Designing and validating a Markov model for hospital-based addiction consult service impact on 12-month drug and non-drug related mortality
Addiction consult services (ACS) engage hospitalized patients with opioid use disorder (OUD) in care and help meet their goals for substance use treatment. Little is known about how ACS affect mortality for patients with OUD. The objective of this study was to design and validate a model that estima...
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| creator | King, Caroline A Englander, Honora Korthuis, P Todd Barocas, Joshua A McConnell, K John Morris, Cynthia D Cook, Ryan |
| description | Addiction consult services (ACS) engage hospitalized patients with opioid use disorder (OUD) in care and help meet their goals for substance use treatment. Little is known about how ACS affect mortality for patients with OUD. The objective of this study was to design and validate a model that estimates the impact of ACS care on 12-month mortality among hospitalized patients with OUD.
We developed a Markov model of referral to an ACS, post-discharge engagement in SUD care, and 12-month drug-related and non-drug related mortality among hospitalized patients with OUD. We populated our model using Oregon Medicaid data and validated it using international modeling standards.
There were 6,654 patients with OUD hospitalized from April 2015 through December 2017. There were 114 (1.7%) drug-related deaths and 408 (6.1%) non-drug related deaths at 12 months. Bayesian logistic regression models estimated four percent (4%, 95% CI = 2%, 6%) of patients were referred to an ACS. Of those, 47% (95% CI = 37%, 57%) engaged in post-discharge OUD care, versus 20% not referred to an ACS (95% CI = 16%, 24%). The risk of drug-related death at 12 months among patients in post-discharge OUD care was 3% (95% CI = 0%, 7%) versus 6% not in care (95% CI = 2%, 10%). The risk of non-drug related death was 7% (95% CI = 1%, 13%) among patients in post-discharge OUD treatment, versus 9% not in care (95% CI = 5%, 13%). We validated our model by evaluating its predictive, external, internal, face and cross validity.
Our novel Markov model reflects trajectories of care and survival for patients hospitalized with OUD. This model can be used to evaluate the impact of other clinical and policy changes to improve patient survival. |
| doi_str_mv | 10.1371/journal.pone.0256793 |
| format | Article |
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We developed a Markov model of referral to an ACS, post-discharge engagement in SUD care, and 12-month drug-related and non-drug related mortality among hospitalized patients with OUD. We populated our model using Oregon Medicaid data and validated it using international modeling standards.
There were 6,654 patients with OUD hospitalized from April 2015 through December 2017. There were 114 (1.7%) drug-related deaths and 408 (6.1%) non-drug related deaths at 12 months. Bayesian logistic regression models estimated four percent (4%, 95% CI = 2%, 6%) of patients were referred to an ACS. Of those, 47% (95% CI = 37%, 57%) engaged in post-discharge OUD care, versus 20% not referred to an ACS (95% CI = 16%, 24%). The risk of drug-related death at 12 months among patients in post-discharge OUD care was 3% (95% CI = 0%, 7%) versus 6% not in care (95% CI = 2%, 10%). The risk of non-drug related death was 7% (95% CI = 1%, 13%) among patients in post-discharge OUD treatment, versus 9% not in care (95% CI = 5%, 13%). We validated our model by evaluating its predictive, external, internal, face and cross validity.
