A Phenome-Wide Association Study (PheWAS) of COVID-19 Outcomes by Race Using the Electronic Health Records Data in Michigan Medicine

A Phenome-Wide Association Study (PheWAS) of COVID-19 Outcomes by Race Using the Electronic Health Records Data in Michigan Medicine

Background: We performed a phenome-wide association study to identify pre-existing conditions related to Coronavirus disease 2019 (COVID-19) prognosis across the medical phenome and how they vary by race. Methods: The study is comprised of 53,853 patients who were tested/diagnosed for COVID-19 betwe...

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Main Authors: Maxwell Salvatore, Tian Gu, Jasmine A. Mack, Swaraaj Prabhu Sankar, Snehal Patil, Thomas S. Valley, Karandeep Singh, Brahmajee K. Nallamothu, Sachin Kheterpal, Lynda Lisabeth, Lars G. Fritsche, Bhramar Mukherjee
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Journal of Clinical Medicine
Subjects:
biobank
health disparities
EHR
phenome
odds ratio
risk profile
Online Access:https://www.mdpi.com/2077-0383/10/7/1351
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spelling doaj-c0602c91bd0745a9808e62982f28e1592021-03-26T00:00:43ZengMDPI AGJournal of Clinical Medicine2077-03832021-03-01101351135110.3390/jcm10071351A Phenome-Wide Association Study (PheWAS) of COVID-19 Outcomes by Race Using the Electronic Health Records Data in Michigan MedicineMaxwell Salvatore0Tian Gu1Jasmine A. Mack2Swaraaj Prabhu Sankar3Snehal Patil4Thomas S. Valley5Karandeep Singh6Brahmajee K. Nallamothu7Sachin Kheterpal8Lynda Lisabeth9Lars G. Fritsche10Bhramar Mukherjee11Department of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USADepartment of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USADepartment of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USACenter for Precision Health Data Science, University of Michigan, Ann Arbor, MI 48109, USADepartment of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USADivision of Pulmonary and Critical Care Medicine, University of Michigan Medicine, Ann Arbor, MI 48109, USAInstitute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI 48109, USADepartment of Internal Medicine, Michigan Medicine, Ann Arbor, MI 48109, USAInstitute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI 48109, USADepartment of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, USADepartment of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USADepartment of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USABackground: We performed a phenome-wide association study to identify pre-existing conditions related to Coronavirus disease 2019 (COVID-19) prognosis across the medical phenome and how they vary by race. Methods: The study is comprised of 53,853 patients who were tested/diagnosed for COVID-19 between 10 March and 2 September 2020 at a large academic medical center. Results: Pre-existing conditions strongly associated with hospitalization were renal failure, pulmonary heart disease, and respiratory failure. Hematopoietic conditions were associated with intensive care unit (ICU) admission/mortality and mental disorders were associated with mortality in non-Hispanic Whites. Circulatory system and genitourinary conditions were associated with ICU admission/mortality in non-Hispanic Blacks. Conclusions: Understanding pre-existing clinical diagnoses related to COVID-19 outcomes informs the need for targeted screening to support specific vulnerable populations to improve disease prevention and healthcare delivery.https://www.mdpi.com/2077-0383/10/7/1351biobankhealth disparitiesEHRphenomeodds ratiorisk profile
collection DOAJ
language English
format Article
sources DOAJ
author Maxwell Salvatore
Tian Gu
Jasmine A. Mack
Swaraaj Prabhu Sankar
Snehal Patil
Thomas S. Valley
Karandeep Singh
Brahmajee K. Nallamothu
Sachin Kheterpal
Lynda Lisabeth
Lars G. Fritsche
Bhramar Mukherjee
spellingShingle Maxwell Salvatore
Tian Gu
Jasmine A. Mack
Swaraaj Prabhu Sankar
Snehal Patil
Thomas S. Valley
Karandeep Singh
Brahmajee K. Nallamothu
Sachin Kheterpal
Lynda Lisabeth
Lars G. Fritsche
Bhramar Mukherjee
A Phenome-Wide Association Study (PheWAS) of COVID-19 Outcomes by Race Using the Electronic Health Records Data in Michigan Medicine
Journal of Clinical Medicine
biobank
health disparities
EHR
phenome
odds ratio
risk profile
author_facet Maxwell Salvatore
Tian Gu
Jasmine A. Mack
Swaraaj Prabhu Sankar
Snehal Patil
Thomas S. Valley
Karandeep Singh
Brahmajee K. Nallamothu
Sachin Kheterpal
Lynda Lisabeth
Lars G. Fritsche
Bhramar Mukherjee
author_sort Maxwell Salvatore
title A Phenome-Wide Association Study (PheWAS) of COVID-19 Outcomes by Race Using the Electronic Health Records Data in Michigan Medicine
title_short A Phenome-Wide Association Study (PheWAS) of COVID-19 Outcomes by Race Using the Electronic Health Records Data in Michigan Medicine
title_full A Phenome-Wide Association Study (PheWAS) of COVID-19 Outcomes by Race Using the Electronic Health Records Data in Michigan Medicine
title_fullStr A Phenome-Wide Association Study (PheWAS) of COVID-19 Outcomes by Race Using the Electronic Health Records Data in Michigan Medicine
title_full_unstemmed A Phenome-Wide Association Study (PheWAS) of COVID-19 Outcomes by Race Using the Electronic Health Records Data in Michigan Medicine
title_sort phenome-wide association study (phewas) of covid-19 outcomes by race using the electronic health records data in michigan medicine
publisher MDPI AG
series Journal of Clinical Medicine
issn 2077-0383
publishDate 2021-03-01
description Background: We performed a phenome-wide association study to identify pre-existing conditions related to Coronavirus disease 2019 (COVID-19) prognosis across the medical phenome and how they vary by race. Methods: The study is comprised of 53,853 patients who were tested/diagnosed for COVID-19 between 10 March and 2 September 2020 at a large academic medical center. Results: Pre-existing conditions strongly associated with hospitalization were renal failure, pulmonary heart disease, and respiratory failure. Hematopoietic conditions were associated with intensive care unit (ICU) admission/mortality and mental disorders were associated with mortality in non-Hispanic Whites. Circulatory system and genitourinary conditions were associated with ICU admission/mortality in non-Hispanic Blacks. Conclusions: Understanding pre-existing clinical diagnoses related to COVID-19 outcomes informs the need for targeted screening to support specific vulnerable populations to improve disease prevention and healthcare delivery.
topic biobank
health disparities
EHR
phenome
odds ratio
risk profile
url https://www.mdpi.com/2077-0383/10/7/1351
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