A phenome-wide association study (PheWAS) of COVID-19 outcomes by race using the electronic health records data in Michigan Medicine

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

doi:10.1101/2020.06.29.20141564

A phenome-wide association study (PheWAS) of COVID-19 outcomes by race using the electronic health records data in Michigan Medicine

medRxiv [Preprint]. 2021 Feb 20:2020.06.29.20141564. doi: 10.1101/2020.06.29.20141564.

Authors

Maxwell Salvatore^#¹, Tian Gu^#¹, Jasmine A Mack¹, Swaraaj Prabhu Sankar^{2

3}, Snehal Patil^{1

4}, Thomas S Valley^{5

6}, Karandeep Singh^{6

7}, Brahmajee K Nallamothu⁸, Sachin Kheterpal^{6

9}, Lynda Lisabeth¹⁰, Lars G Fritsche^{1

2

11}, Bhramar Mukherjee^{1

2

4}

Affiliations

¹ Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States.
² Rogel Cancer Center, University of Michigan Medicine, Ann Arbor, MI 48109, United States.
³ Data Office for Clinical and Translational Research, University of Michigan, Ann Arbor, MI 41809, United States.
⁴ Precision Health, University of Michigan, Ann Arbor, MI 48109, United States.
⁵ Division of Pulmonary and Critical Care Medicine and Department of Internal Medicine, University of Michigan Medicine, Ann Arbor, MI 48109, United States.
⁶ Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI 48109, United States.
⁷ Department of Learning Health Sciences, University of Michigan, Ann Arbor, MI 48109, United States.
⁸ Division of Cardiovascular Medicine and Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, United States.
⁹ Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, United States.
¹⁰ Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States.
¹¹ Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States.

^# Contributed equally.

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: EHR; biobank; health disparities; odds ratio; phenome; risk profile.

Publication types

Preprint

Abstract

Publication types

Grants and funding