Reducing age bias in decision analyses of anticoagulation for patients with nonvalvular atrial fibrillation - A microsimulation study
Anticoagulation decreases a patient's risk of ischemic stroke and increases the risk of hemorrhage. Decision analyses regarding anticoagulation therefore require that different outcomes be weighted in comparison to one another. Most decision analyses to date have weighted intracranial hemorrhag...
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description | Anticoagulation decreases a patient's risk of ischemic stroke and increases the risk of hemorrhage. Decision analyses regarding anticoagulation therefore require that different outcomes be weighted in comparison to one another. Most decision analyses to date have weighted intracranial hemorrhage (ICH) as 1.5 times worse than ischemic stroke, but because death and disability have lifelong impact, the expected impact should vary by life expectancy. Therefore, a fixed weighting ratio leads to age-related bias decision analyses of anticoagulation. We aimed to quantify the relative impact of ICH and ischemic stroke and derive a ratio that allows decision analysis without microsimulation.
We created a microsimulation model to predict QALYs lost due to ICH and ischemic stroke. We then applied a meta-model to predict the ratio of QALYs lost from ICH relative to ischemic stroke.
Previously-used weighting ratios (1.5) are close to our derived mean weighting ratio (1.60). However, the weighting ratio of QALYs lost from ICH relative to ischemic stroke is sensitive to age and discount rate. Patients at younger ages have higher mean weighting ratios, as do patients with higher discount rates.
The ratio of QALYs lost to ICH relative to ischemic stroke varies with age and discount rate. We present a set of such ratios here for use in decision analyses that do not incorporate full microsimulation models. Use of weighting ratios that vary with age, rather than the current fixed ratios, has the potential to reduce age-based bias in decision-making regarding events with lifelong implications. In this case, use of dynamic ratios may change anticoagulation recommendations for patients with nonvalvular atrial fibrillation at relatively low stroke risk. |
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We created a microsimulation model to predict QALYs lost due to ICH and ischemic stroke. We then applied a meta-model to predict the ratio of QALYs lost from ICH relative to ischemic stroke.
Previously-used weighting ratios (1.5) are close to our derived mean weighting ratio (1.60). However, the weighting ratio of QALYs lost from ICH relative to ischemic stroke is sensitive to age and discount rate. Patients at younger ages have higher mean weighting ratios, as do patients with higher discount rates.
The ratio of QALYs lost to ICH relative to ischemic stroke varies with age and discount rate. We present a set of such ratios here for use in decision analyses that do not incorporate full microsimulation models. Use of weighting ratios that vary with age, rather than the current fixed ratios, has the potential to reduce age-based bias in decision-making regarding events with lifelong implications. In this case, use of dynamic ratios may change anticoagulation recommendations for patients with nonvalvular atrial fibrillation at relatively low stroke risk.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0199593</identifier><identifier>PMID: 29995900</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adult ; Age ; Age Factors ; Aged ; Aged, 80 and over ; Anticoagulants ; Anticoagulants - pharmacology ; Anticoagulants - therapeutic use ; Atrial Fibrillation - blood ; Atrial Fibrillation - complications ; Atrial Fibrillation - epidemiology ; Bias ; Biology and Life Sciences ; Blood Coagulation - drug effects ; Cardiac arrhythmia ; Census of Population ; Complications and side effects ; Cost analysis ; Decision analysis ; Decision making ; Decision Support Techniques ; Disease control ; Dosage and administration ; Drug dosages ; Engineering and Technology ; Estimates ; Female ; Fibrillation ; Health risks ; Hemorrhage ; Hospital Mortality ; Hospitals ; Humans ; Internal medicine ; Intracranial Hemorrhages - etiology ; Ischemia ; Life expectancy ; Life span ; Male ; Mathematical models ; Medicine ; Medicine and Health Sciences ; Middle Aged ; Models, Cardiovascular ; Monte Carlo Method ; Monte Carlo simulation ; Mortality ; Nutrition ; Patients ; Prevention ; Quality-Adjusted Life Years ; Research and Analysis Methods ; Risk factors ; Severity of Illness Index ; Stroke ; Stroke - diagnosis ; Stroke - epidemiology ; Stroke - etiology ; Stroke - prevention & control ; Thrombolytic Therapy ; Weighting</subject><ispartof>PloS one, 2018-07, Vol.13 (7), p.e0199593</ispartof><rights>COPYRIGHT 2018 Public Library of Science</rights><rights>This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication: https://creativecommons.org/publicdomain/zero/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-c609edf3c13361052e92d9118080049fe1e35e155dfa46c0b6774ce36ffdaac23</citedby><cites>FETCH-LOGICAL-c692t-c609edf3c13361052e92d9118080049fe1e35e155dfa46c0b6774ce36ffdaac23</cites><orcidid>0000-0002-0353-1785</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/PMC6040745/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6040745/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29995900$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Salinas, Pablo</contributor><creatorcontrib>Pappas, Matthew A</creatorcontrib><creatorcontrib>Vijan, Sandeep</creatorcontrib><creatorcontrib>Rothberg, Michael B</creatorcontrib><creatorcontrib>Singer, Daniel E</creatorcontrib><title>Reducing age bias in decision analyses of anticoagulation for patients with nonvalvular atrial fibrillation - A microsimulation study</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Anticoagulation decreases a patient's risk of ischemic stroke and increases the risk of hemorrhage. Decision analyses regarding anticoagulation therefore require that different outcomes be weighted in comparison to one another. Most decision analyses to date have weighted intracranial hemorrhage (ICH) as 1.5 times worse than ischemic stroke, but because death and disability have lifelong impact, the expected impact should vary by life expectancy. Therefore, a fixed weighting ratio leads to age-related bias decision analyses of anticoagulation. We aimed to quantify the relative impact of ICH and ischemic stroke and derive a ratio that allows decision analysis without microsimulation.
We created a microsimulation model to predict QALYs lost due to ICH and ischemic stroke. We then applied a meta-model to predict the ratio of QALYs lost from ICH relative to ischemic stroke.
Previously-used weighting ratios (1.5) are close to our derived mean weighting ratio (1.60). However, the weighting ratio of QALYs lost from ICH relative to ischemic stroke is sensitive to age and discount rate. Patients at younger ages have higher mean weighting ratios, as do patients with higher discount rates.
The ratio of QALYs lost to ICH relative to ischemic stroke varies with age and discount rate. We present a set of such ratios here for use in decision analyses that do not incorporate full microsimulation models. Use of weighting ratios that vary with age, rather than the current fixed ratios, has the potential to reduce age-based bias in decision-making regarding events with lifelong implications. In this case, use of dynamic ratios may change anticoagulation recommendations for patients with nonvalvular atrial fibrillation at relatively low stroke risk.</description><subject>Adult</subject><subject>Age</subject><subject>Age Factors</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Anticoagulants</subject><subject>Anticoagulants - pharmacology</subject><subject>Anticoagulants - therapeutic use</subject><subject>Atrial Fibrillation - blood</subject><subject>Atrial Fibrillation - complications</subject><subject>Atrial Fibrillation - epidemiology</subject><subject>Bias</subject><subject>Biology and Life Sciences</subject><subject>Blood Coagulation - drug effects</subject><subject>Cardiac arrhythmia</subject><subject>Census of Population</subject><subject>Complications and side effects</subject><subject>Cost analysis</subject><subject>Decision analysis</subject><subject>Decision making</subject><subject>Decision Support Techniques</subject><subject>Disease control</subject><subject>Dosage and administration</subject><subject>Drug dosages</subject><subject>Engineering and Technology</subject><subject>Estimates</subject><subject>Female</subject><subject>Fibrillation</subject><subject>Health risks</subject><subject>Hemorrhage</subject><subject>Hospital Mortality</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Internal medicine</subject><subject>Intracranial Hemorrhages - etiology</subject><subject>Ischemia</subject><subject>Life expectancy</subject><subject>Life span</subject><subject>Male</subject><subject>Mathematical models</subject><subject>Medicine</subject><subject>Medicine and Health Sciences</subject><subject>Middle Aged</subject><subject>Models, Cardiovascular</subject><subject>Monte Carlo Method</subject><subject>Monte Carlo simulation</subject><subject>Mortality</subject><subject>Nutrition</subject><subject>Patients</subject><subject>Prevention</subject><subject>Quality-Adjusted Life Years</subject><subject>Research and Analysis Methods</subject><subject>Risk factors</subject><subject>Severity of Illness Index</subject><subject>Stroke</subject><subject>Stroke - diagnosis</subject><subject>Stroke - epidemiology</subject><subject>Stroke - etiology</subject><subject>Stroke - prevention & control</subject><subject>Thrombolytic Therapy</subject><subject>Weighting</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk2uL1DAUhoso7rr6D0QDguCHGXNrOv0iDIuXgYWF9fI1nObSydBpxqQdnR_g_zZ1OssUFKTQHpLnfRPenpNlzwmeE1aQtxvfhxaa-c63Zo5JWeYle5BdkpLRmaCYPTyrL7InMW4wztlCiMfZBS0HHOPL7Ned0b1ybY2gNqhyEJFrkTbKRedbBOmEQzQReZvqzikPdd9AN-xZH9AulabtIvrhujVqfbuHZp-AgKALDhpkXRVcMypmaIm2TgUf3fbkErteH55mjyw00Twbv1fZ1w_vv1x_mt3cflxdL29mSpS0S29cGm2ZIowJgnNqSqpLQhZ4gTEvrSGG5YbkubbAhcKVKAquDBPWagBF2VX28ui7a3yUY4JRUiwWjGPKi0SsjoT2sJG74LYQDtKDk38WfKglhJRDY2ShFkRUC64rXvG8okB1AbosCgW5KQxJXu_G0_pqa7RKQQVoJqbTndatZe33UmCOC54ng1ejQfDfexO7f1x5pGpIt3Kt9clMbV1UcplzQTnLGU_U_C9UerRJvyT1kHVpfSJ4MxEkpjM_uxr6GOXq893_s7ffpuzrM3ZtoOnW0Tf90A1xCvIjODRMDMbeJ0ewHEbglIYcRkCOI5BkL85Tvxedep79Bv8rBDg</recordid><startdate>20180711</startdate><enddate>20180711</enddate><creator>Pappas, Matthew A</creator><creator>Vijan, Sandeep</creator><creator>Rothberg, Michael B</creator><creator>Singer, Daniel E</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-0353-1785</orcidid></search><sort><creationdate>20180711</creationdate><title>Reducing age bias in decision analyses of anticoagulation for patients with nonvalvular atrial fibrillation - A microsimulation study</title><author>Pappas, Matthew A ; Vijan, Sandeep ; Rothberg, Michael B ; Singer, Daniel E</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-c609edf3c13361052e92d9118080049fe1e35e155dfa46c0b6774ce36ffdaac23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adult</topic><topic>Age</topic><topic>Age Factors</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Anticoagulants</topic><topic>Anticoagulants - pharmacology</topic><topic>Anticoagulants - therapeutic use</topic><topic>Atrial Fibrillation - blood</topic><topic>Atrial Fibrillation - complications</topic><topic>Atrial Fibrillation - epidemiology</topic><topic>Bias</topic><topic>Biology and Life Sciences</topic><topic>Blood Coagulation - drug effects</topic><topic>Cardiac arrhythmia</topic><topic>Census of Population</topic><topic>Complications and side effects</topic><topic>Cost analysis</topic><topic>Decision analysis</topic><topic>Decision making</topic><topic>Decision Support Techniques</topic><topic>Disease control</topic><topic>Dosage and administration</topic><topic>Drug dosages</topic><topic>Engineering and Technology</topic><topic>Estimates</topic><topic>Female</topic><topic>Fibrillation</topic><topic>Health risks</topic><topic>Hemorrhage</topic><topic>Hospital Mortality</topic><topic>Hospitals</topic><topic>Humans</topic><topic>Internal medicine</topic><topic>Intracranial Hemorrhages - etiology</topic><topic>Ischemia</topic><topic>Life expectancy</topic><topic>Life span</topic><topic>Male</topic><topic>Mathematical models</topic><topic>Medicine</topic><topic>Medicine and Health Sciences</topic><topic>Middle Aged</topic><topic>Models, Cardiovascular</topic><topic>Monte Carlo Method</topic><topic>Monte Carlo simulation</topic><topic>Mortality</topic><topic>Nutrition</topic><topic>Patients</topic><topic>Prevention</topic><topic>Quality-Adjusted Life Years</topic><topic>Research and Analysis Methods</topic><topic>Risk factors</topic><topic>Severity of Illness Index</topic><topic>Stroke</topic><topic>Stroke - diagnosis</topic><topic>Stroke - epidemiology</topic><topic>Stroke - etiology</topic><topic>Stroke - prevention & control</topic><topic>Thrombolytic Therapy</topic><topic>Weighting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pappas, Matthew A</creatorcontrib><creatorcontrib>Vijan, Sandeep</creatorcontrib><creatorcontrib>Rothberg, Michael B</creatorcontrib><creatorcontrib>Singer, Daniel E</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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A microsimulation study</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2018-07-11</date><risdate>2018</risdate><volume>13</volume><issue>7</issue><spage>e0199593</spage><pages>e0199593-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Anticoagulation decreases a patient's risk of ischemic stroke and increases the risk of hemorrhage. Decision analyses regarding anticoagulation therefore require that different outcomes be weighted in comparison to one another. Most decision analyses to date have weighted intracranial hemorrhage (ICH) as 1.5 times worse than ischemic stroke, but because death and disability have lifelong impact, the expected impact should vary by life expectancy. Therefore, a fixed weighting ratio leads to age-related bias decision analyses of anticoagulation. We aimed to quantify the relative impact of ICH and ischemic stroke and derive a ratio that allows decision analysis without microsimulation.
