Effect Modification by Social Determinants of Pharmacogenetic Medication Interactions on 90-Day Hospital Readmissions within an Integrated U.S. Healthcare System
The present study builds on our prior work that demonstrated an association between pharmacogenetic interactions and 90-day readmission. In a substantially larger, more diverse study population of 19,999 adults tracked from 2010 through 2020 who underwent testing with a 13-gene pharmacogenetic panel...
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Veröffentlicht in: | Journal of personalized medicine 2022-07, Vol.12 (7), p.1145 |
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creator | Saulsberry, Loren Singh, Lavisha Pruitt, Jaclyn Ward, Christopher Wake, Dyson T Gibbons, Robert D Meltzer, David O O'Donnell, Peter H Cruz-Knight, Wanda Hulick, Peter J Dunnenberger, Henry M David, Sean P |
description | The present study builds on our prior work that demonstrated an association between pharmacogenetic interactions and 90-day readmission. In a substantially larger, more diverse study population of 19,999 adults tracked from 2010 through 2020 who underwent testing with a 13-gene pharmacogenetic panel, we included additional covariates to evaluate aggregate contribution of social determinants and medical comorbidity with the presence of identified gene-x-drug interactions to moderate 90-day hospital readmission (primary outcome). Univariate logistic regression analyses demonstrated that strongest associations with 90 day hospital readmissions were the number of medications prescribed within 30 days of a first hospital admission that had Clinical Pharmacogenomics Implementation Consortium (CPIC) guidance (CPIC medications) (5+ CPIC medications, odds ratio (OR) = 7.66, 95% confidence interval 5.45−10.77) (p < 0.0001), major comorbidities (5+ comorbidities, OR 3.36, 2.61−4.32) (p < 0.0001), age (65 + years, OR = 2.35, 1.77−3.12) (p < 0.0001), unemployment (OR = 2.19, 1.88−2.64) (p < 0.0001), Black/African-American race (OR 2.12, 1.47−3.07) (p < 0.0001), median household income (OR = 1.63, 1.03−2.58) (p = 0.035), male gender (OR = 1.47, 1.21−1.80) (p = 0.0001), and one or more gene-x-drug interaction (defined as a prescribed CPIC medication for a patient with a corresponding actionable pharmacogenetic variant) (OR = 1.41, 1.18−1.70). Health insurance was not associated with risk of 90-day readmission. Race, income, employment status, and gene-x-drug interactions were robust in a multivariable logistic regression model. The odds of 90-day readmission for patients with one or more identified gene-x-drug interactions after adjustment for these covariates was attenuated by 10% (OR = 1.31, 1.08−1.59) (p = 0.006). Although the interaction between race and gene-x-drug interactions was not statistically significant, White patients were more likely to have a gene-x-drug interaction (35.2%) than Black/African-American patients (25.9%) who were not readmitted (p < 0.0001). These results highlight the major contribution of social determinants and medical complexity to risk for hospital readmission, and that these determinants may modify the effect of gene-x-drug interactions on rehospitalization risk. |
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In a substantially larger, more diverse study population of 19,999 adults tracked from 2010 through 2020 who underwent testing with a 13-gene pharmacogenetic panel, we included additional covariates to evaluate aggregate contribution of social determinants and medical comorbidity with the presence of identified gene-x-drug interactions to moderate 90-day hospital readmission (primary outcome). Univariate logistic regression analyses demonstrated that strongest associations with 90 day hospital readmissions were the number of medications prescribed within 30 days of a first hospital admission that had Clinical Pharmacogenomics Implementation Consortium (CPIC) guidance (CPIC medications) (5+ CPIC medications, odds ratio (OR) = 7.66, 95% confidence interval 5.45−10.77) (p < 0.0001), major comorbidities (5+ comorbidities, OR 3.36, 2.61−4.32) (p < 0.0001), age (65 + years, OR = 2.35, 1.77−3.12) (p < 0.0001), unemployment (OR = 2.19, 1.88−2.64) (p < 0.0001), Black/African-American race (OR 2.12, 1.47−3.07) (p < 0.0001), median household income (OR = 1.63, 1.03−2.58) (p = 0.035), male gender (OR = 1.47, 1.21−1.80) (p = 0.0001), and one or more gene-x-drug interaction (defined as a prescribed CPIC medication for a patient with a corresponding actionable pharmacogenetic variant) (OR = 1.41, 1.18−1.70). Health insurance was not associated with risk of 90-day readmission. Race, income, employment status, and gene-x-drug interactions