. 2021 Mar 25;10(7):1351.

doi: 10.3390/jcm10071351.

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

Maxwell Salvatore^{1

2

3}, Tian Gu^{1

2}, Jasmine A Mack¹, Swaraaj Prabhu Sankar^{2

4

5}, Snehal Patil^{1

2}, Thomas S Valley^{6

7

8}, Karandeep Singh^{8

9}, Brahmajee K Nallamothu^{7

10}, Sachin Kheterpal^{8

11}, Lynda Lisabeth³, Lars G Fritsche^{1

2

4

12}, Bhramar Mukherjee^{1

2

3}

Affiliations

¹ Department of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USA.
² Center for Precision Health Data Science, University of Michigan, Ann Arbor, MI 48109, USA.
³ Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.
⁴ Rogel Cancer Center, Michigan Medicine, Ann Arbor, MI 48109, USA.
⁵ Data Office for Clinical and Translational Research, University of Michigan, Ann Arbor, MI 41809, USA.
⁶ Division of Pulmonary and Critical Care Medicine, University of Michigan Medicine, Ann Arbor, MI 48109, USA.
⁷ Department of Internal Medicine, Michigan Medicine, Ann Arbor, MI 48109, USA.
⁸ Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI 48109, USA.
⁹ Department of Learning Health Sciences, University of Michigan, Ann Arbor, MI 48109, USA.
¹⁰ Division of Cardiovascular Medicine, Michigan Medicine, Ann Arbor, MI 48109, USA.
¹¹ Department of Anesthesiology, Michigan Medicine, Ann Arbor, MI 48109, USA.
¹² Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.

PMID: 33805886
PMCID: PMC8037108
DOI: 10.3390/jcm10071351

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

Maxwell Salvatore et al. J Clin Med. 2021.

. 2021 Mar 25;10(7):1351.

doi: 10.3390/jcm10071351.

Authors

Affiliations

¹ Department of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USA.
² Center for Precision Health Data Science, University of Michigan, Ann Arbor, MI 48109, USA.
³ Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.
⁴ Rogel Cancer Center, Michigan Medicine, Ann Arbor, MI 48109, USA.
⁵ Data Office for Clinical and Translational Research, University of Michigan, Ann Arbor, MI 41809, USA.
⁶ Division of Pulmonary and Critical Care Medicine, University of Michigan Medicine, Ann Arbor, MI 48109, USA.
⁷ Department of Internal Medicine, Michigan Medicine, Ann Arbor, MI 48109, USA.
⁸ Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI 48109, USA.
⁹ Department of Learning Health Sciences, University of Michigan, Ann Arbor, MI 48109, USA.
¹⁰ Division of Cardiovascular Medicine, Michigan Medicine, Ann Arbor, MI 48109, USA.
¹¹ Department of Anesthesiology, Michigan Medicine, Ann Arbor, MI 48109, USA.
¹² Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.

PMID: 33805886
PMCID: PMC8037108
DOI: 10.3390/jcm10071351

Abstract

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.

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

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Conflict of interest statement

The authors have no conflict of interests to declare.

Figures

**Figure 1**
Manhattan plot showing the phenome-wide association between disease conditions and hospitalization for COVID-19. Models are adjusted for age, sex, race (full cohort only), and three census tract-level socioeconomic indicators: proportion with less than high school education, proportion unemployed, and proportion with annual income below the federal poverty level. The x-axis are individual disease codes, color-coded by their corresponding disease category as described in the shared legend. The y-axis represents the −log₁₀ transformed p-value of the association. The dashed, horizontal lines represent the p = 0.05 (in orange) and the Bonferroni corrected p-value (0.05/number of tests; in red). Each point is represented by either an upward triangle indicating a positive association or a downward triangle indicating a negative association. (A): Full cohort, (B): Restricted to Whites, (C): Restricted to Blacks

**Figure 2**
Manhattan plot showing the phenome-wide association between disease conditions and ICU admission for COVID-19. Models are adjusted for age, sex, race (full cohort only), and three census tract-level socioeconomic indicators: proportion with less than high school education, proportion unemployed, and proportion with annual income below the federal poverty level. The x-axis are individual disease codes, color-coded by their corresponding disease category as described in the shared legend. The y-axis represents the −log₁₀ transformed p-value of the association. The dashed, horizontal lines represent the p = 0.05 (in orange) and the Bonferroni corrected p-value (0.05/number of tests; in red). Each point is represented by either an upward triangle indicating a positive association or a downward triangle indicating a negative association. (A): Full cohort, (B): Restricted to Whites, (C): Restricted to Blacks

**Figure 3**
Manhattan plot showing the phenome-wide association between disease conditions and prognostic outcomes for COVID-19. Models are adjusted for age, sex, race (full cohort only), and three census tract-level socioeconomic indicators: proportion with less than high school education, proportion unemployed, and proportion with annual income below the federal poverty level. The x-axis are individual disease codes, color-coded by their corresponding disease category as described in the shared legend. The y-axis represents the −log₁₀ transformed p-value of the association. The dashed, horizontal lines represent the p = 0.05 (in orange) and the Bonferroni corrected p-value (0.05/number of tests; in red). Each point is represented by either an upward triangle indicating a positive association or a downward triangle indicating a negative association. (A): Full cohort, (B): Restricted to Whites, (C): Restricted to Blacks

**Figure 4**
Venn diagrams of the top 50 traits. Each circle represents the top 50 hits from the full cohort PheWAS. Traits shared across PheWAS are stated, while the corresponding number of traits within a given disease category that are unique to that PheWAS are also provided. Abbreviations: NOS, not otherwise specified; SIRS, systemic inflammatory response syndrome

See this image and copyright information in PMC

Update of

A phenome-wide association study (PheWAS) of COVID-19 outcomes by race using the electronic health records data in Michigan Medicine.
Salvatore M, Gu T, Mack JA, Sankar SP, Patil S, Valley TS, Singh K, Nallamothu BK, Kheterpal S, Lisabeth L, Fritsche LG, Mukherjee B. Salvatore M, et al. medRxiv [Preprint]. 2021 Feb 20:2020.06.29.20141564. doi: 10.1101/2020.06.29.20141564. medRxiv. 2021. Update in: J Clin Med. 2021 Mar 25;10(7):1351. doi: 10.3390/jcm10071351. PMID: 32793923 Free PMC article. Updated. Preprint.

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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

Affiliations

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

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

Update of

References

Grants and funding

LinkOut - more resources

Full Text Sources

Other Literature Sources

Medical