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Electronic health record analysis identifies kidney disease as the leading risk factor for hospitalization in confirmed COVID-19 patients

BACKGROUND: Empirical data on conditions that increase risk of coronavirus disease 2019 (COVID-19) progression are needed to identify high risk individuals. We performed a comprehensive quantitative assessment of pre-existing clinical phenotypes associated with COVID-19-related hospitalization. METH...

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Autores principales: Oetjens, Matthew T., Luo, Jonathan Z., Chang, Alexander, Leader, Joseph B., Hartzel, Dustin N., Moore, Bryn S., Strande, Natasha T., Kirchner, H. Lester, Ledbetter, David H., Justice, Anne E., Carey, David J., Mirshahi, Tooraj
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7660530/
https://www.ncbi.nlm.nih.gov/pubmed/33180868
http://dx.doi.org/10.1371/journal.pone.0242182
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author Oetjens, Matthew T.
Luo, Jonathan Z.
Chang, Alexander
Leader, Joseph B.
Hartzel, Dustin N.
Moore, Bryn S.
Strande, Natasha T.
Kirchner, H. Lester
Ledbetter, David H.
Justice, Anne E.
Carey, David J.
Mirshahi, Tooraj
author_facet Oetjens, Matthew T.
Luo, Jonathan Z.
Chang, Alexander
Leader, Joseph B.
Hartzel, Dustin N.
Moore, Bryn S.
Strande, Natasha T.
Kirchner, H. Lester
Ledbetter, David H.
Justice, Anne E.
Carey, David J.
Mirshahi, Tooraj
author_sort Oetjens, Matthew T.
collection PubMed
description BACKGROUND: Empirical data on conditions that increase risk of coronavirus disease 2019 (COVID-19) progression are needed to identify high risk individuals. We performed a comprehensive quantitative assessment of pre-existing clinical phenotypes associated with COVID-19-related hospitalization. METHODS: Phenome-wide association study (PheWAS) of SARS-CoV-2-positive patients from an integrated health system (Geisinger) with system-level outpatient/inpatient COVID-19 testing capacity and retrospective electronic health record (EHR) data to assess pre-COVID-19 pandemic clinical phenotypes associated with hospital admission (hospitalization). RESULTS: Of 12,971 individuals tested for SARS-CoV-2 with sufficient pre-COVID-19 pandemic EHR data at Geisinger, 1604 were SARS-CoV-2 positive and 354 required hospitalization. We identified 21 clinical phenotypes in 5 disease categories meeting phenome-wide significance (P<1.60x10(-4)), including: six kidney phenotypes, e.g. end stage renal disease or stage 5 CKD (OR = 11.07, p = 1.96x10(-8)), six cardiovascular phenotypes, e.g. congestive heart failure (OR = 3.8, p = 3.24x10(-5)), five respiratory phenotypes, e.g. chronic airway obstruction (OR = 2.54, p = 3.71x10(-5)), and three metabolic phenotypes, e.g. type 2 diabetes (OR = 1.80, p = 7.51x10(-5)). Additional analyses defining CKD based on estimated glomerular filtration rate, confirmed high risk of hospitalization associated with pre-existing stage 4 CKD (OR 2.90, 95% CI: 1.47, 5.74), stage 5 CKD/dialysis (OR 8.83, 95% CI: 2.76, 28.27), and kidney transplant (OR 14.98, 95% CI: 2.77, 80.8) but not stage 3 CKD (OR 1.03, 95% CI: 0.71, 1.48). CONCLUSIONS: This study provides quantitative estimates of the contribution of pre-existing clinical phenotypes to COVID-19 hospitalization and highlights kidney disorders as the strongest factors associated with hospitalization in an integrated US healthcare system.