were robust in a multivariable logistic regression model. The odds of 90-day readmission for patients with one or more identified gene-x-drug interactions after adjustment for these covariates was attenuated by 10% (OR = 1.31, 1.08−1.59) (p = 0.006). Although the interaction between race and gene-x-drug interactions was not statistically significant, White patients were more likely to have a gene-x-drug interaction (35.2%) than Black/African-American patients (25.9%) who were not readmitted (p < 0.0001). These results highlight the major contribution of social determinants and medical complexity to risk for hospital readmission, and that these determinants may modify the effect of gene-x-drug interactions on rehospitalization risk.]]></description><identifier>ISSN: 2075-4426</identifier><identifier>EISSN: 2075-4426</identifier><identifier>DOI: 10.3390/jpm12071145</identifier><identifier>PMID: 35887642</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Chronic illnesses ; Chronic obstructive pulmonary disease ; Comorbidity ; Consortia ; Coronaviruses ; COVID-19 ; Data collection ; Drug dosages ; Drug interaction ; Drug interactions ; Electronic health records ; Ethnicity ; Family income ; Genes ; Health care ; Health care policy ; Health disparities ; Hospitalization ; Hospitals ; Patient admissions ; Patients ; Pharmacogenomics ; Population studies ; Precision medicine ; Prescription drugs ; Sociodemographics ; Statistical analysis</subject><ispartof>Journal of personalized medicine, 2022-07, Vol.12 (7), p.1145</ispartof><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 by the authors. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c409t-222fcc5d1f5413ab6c8ce59dc483ca038c42d05eb3ff9b4149031a97d5b20cfe3</citedby><cites>FETCH-LOGICAL-c409t-222fcc5d1f5413ab6c8ce59dc483ca038c42d05eb3ff9b4149031a97d5b20cfe3</cites><orcidid>0000-0002-6211-1713 ; 0000-0001-8397-4078 ; 0000-0003-0325-2329 ; 0000-0002-4922-2603</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319564/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319564/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35887642$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Saulsberry, Loren</creatorcontrib><creatorcontrib>Singh, Lavisha</creatorcontrib><creatorcontrib>Pruitt, Jaclyn</creatorcontrib><creatorcontrib>Ward, Christopher</creatorcontrib><creatorcontrib>Wake, Dyson T</creatorcontrib><creatorcontrib>Gibbons, Robert D</creatorcontrib><creatorcontrib>Meltzer, David O</creatorcontrib><creatorcontrib>O'Donnell, Peter H</creatorcontrib><creatorcontrib>Cruz-Knight, Wanda</creatorcontrib><creatorcontrib>Hulick, Peter J</creatorcontrib><creatorcontrib>Dunnenberger, Henry M</creatorcontrib><creatorcontrib>David, Sean P</creatorcontrib><title>Effect Modification by Social Determinants of Pharmacogenetic Medication Interactions on 90-Day Hospital Readmissions within an Integrated U.S. Healthcare System</title><title>Journal of personalized medicine</title><addtitle>J Pers Med</addtitle><description><![CDATA[The present study builds on our prior work that demonstrated an association between pharmacogenetic interactions and 90-day readmission. In a substantially larger, more diverse study population of 19,999 adults tracked from 2010 through 2020 who underwent testing with a 13-gene pharmacogenetic panel, we included additional covariates to evaluate aggregate contribution of social determinants and medical comorbidity with the presence of identified gene-x-drug interactions to moderate 90-day hospital readmission (primary outcome). Univariate logistic regression analyses demonstrated that strongest associations with 90 day hospital readmissions were the number of medications prescribed within 30 days of a first hospital admission that had Clinical Pharmacogenomics Implementation Consortium (CPIC) guidance (CPIC medications) (5+ CPIC medications, odds ratio (OR) = 7.66, 95% confidence interval 5.45−10.77) (p < 0.0001), major comorbidities (5+ comorbidities, OR 3.36, 2.61−4.32) (p < 0.0001), age (65 + years, OR = 2.35, 1.77−3.12) (p < 0.0001), unemployment (OR = 2.19, 1.88−2.64) (p < 0.0001), Black/African-American race (OR 2.12, 1.47−3.07) (p < 0.0001), median household income (OR = 1.63, 1.03−2.58) (p = 0.035), male gender (OR = 1.47, 1.21−1.80) (p = 0.0001), and one or more gene-x-drug interaction (defined as a prescribed CPIC medication for a patient with a corresponding actionable pharmacogenetic variant) (OR = 1.41, 1.18−1.70). Health insurance was not associated with risk of 90-day readmission. Race, income, employment status, and gene-x-drug interactions were robust in a multivariable logistic regression model. The odds of 90-day readmission for patients with one or more identified gene-x-drug interactions after adjustment for these covariates was attenuated by 10% (OR = 1.31, 1.08−1.59) (p = 0.006). Although the interaction between race and gene-x-drug interactions was not statistically significant, White patients were more likely to have a gene-x-drug interaction (35.2%) than Black/African-American patients (25.9%) who were not readmitted (p < 0.0001). These results highlight the major contribution of social determinants and medical complexity to risk for hospital readmission, and that these determinants may modify the effect of gene-x-drug interactions on rehospitalization risk.]]