferdinand Lehmann, Christoph U. Hedayati, S. Susan |
author_facet | McAdams, Meredith C. Li, Michael Xu, Pin Gregg, L. Parker Patel, Jiten Willett, Duwayne L. Velasco, Ferdinand Lehmann, Christoph U. Hedayati, S. Susan |
author_sort | McAdams, Meredith C. |
collection | PubMed |
description | BACKGROUND: Acute kidney injury (AKI) is a common complication in patients hospitalized with COVID-19 and may require renal replacement therapy (RRT). Dipstick urinalysis is frequently obtained, but data regarding the prognostic value of hematuria and proteinuria for kidney outcomes is scarce. METHODS: Patients with positive severe acute respiratory syndrome-coronavirus 2 (SARS-CoV2) PCR, who had a urinalysis obtained on admission to one of 20 hospitals, were included. Nested models with degree of hematuria and proteinuria were used to predict AKI and RRT during admission. Presence of Chronic Kidney Disease (CKD) and baseline serum creatinine were added to test improvement in model fit. RESULTS: Of 5,980 individuals, 829 (13.9%) developed an AKI during admission, and 149 (18.0%) of those with AKI received RRT. Proteinuria and hematuria degrees significantly increased with AKI severity (P < 0.001 for both). Any degree of proteinuria and hematuria was associated with an increased risk of AKI and RRT. In predictive models for AKI, presence of CKD improved the area under the curve (AUC) (95% confidence interval) to 0.73 (0.71, 0.75), P < 0.001, and adding baseline creatinine improved the AUC to 0.85 (0.83, 0.86), P < 0.001, when compared to the base model AUC using only proteinuria and hematuria, AUC = 0.64 (0.62, 0.67). In RRT models, CKD status improved the AUC to 0.78 (0.75, 0.82), P < 0.001, and baseline creatinine improved the AUC to 0.84 (0.80, 0.88), P < 0.001, compared to the base model, AUC = 0.72 (0.68, 0.76). There was no significant improvement in model discrimination when both CKD and baseline serum creatinine were included. CONCLUSIONS: Proteinuria and hematuria values on dipstick urinalysis can be utilized to predict AKI and RRT in hospitalized patients with COVID-19. We derived formulas using these two readily available values to help prognosticate kidney outcomes in these patients. Furthermore, the incorporation of CKD or baseline creatinine increases the accuracy of these formulas. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12882-022-02677-y. |
format | Online Article Text |
id | pubmed-8805668 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-88056682022-02-02 Using dipstick urinalysis to predict development of acute kidney injury in patients with COVID-19 McAdams, Meredith C. Li, Michael Xu, Pin Gregg, L. Parker Patel, Jiten Willett, Duwayne L. Velasco, Ferdinand Lehmann, Christoph U. Hedayati, S. Susan BMC Nephrol Research BACKGROUND: Acute kidney injury (AKI) is a common complication in patients hospitalized with COVID-19 and may require renal replacement therapy (RRT). Dipstick urinalysis is frequently obtained, but data regarding the prognostic value of hematuria and proteinuria for kidney outcomes is scarce. METHODS: Patients with positive severe acute respiratory syndrome-coronavirus 2 (SARS-CoV2) PCR, who had a urinalysis obtained on admission to one of 20 hospitals, were included. Nested models with degree of hematuria and proteinuria were used to predict AKI and RRT during admission. Presence of Chronic Kidney Disease (CKD) and baseline serum creatinine were added to test improvement in model fit. RESULTS: Of 5,980 individuals, 829 (13.9%) developed an AKI during admission, and 149 (18.0%) of those with AKI received RRT. Proteinuria and hematuria degrees significantly increased with AKI severity (P < 0.001 for both). Any degree of proteinuria and hematuria was associated with an increased risk of AKI and RRT. In predictive models for AKI, presence of CKD improved the area under the curve (AUC) (95% confidence interval) to 0.73 (0.71, 0.75), P < 0.001, and adding baseline creatinine improved the AUC to 0.85 (0.83, 0.86), P < 0.001, when compared to the base model AUC using only proteinuria and hematuria, AUC = 0.64 (0.62, 0.67). In RRT models, CKD status improved the AUC to 0.78 (0.75, 0.82), P < 0.001, and baseline creatinine improved the AUC to 0.84 (0.80, 0.88), P < 0.001, compared to the base model, AUC = 0.72 (0.68, 0.76). There was no significant improvement in model discrimination when both CKD and baseline serum creatinine were included. CONCLUSIONS: Proteinuria and hematuria values on dipstick urinalysis can be utilized to predict AKI and RRT in hospitalized patients with COVID-19. We derived formulas using these two readily available values to help prognosticate kidney outcomes in these patients. Furthermore, the incorporation of CKD or baseline creatinine increases the accuracy of these formulas. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12882-022-02677-y. BioMed Central 2022-02-01 /pmc/articles/PMC8805668/ /pubmed/35105331 http://dx.doi.org/10.1186/s12882-022-02677-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research McAdams, Meredith C. Li, Michael Xu, Pin Gregg, L. Parker Patel, Jiten Willett, Duwayne L. Velasco, Ferdinand Lehmann, Christoph U. Hedayati, S. Susan Using dipstick urinalysis to predict development of acute kidney injury in patients with COVID-19 |
title | Using dipstick urinalysis to predict development of acute kidney injury in patients with COVID-19 |
title_full | Using dipstick urinalysis to predict development of acute kidney injury in patients with COVID-19 |
title_fullStr | Using dipstick urinalysis to predict development of acute kidney injury in patients with COVID-19 |
title_full_unstemmed | Using dipstick urinalysis to predict development of acute kidney injury in patients with COVID-19 |
title_short | Using dipstick urinalysis to predict development of acute kidney injury in patients with COVID-19 |
title_sort | using dipstick urinalysis to predict development of acute kidney injury in patients with covid-19 |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8805668/ https://www.ncbi.nlm.nih.gov/pubmed/35105331 http://dx.doi.org/10.1186/s12882-022-02677-y |
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