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spelling pubmed-76605302020-11-18 Electronic health record analysis identifies kidney disease as the leading risk factor for hospitalization in confirmed COVID-19 patients Oetjens, Matthew T. Luo, Jonathan Z. Chang, Alexander Leader, Joseph B. Hartzel, Dustin N. Moore, Bryn S. Strande, Natasha T. Kirchner, H. Lester Ledbetter, David H. Justice, Anne E. Carey, David J. Mirshahi, Tooraj PLoS One Research Article BACKGROUND: Empirical data on conditions that increase risk of coronavirus disease 2019 (COVID-19) progression are needed to identify high risk individuals. We performed a comprehensive quantitative assessment of pre-existing clinical phenotypes associated with COVID-19-related hospitalization. METHODS: Phenome-wide association study (PheWAS) of SARS-CoV-2-positive patients from an integrated health system (Geisinger) with system-level outpatient/inpatient COVID-19 testing capacity and retrospective electronic health record (EHR) data to assess pre-COVID-19 pandemic clinical phenotypes associated with hospital admission (hospitalization). RESULTS: Of 12,971 individuals tested for SARS-CoV-2 with sufficient pre-COVID-19 pandemic EHR data at Geisinger, 1604 were SARS-CoV-2 positive and 354 required hospitalization. We identified 21 clinical phenotypes in 5 disease categories meeting phenome-wide significance (P<1.60x10(-4)), including: six kidney phenotypes, e.g. end stage renal disease or stage 5 CKD (OR = 11.07, p = 1.96x10(-8)), six cardiovascular phenotypes, e.g. congestive heart failure (OR = 3.8, p = 3.24x10(-5)), five respiratory phenotypes, e.g. chronic airway obstruction (OR = 2.54, p = 3.71x10(-5)), and three metabolic phenotypes, e.g. type 2 diabetes (OR = 1.80, p = 7.51x10(-5)). Additional analyses defining CKD based on estimated glomerular filtration rate, confirmed high risk of hospitalization associated with pre-existing stage 4 CKD (OR 2.90, 95% CI: 1.47, 5.74), stage 5 CKD/dialysis (OR 8.83, 95% CI: 2.76, 28.27), and kidney transplant (OR 14.98, 95% CI: 2.77, 80.8) but not stage 3 CKD (OR 1.03, 95% CI: 0.71, 1.48). CONCLUSIONS: This study provides quantitative estimates of the contribution of pre-existing clinical phenotypes to COVID-19 hospitalization and highlights kidney disorders as the strongest factors associated with hospitalization in an integrated US healthcare system. Public Library of Science 2020-11-12 /pmc/articles/PMC7660530/ /pubmed/33180868 http://dx.doi.org/10.1371/journal.pone.0242182 Text en © 2020 Oetjens et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://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
Oetjens, Matthew T.
Luo, Jonathan Z.
Chang, Alexander
Leader, Joseph B.
Hartzel, Dustin N.
Moore, Bryn S.
Strande, Natasha T.
Kirchner, H. Lester
Ledbetter, David H.
Justice, Anne E.
Carey, David J.
Mirshahi, Tooraj
Electronic health record analysis identifies kidney disease as the leading risk factor for hospitalization in confirmed COVID-19 patients
title Electronic health record analysis identifies kidney disease as the leading risk factor for hospitalization in confirmed COVID-19 patients
title_full Electronic health record analysis identifies kidney disease as the leading risk factor for hospitalization in confirmed COVID-19 patients
title_fullStr Electronic health record analysis identifies kidney disease as the leading risk factor for hospitalization in confirmed COVID-19 patients
title_full_unstemmed Electronic health record analysis identifies kidney disease as the leading risk factor for hospitalization in confirmed COVID-19 patients
title_short Electronic health record analysis identifies kidney disease as the leading risk factor for hospitalization in confirmed COVID-19 patients
title_sort electronic health record analysis identifies kidney disease as the leading risk factor for hospitalization in confirmed covid-19 patients
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7660530/
https://www.ncbi.nlm.nih.gov/pubmed/33180868
http://dx.doi.org/10.1371/journal.pone.0242182
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