{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T18:21:31Z","timestamp":1788286891359,"version":"build-2803163510"},"reference-count":50,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2020,11,22]],"date-time":"2020-11-22T00:00:00Z","timestamp":1606003200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["ascpt.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Translational Sci"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Many academic institutions are collecting blood samples from patients seeking treatment for coronavirus disease 2019 (COVID\u201019) to build research biorepositories. It may be feasible to extract pharmacogenomic (PGx) information from biorepositories for clinical use. We sought to characterize the potential value of multigene PGx testing among individuals hospitalized with COVID\u201019 in the United States. We performed a cross\u2010sectional analysis of electronic health records from consecutive individuals hospitalized with COVID\u201019 at a large, urban academic health system. We characterized medication orders, focusing on medications with actionable PGx guidance related to 14 commonly assayed genes (<jats:italic>CYP2C19<\/jats:italic>, <jats:italic>CYP2C9<\/jats:italic>, <jats:italic>CYP2D6<\/jats:italic>, <jats:italic>CYP3A5<\/jats:italic>, <jats:italic>DPYD<\/jats:italic>, <jats:italic>G6PD<\/jats:italic>, <jats:italic>HLA\u2010A<\/jats:italic>, <jats:italic>HLA\u2010B<\/jats:italic>, <jats:italic>IFNL3<\/jats:italic>, <jats:italic>NUDT15<\/jats:italic>, <jats:italic>SLCO1B1<\/jats:italic>, <jats:italic>TPMT<\/jats:italic>, <jats:italic>UGT1A1<\/jats:italic>, and <jats:italic>VKORC1<\/jats:italic>). A simulation analysis combined medication data with population phenotype frequencies to estimate how many treatment modifications would be enabled if multigene PGx results were available. Sixty\u2010four unique medications with PGx guidance were ordered at least once in the cohort (<jats:italic>n<\/jats:italic>\u00a0=\u00a01,852, mean age 60.1\u00a0years). Nearly nine in 10 individuals (89.7%) had at least one order for a medication with PGx guidance and 427 patients (23.1%) had orders for 4 or more actionable medications. Using a simulation, we estimated that 17 treatment modifications per 100 patients would be enabled if PGx results were available. The genes <jats:italic>CYP2D6<\/jats:italic> and <jats:italic>CYP2C19<\/jats:italic> were responsible for the majority of treatment modifications, and the medications most often affected were ondansetron, oxycodone, and clopidogrel. PGx results would be relevant for nearly all individuals hospitalized with COVID\u201019 and would provide the opportunity to improve clinical care.<\/jats:p>","DOI":"10.1111\/cts.12919","type":"journal-article","created":{"date-parts":[[2020,10,21]],"date-time":"2020-10-21T11:47:21Z","timestamp":1603280841000},"page":"153-162","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Projected Utility of Pharmacogenomic Testing Among Individuals Hospitalized With COVID\u201019: A Retrospective Multicenter Study in the United States"],"prefix":"10.1111","volume":"14","author":[{"given":"James M.","family":"Stevenson","sequence":"first","affiliation":[{"name":"Division of Clinical Pharmacology Johns Hopkins University School of Medicine  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G. Caleb","family":"Alexander","sequence":"additional","affiliation":[{"name":"Center for Drug Safety and Effectiveness Johns Hopkins Bloomberg School of Public Health  Baltimore Maryland USA"},{"name":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health  Baltimore Maryland USA"},{"name":"Division of General Internal Medicine Johns Hopkins University School of Medicine  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natasha","family":"Palamuttam","sequence":"additional","affiliation":[{"name":"Division of Health Sciences Informatics Johns Hopkins University School of Medicine  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hemalkumar B.","family":"Mehta","sequence":"additional","affiliation":[{"name":"Center for Drug Safety and Effectiveness Johns Hopkins Bloomberg School of Public Health  Baltimore Maryland USA"},{"name":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,11,22]]},"reference":[{"key":"e_1_2_10_1_1","unstructured":"United States Food and Drug Administration.Table of pharmacogenomic biomarkers in drug labeling<http:\/\/www.fda.gov\/drugs\/scienceresearch\/researchareas\/pharmacogenetics\/ucm083378.htm> (2020). 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