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A phenome-wide association study (PheWAS) of COVID-19 outcomes by race using the electronic health records data in Michigan Medicine.
Salvatore, Maxwell; Gu, Tian; Mack, Jasmine A; Sankar, Swaraaj Prabhu; Patil, Snehal; Valley, Thomas S; Singh, Karandeep; Nallamothu, Brahmajee K; Kheterpal, Sachin; Lisabeth, Lynda; Fritsche, Lars G; Mukherjee, Bhramar.
  • Salvatore M; Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States.
  • Gu T; Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States.
  • Mack JA; Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States.
  • Sankar SP; Rogel Cancer Center, University of Michigan Medicine, Ann Arbor, MI 48109, United States.
  • Patil S; Data Office for Clinical and Translational Research, University of Michigan, Ann Arbor, MI 41809, United States.
  • Valley TS; Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States.
  • Singh K; Precision Health, University of Michigan, Ann Arbor, MI 48109, United States.
  • Nallamothu BK; Division of Pulmonary and Critical Care Medicine and Department of Internal Medicine, University of Michigan Medicine, Ann Arbor, MI 48109, United States.
  • Kheterpal S; Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI 48109, United States.
  • Lisabeth L; Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI 48109, United States.
  • Fritsche LG; Department of Learning Health Sciences, University of Michigan, Ann Arbor, MI 48109, United States.
  • Mukherjee B; Division of Cardiovascular Medicine and Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, United States.
medRxiv ; 2021 Feb 20.
Article in English | MEDLINE | ID: covidwho-721052
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
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ABSTRACT

BACKGROUND:

We perform a phenome-wide scan to identify pre-existing conditions related to COVID-19 susceptibility and prognosis across the medical phenome and how they vary by race.

METHODS:

The study is comprised of 53,853 patients who were tested/positive for COVID-19 between March 10 and September 2, 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 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.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Year: 2021 Document Type: Article Affiliation country: 2020.06.29.20141564

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Year: 2021 Document Type: Article Affiliation country: 2020.06.29.20141564