Our novel Markov model reflects trajectories of care and survival for patients hospitalized with OUD. This model can be used to evaluate the impact of other clinical and policy changes to improve patient survival.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0256793</identifier><identifier>PMID: 34506517</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Addictions ; Adult ; Alcohol ; Analysis ; Bayesian analysis ; Biology and Life Sciences ; Care and treatment ; Cohort Studies ; Datasets ; Death & dying ; Discharge ; Drug abuse ; Drug addiction ; Drug overdose ; Drug use ; Drug withdrawal ; Drugs ; Estimates ; Fatalities ; Female ; Government programs ; Harm reduction ; Health aspects ; Health risks ; Hospital patients ; Hospitalization ; Hospitals ; Humans ; Infectious diseases ; International standardization ; Male ; Markov Chains ; Markov processes ; Mathematical models ; Medicaid ; Medicine ; Medicine and Health Sciences ; Methadone ; Middle Aged ; Modelling ; Mortality ; Narcotics ; Opiates ; Opioid-Related Disorders - mortality ; Opioids ; Oregon - epidemiology ; Patients ; People and places ; Physical Sciences ; Regression analysis ; Regression models ; Research and Analysis Methods ; Social Sciences ; Social workers ; Substance abuse ; Substance abuse treatment ; Substance use ; Survival</subject><ispartof>PloS one, 2021-09, Vol.16 (9), p.e0256793</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 King et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2021 King et al 2021 King et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-60d28ef44aa70bfba90b2b771d4c42fc6e2b61bb555d3b6da8ae83556dbb46c13</citedby><cites>FETCH-LOGICAL-c692t-60d28ef44aa70bfba90b2b771d4c42fc6e2b61bb555d3b6da8ae83556dbb46c13</cites><orcidid>0000-0003-1863-2041 ; 0000-0002-9913-6340</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/PMC8432751/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8432751/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27321,27901,27902,33751,53766,53768,79343,79344</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34506517$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>King, Caroline A</creatorcontrib><creatorcontrib>Englander, Honora</creatorcontrib><creatorcontrib>Korthuis, P Todd</creatorcontrib><creatorcontrib>Barocas, Joshua A</creatorcontrib><creatorcontrib>McConnell, K John</creatorcontrib><creatorcontrib>Morris, Cynthia D</creatorcontrib><creatorcontrib>Cook, Ryan</creatorcontrib><title>Designing and validating a Markov model for hospital-based addiction consult service impact on 12-month drug and non-drug related mortality</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Addiction consult services (ACS) engage hospitalized patients with opioid use disorder (OUD) in care and help meet their goals for substance use treatment. Little is known about how ACS affect mortality for patients with OUD. The objective of this study was to design and validate a model that estimates the impact of ACS care on 12-month mortality among hospitalized patients with OUD.
We developed a Markov model of referral to an ACS, post-discharge engagement in SUD care, and 12-month drug-related and non-drug related mortality among hospitalized patients with OUD. We populated our model using Oregon Medicaid data and validated it using international modeling standards.
There were 6,654 patients with OUD hospitalized from April 2015 through December 2017. There were 114 (1.7%) drug-related deaths and 408 (6.1%) non-drug related deaths at 12 months. Bayesian logistic regression models estimated four percent (4%, 95% CI = 2%, 6%) of patients were referred to an ACS. Of those, 47% (95% CI = 37%, 57%) engaged in post-discharge OUD care, versus 20% not referred to an ACS (95% CI = 16%, 24%). The risk of drug-related death at 12 months among patients in post-discharge OUD care was 3% (95% CI = 0%, 7%) versus 6% not in care (95% CI = 2%, 10%). The risk of non-drug related death was 7% (95% CI = 1%, 13%) among patients in post-discharge OUD treatment, versus 9% not in care (95% CI = 5%, 13%). We validated our model by evaluating its predictive, external, internal, face and cross validity.
Our novel Markov model reflects trajectories of care and survival for patients hospitalized with OUD. This model can be used to evaluate the impact of other clinical and policy changes to improve patient survival.</description><subject>Addictions</subject><subject>Adult</subject><subject>Alcohol</subject><subject>Analysis</subject><subject>Bayesian analysis</subject><subject>Biology and Life Sciences</subject><subject>Care and treatment</subject><subject>Cohort Studies</subject><subject>Datasets</subject><subject>Death & dying</subject><subject>Discharge</subject><subject>Drug abuse</subject><subject>Drug addiction</subject><subject>Drug overdose</subject><subject>Drug use</subject><subject>Drug withdrawal</subject><subject>Drugs</subject><subject>Estimates</subject><subject>Fatalities</subject><subject>Female</subject><subject>Government programs</subject><subject>Harm reduction</subject><subject>Health aspects</subject><subject>Health risks</subject><subject>Hospital patients</subject><subject>Hospitalization</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Infectious diseases</subject><subject>International standardization</subject><subject>Male</subject><subject>Markov Chains</subject><subject>Markov processes</subject><subject>Mathematical models</subject><subject>Medicaid</subject><subject>Medicine</subject><subject>Medicine and Health Sciences</subject><subject>Methadone</subject><subject>Middle Aged</subject><subject>Modelling</subject><subject>Mortality</subject><subject>Narcotics</subject><subject>Opiates</subject><subject>Opioid-Related Disorders - mortality</subject><subject>Opioids</subject><subject>Oregon - epidemiology</subject><subject>Patients</subject><subject>People and places</subject><subject>Physical Sciences</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Research and Analysis Methods</subject><subject>Social Sciences</subject><subject>Social 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and validating a Markov model for hospital-based addiction consult service impact on 12-month drug and non-drug related mortality</title><author>King, Caroline A ; Englander, Honora ; Korthuis, P Todd ; Barocas, Joshua A ; McConnell, K John ; Morris, Cynthia D ; Cook, Ryan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-60d28ef44aa70bfba90b2b771d4c42fc6e2b61bb555d3b6da8ae83556dbb46c13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Addictions</topic><topic>Adult</topic><topic>Alcohol</topic><topic>Analysis</topic><topic>Bayesian analysis</topic><topic>Biology and Life Sciences</topic><topic>Care and treatment</topic><topic>Cohort Studies</topic><topic>Datasets</topic><topic>Death & dying</topic><topic>Discharge</topic><topic>Drug abuse</topic><topic>Drug addiction</topic><topic>Drug overdose</topic><topic>Drug use</topic><topic>Drug 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One</addtitle><date>2021-09-10</date><risdate>2021</risdate><volume>16</volume><issue>9</issue><spage>e0256793</spage><pages>e0256793-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Addiction consult services (ACS) engage hospitalized patients with opioid use disorder (OUD) in care and help meet their goals for substance use treatment. Little is known about how ACS affect mortality for patients with OUD. The objective of this study was to design and validate a model that estimates the impact of ACS care on 12-month mortality among hospitalized patients with OUD.
