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No Association Between Pharmacogenomics Variants and Hospital and Emergency Department Utilization: A Mayo Clinic Biobank Retrospective Study

Authors Takahashi PY, Ryu E, Bielinski SJ, Hathcock M, Jenkins GD, Cerhan JR, Olson JE

Received 12 September 2020

Accepted for publication 29 December 2020

Published 11 February 2021 Volume 2021:14 Pages 229—237

DOI https://doi.org/10.2147/PGPM.S281645

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 3

Editor who approved publication: Dr Martin Bluth


Paul Y Takahashi,1 Euijung Ryu,2 Suzette J Bielinski,3 Matthew Hathcock,2 Gregory D Jenkins,2 James R Cerhan,3 Janet E Olson3

1Division of Community Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA; 2Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA; 3Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA

Correspondence: Paul Y Takahashi
Division of Community Internal Medicine, Department of Internal Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
Tel +1-507-284-2511
Fax +1-507-266-2297
Email Takahashi.paul@mayo.edu

Background: The use of pharmacogenomics data is increasing in clinical practice. However, it is unknown if pharmacogenomics data can be used more broadly to predict outcomes like hospitalization or emergency department (ED) visit. We aim to determine the association between selected pharmacogenomics phenotypes and hospital utilization outcomes (hospitalization and ED visits).
Methods: This cohort study utilized 10,078 patients from the Mayo Clinic Biobank in the RIGHT protocol with sequence and interpreted phenotypes for 10 selected pharmacogenes including CYP2D6, CYP2C9, CYP2C19, CYP3A5, HLA B 5701, HLA B 5702, HLA B 5801, TPMT, SLCO1B1, and DPYD. The primary outcome was hospitalization with ED visits as a secondary outcome. We used Cox proportional hazards model to test the association between each pharmacogenomics phenotype and the risk of the outcomes.
Results: During the follow-up period (median [in years] = 7.3), 13% (n=1354) and 8% (n=813) of the subjects experienced hospitalization and ED visits, respectively. Compared to subjects who did not experience hospitalization, hospitalized patients were older (median age [in years]: 67 vs 65), poorer self-rated health (15% vs 4.7% for fair/poor), and higher disease burden (median number of chronic conditions: 7 vs 4) at baseline. There was no association of hospitalization with any of the pharmacogenomics phenotypes. The pharmacogenomics phenotypes were not associated with disease burden, a well-established risk factor for hospital utilization outcomes. Similar findings were observed for patients with ED visits during the follow-up period.
Conclusion: We found no association of 10 well-established pharmacogenomics phenotypes with either hospitalization or ED visits in this relatively large biobank population and outside the context of specific drug use related to these genes. Traditional risk factors for hospitalization like age and self-rated health were much more likely to predict hospitalization and/or ED visits than this pharmacogenomics information.

Keywords: pharmacogenomics, emergency department, hospitalization

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