We created a microsimulation model to predict QALYs lost due to ICH and ischemic stroke. We then applied a meta-model to predict the ratio of QALYs lost from ICH relative to ischemic stroke.
Previously-used weighting ratios (1.5) are close to our derived mean weighting ratio (1.60). However, the weighting ratio of QALYs lost from ICH relative to ischemic stroke is sensitive to age and discount rate. Patients at younger ages have higher mean weighting ratios, as do patients with higher discount rates.
The ratio of QALYs lost to ICH relative to ischemic stroke varies with age and discount rate. We present a set of such ratios here for use in decision analyses that do not incorporate full microsimulation models. Use of weighting ratios that vary with age, rather than the current fixed ratios, has the potential to reduce age-based bias in decision-making regarding events with lifelong implications. In this case, use of dynamic ratios may change anticoagulation recommendations for patients with nonvalvular atrial fibrillation at relatively low stroke risk.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>29995900</pmid><doi>10.1371/journal.pone.0199593</doi><tpages>e0199593</tpages><orcidid>https://orcid.org/0000-0002-0353-1785</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adult Age Age Factors Aged Aged, 80 and over Anticoagulants Anticoagulants - pharmacology Anticoagulants - therapeutic use Atrial Fibrillation - blood Atrial Fibrillation - complications Atrial Fibrillation - epidemiology Bias Biology and Life Sciences Blood Coagulation - drug effects Cardiac arrhythmia Census of Population Complications and side effects Cost analysis Decision analysis Decision making Decision Support Techniques Disease control Dosage and administration Drug dosages Engineering and Technology Estimates Female Fibrillation Health risks Hemorrhage Hospital Mortality Hospitals Humans Internal medicine Intracranial Hemorrhages - etiology Ischemia Life expectancy Life span Male Mathematical models Medicine Medicine and Health Sciences Middle Aged Models, Cardiovascular Monte Carlo Method Monte Carlo simulation Mortality Nutrition Patients Prevention Quality-Adjusted Life Years Research and Analysis Methods Risk factors Severity of Illness Index Stroke Stroke - diagnosis Stroke - epidemiology Stroke - etiology Stroke - prevention & control Thrombolytic Therapy Weighting |
title | Reducing age bias in decision analyses of anticoagulation for patients with nonvalvular atrial fibrillation - A microsimulation study |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-03T03%3A29%3A52IST&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=Reducing%20age%20bias%20in%20decision%20analyses%20of%20anticoagulation%20for%20patients%20with%20nonvalvular%20atrial%20fibrillation%20-%20A%20microsimulation%20study&rft.jtitle=PloS%20one&rft.au=Pappas,%20Matthew%20A&rft.date=2018-07-11&rft.volume=13&rft.issue=7&rft.spage=e0199593&rft.pages=e0199593-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0199593&rft_dat=%3Cgale_plos_%3EA546243534%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=2068340247&rft_id=info:pmid/29995900&rft_galeid=A546243534&rft_doaj_id=oai_doaj_org_article_7c816b84db4b45b2a2d7ad977ca5e7e1&rfr_iscdi=true |