></description><subject>Chronic illnesses</subject><subject>Chronic obstructive pulmonary disease</subject><subject>Comorbidity</subject><subject>Consortia</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Data collection</subject><subject>Drug dosages</subject><subject>Drug interaction</subject><subject>Drug interactions</subject><subject>Electronic health records</subject><subject>Ethnicity</subject><subject>Family income</subject><subject>Genes</subject><subject>Health care</subject><subject>Health care policy</subject><subject>Health disparities</subject><subject>Hospitalization</subject><subject>Hospitals</subject><subject>Patient admissions</subject><subject>Patients</subject><subject>Pharmacogenomics</subject><subject>Population studies</subject><subject>Precision medicine</subject><subject>Prescription drugs</subject><subject>Sociodemographics</subject><subject>Statistical analysis</subject><issn>2075-4426</issn><issn>2075-4426</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNpVkU9rGzEQxZeS0oQ0p96LoMewrv6uV5dCSdI6kNBSN-dlVjuyZbySI8kp_jj9plXqJDhzmQfzmzcDr6o-MDoRQtPPq83IOJ0yJtWb6qQoVUvJm6MDfVydpbSipVrFeUPfVcdCte20kfyk-ntlLZpMbsPgrDOQXfCk35F5MA7W5BIzxtF58DmRYMnPJcQRTFigx-wMucXheenaFxTMoy6oJ5rWl7Ajs5A2LherXwjD6FL6P__j8tJ5Avu1RYSMA7mbzCdkhrDOSwMRyXyXMo7vq7cW1gnPnvppdfft6vfFrL758f364utNbSTVueacW2PUwKySTEDfmNag0oORrTBARWskH6jCXlire8mkpoKBng6q59RYFKfVl73vZtuPOBj0OcK620Q3Qtx1AVz3euLdsluEh04LplUji8GnJ4MY7reYcrcK2-jLzx1vtKRMTQUr1PmeMjGkFNG-XGC0e4y0O4i00B8Pn3phnwMU_wCsXZ_s</recordid><startdate>20220715</startdate><enddate>20220715</enddate><creator>Saulsberry, Loren</creator><creator>Singh, Lavisha</creator><creator>Pruitt, Jaclyn</creator><creator>Ward, Christopher</creator><creator>Wake, Dyson T</creator><creator>Gibbons, Robert D</creator><creator>Meltzer, David O</creator><creator>O'Donnell, Peter H</creator><creator>Cruz-Knight, Wanda</creator><creator>Hulick, Peter J</creator><creator>Dunnenberger, Henry M</creator><creator>David, Sean P</creator><general>MDPI AG</general><general>MDPI</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FH</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>LK8</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-6211-1713</orcidid><orcidid>https://orcid.org/0000-0001-8397-4078</orcidid><orcidid>https://orcid.org/0000-0003-0325-2329</orcidid><orcidid>https://orcid.org/0000-0002-4922-2603</orcidid></search><sort><creationdate>20220715</creationdate><title>Effect Modification by Social Determinants of Pharmacogenetic Medication Interactions on 90-Day Hospital Readmissions within an Integrated U.S. Healthcare System</title><author>Saulsberry, Loren ; Singh, Lavisha ; Pruitt, Jaclyn ; Ward, Christopher ; Wake, Dyson T ; Gibbons, Robert D ; Meltzer, David O ; O'Donnell, Peter H ; Cruz-Knight, Wanda ; Hulick, Peter J ; Dunnenberger, Henry M ; David, Sean P</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c409t-222fcc5d1f5413ab6c8ce59dc483ca038c42d05eb3ff9b4149031a97d5b20cfe3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Chronic illnesses</topic><topic>Chronic obstructive pulmonary disease</topic><topic>Comorbidity</topic><topic>Consortia</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>Data collection</topic><topic>Drug dosages</topic><topic>Drug interaction</topic><topic>Drug interactions</topic><topic>Electronic health records</topic><topic>Ethnicity</topic><topic>Family income</topic><topic>Genes</topic><topic>Health care</topic><topic>Health care policy</topic><topic>Health disparities</topic><topic>Hospitalization</topic><topic>Hospitals</topic><topic>Patient admissions</topic><topic>Patients</topic><topic>Pharmacogenomics</topic><topic>Population studies</topic><topic>Precision medicine</topic><topic>Prescription drugs</topic><topic>Sociodemographics</topic><topic>Statistical analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Saulsberry, Loren</creatorcontrib><creatorcontrib>Singh, Lavisha</creatorcontrib><creatorcontrib>Pruitt, Jaclyn</creatorcontrib><creatorcontrib>Ward, Christopher</creatorcontrib><creatorcontrib>Wake, Dyson