We developed a Markov model of referral to an ACS, post-discharge engagement in SUD care, and 12-month drug-related and non-drug related mortality among hospitalized patients with OUD. We populated our model using Oregon Medicaid data and validated it using international modeling standards.
There were 6,654 patients with OUD hospitalized from April 2015 through December 2017. There were 114 (1.7%) drug-related deaths and 408 (6.1%) non-drug related deaths at 12 months. Bayesian logistic regression models estimated four percent (4%, 95% CI = 2%, 6%) of patients were referred to an ACS. Of those, 47% (95% CI = 37%, 57%) engaged in post-discharge OUD care, versus 20% not referred to an ACS (95% CI = 16%, 24%). The risk of drug-related death at 12 months among patients in post-discharge OUD care was 3% (95% CI = 0%, 7%) versus 6% not in care (95% CI = 2%, 10%). The risk of non-drug related death was 7% (95% CI = 1%, 13%) among patients in post-discharge OUD treatment, versus 9% not in care (95% CI = 5%, 13%). We validated our model by evaluating its predictive, external, internal, face and cross validity.
Our novel Markov model reflects trajectories of care and survival for patients hospitalized with OUD. This model can be used to evaluate the impact of other clinical and policy changes to improve patient survival.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>34506517</pmid><doi>10.1371/journal.pone.0256793</doi><tpages>e0256793</tpages><orcidid>https://orcid.org/0000-0003-1863-2041</orcidid><orcidid>https://orcid.org/0000-0002-9913-6340</orcidid><oa>free_for_read</oa></addata></record> |
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| language | eng |
| recordid | cdi_plos_journals_2571446241 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry; Sociological Abstracts; Public Library of Science (PLoS) |
| subjects | Addictions Adult Alcohol Analysis Bayesian analysis Biology and Life Sciences Care and treatment Cohort Studies Datasets Death & dying Discharge Drug abuse Drug addiction Drug overdose Drug use Drug withdrawal Drugs Estimates Fatalities Female Government programs Harm reduction Health aspects Health risks Hospital patients Hospitalization Hospitals Humans Infectious diseases International standardization Male Markov Chains Markov processes Mathematical models Medicaid Medicine Medicine and Health Sciences Methadone Middle Aged Modelling Mortality Narcotics Opiates Opioid-Related Disorders - mortality Opioids Oregon - epidemiology Patients People and places Physical Sciences Regression analysis Regression models Research and Analysis Methods Social Sciences Social workers Substance abuse Substance abuse treatment Substance use Survival |
| title | Designing and validating a Markov model for hospital-based addiction consult service impact on 12-month drug and non-drug related mortality |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-03T12%3A55%3A14IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Designing%20and%20validating%20a%20Markov%20model%20for%20hospital-based%20addiction%20consult%20service%20impact%20on%2012-month%20drug%20and%20non-drug%20related%20mortality&rft.jtitle=PloS%20one&rft.au=King,%20Caroline%20A&rft.date=2021-09-10&rft.volume=16&rft.issue=9&rft.spage=e0256793&rft.pages=e0256793-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0256793&rft_dat=%3Cgale_plos_%3EA674968980%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2571446241&rft_id=info:pmid/34506517&rft_galeid=A674968980&rft_doaj_id=oai_doaj_org_article_8bb92735eef24e0ebee815b169883242&rfr_iscdi=true |