T</creatorcontrib><creatorcontrib>Gibbons, Robert D</creatorcontrib><creatorcontrib>Meltzer, David O</creatorcontrib><creatorcontrib>O'Donnell, Peter H</creatorcontrib><creatorcontrib>Cruz-Knight, Wanda</creatorcontrib><creatorcontrib>Hulick, Peter J</creatorcontrib><creatorcontrib>Dunnenberger, Henry M</creatorcontrib><creatorcontrib>David, Sean P</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Biological Science Database</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of personalized medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Saulsberry, Loren</au><au>Singh, Lavisha</au><au>Pruitt, Jaclyn</au><au>Ward, Christopher</au><au>Wake, Dyson T</au><au>Gibbons, Robert D</au><au>Meltzer, David O</au><au>O'Donnell, Peter H</au><au>Cruz-Knight, Wanda</au><au>Hulick, Peter J</au><au>Dunnenberger, Henry M</au><au>David, Sean P</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Effect Modification by Social Determinants of Pharmacogenetic Medication Interactions on 90-Day Hospital Readmissions within an Integrated U.S. Healthcare System</atitle><jtitle>Journal of personalized medicine</jtitle><addtitle>J Pers Med</addtitle><date>2022-07-15</date><risdate>2022</risdate><volume>12</volume><issue>7</issue><spage>1145</spage><pages>1145-</pages><issn>2075-4426</issn><eissn>2075-4426</eissn><abstract><![CDATA[The present study builds on our prior work that demonstrated an association between pharmacogenetic interactions and 90-day readmission. In a substantially larger, more diverse study population of 19,999 adults tracked from 2010 through 2020 who underwent testing with a 13-gene pharmacogenetic panel, we included additional covariates to evaluate aggregate contribution of social determinants and medical comorbidity with the presence of identified gene-x-drug interactions to moderate 90-day hospital readmission (primary outcome). Univariate logistic regression analyses demonstrated that strongest associations with 90 day hospital readmissions were the number of medications prescribed within 30 days of a first hospital admission that had Clinical Pharmacogenomics Implementation Consortium (CPIC) guidance (CPIC medications) (5+ CPIC medications, odds ratio (OR) = 7.66, 95% confidence interval 5.45−10.77) (p < 0.0001), major comorbidities (5+ comorbidities, OR 3.36, 2.61−4.32) (p < 0.0001), age (65 + years, OR = 2.35, 1.77−3.12) (p < 0.0001), unemployment (OR = 2.19, 1.88−2.64) (p < 0.0001), Black/African-American race (OR 2.12, 1.47−3.07) (p < 0.0001), median household income (OR = 1.63, 1.03−2.58) (p = 0.035), male gender (OR = 1.47, 1.21−1.80) (p = 0.0001), and one or more gene-x-drug interaction (defined as a prescribed CPIC medication for a patient with a corresponding actionable pharmacogenetic variant) (OR = 1.41, 1.18−1.70). Health insurance was not associated with risk of 90-day readmission. Race, income, employment status, and gene-x-drug interactions were robust in a multivariable logistic regression model. The odds of 90-day readmission for patients with one or more identified gene-x-drug interactions after adjustment for these covariates was attenuated by 10% (OR = 1.31, 1.08−1.59) (p = 0.006). Although the interaction between race and gene-x-drug interactions was not statistically significant, White patients were more likely to have a gene-x-drug interaction (35.2%) than Black/African-American patients (25.9%) who were not readmitted (p < 0.0001). These results highlight the major contribution of social determinants and medical complexity to risk for hospital readmission, and that these determinants may modify the effect of gene-x-drug interactions on rehospitalization risk.]]></abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>35887642</pmid><doi>10.3390/jpm12071145</doi><orcidid>https://orcid.org/0000-0002-6211-1713</orcidid><orcidid>https://orcid.org/0000-0001-8397-4078</orcidid><orcidid>https://orcid.org/0000-0003-0325-2329</orcidid><orcidid>https://orcid.org/0000-0002-4922-2603</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Chronic illnesses Chronic obstructive pulmonary disease Comorbidity Consortia Coronaviruses COVID-19 Data collection Drug dosages Drug interaction Drug interactions Electronic health records Ethnicity Family income Genes Health care Health care policy Health disparities Hospitalization Hospitals Patient admissions Patients Pharmacogenomics Population studies Precision medicine Prescription drugs Sociodemographics Statistical analysis |
title | Effect Modification by Social Determinants of Pharmacogenetic Medication Interactions on 90-Day Hospital Readmissions within an Integrated U.S. Healthcare System |
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