Optimizing algorithms to identify Parkinson's disease cases within an administrative database
Identifieur interne : 001771 ( Istex/Corpus ); précédent : 001770; suivant : 001772Optimizing algorithms to identify Parkinson's disease cases within an administrative database
Auteurs : Nicholas R. Szumski ; Eric M. ChengSource :
- Movement Disorders [ 0885-3185 ] ; 2009-01-15.
English descriptors
- KwdEn :
Abstract
Patients assigned the diagnostic ICD‐9‐CM code for Parkinson's disease (PD) in an administrative database may not truly carry that diagnosis because of the various error sources. Improved ability to identify PD cases within databases may facilitate specific research goals. Experienced chart reviewers abstracted the working diagnosis of all 577 patients assigned diagnostic code 332.0 (PD) during 1 year at a VA Healthcare System. We then tested the ability of various algorithms making use of PD and non‐PD diagnostic codes, specialty of clinics visited, and medication prescription data to predict the abstracted working diagnosis. Chart review determined 436 (75.6%) patients to be PD or Possibly PD, and 141 (24.4%) to be Not PD. Our tiered consensus algorithm preferentially used data from specialists over nonspecialists improved PPV to 83.2% (P = 0.003 vs. baseline). When presence of a PD prescription was an additional criterion, PPV increased further to 88.2% (P = 0.04 vs. without medication criterion), but sensitivity decreased from 87.4 to 77.1% (P = 0.0001). We demonstrate that algorithms provide better identification of PD cases than using a single occurrence of the diagnostic code for PD, and modifications of such algorithms can be tuned to maximize parameters that best meet the goals of a particular database query. © 2008 Movement Disorder Society
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DOI: 10.1002/mds.22283
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ISTEX:E84C44991A479B42B0866A516747EC8078F48D0ALe document en format XML
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Disord.</title><idno type="ISSN">0885-3185</idno><idno type="eISSN">1531-8257</idno><imprint><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><pubPlace>Hoboken</pubPlace><date type="published" when="2009-01-15">2009-01-15</date><biblScope unit="vol">24</biblScope><biblScope unit="issue">1</biblScope><biblScope unit="page" from="51">51</biblScope><biblScope unit="page" to="56">56</biblScope></imprint><idno type="ISSN">0885-3185</idno></series><idno type="istex">E84C44991A479B42B0866A516747EC8078F48D0A</idno><idno type="DOI">10.1002/mds.22283</idno><idno type="ArticleID">MDS22283</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0885-3185</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>ICD‐9‐CM codes</term><term>Parkinson's disease</term><term>administrative data</term><term>predictive value</term><term>sensitivity</term><term>specificity</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Patients assigned the diagnostic ICD‐9‐CM code for Parkinson's disease (PD) in an administrative database may not truly carry that diagnosis because of the various error sources. Improved ability to identify PD cases within databases may facilitate specific research goals. Experienced chart reviewers abstracted the working diagnosis of all 577 patients assigned diagnostic code 332.0 (PD) during 1 year at a VA Healthcare System. We then tested the ability of various algorithms making use of PD and non‐PD diagnostic codes, specialty of clinics visited, and medication prescription data to predict the abstracted working diagnosis. Chart review determined 436 (75.6%) patients to be PD or Possibly PD, and 141 (24.4%) to be Not PD. Our tiered consensus algorithm preferentially used data from specialists over nonspecialists improved PPV to 83.2% (P = 0.003 vs. baseline). When presence of a PD prescription was an additional criterion, PPV increased further to 88.2% (P = 0.04 vs. without medication criterion), but sensitivity decreased from 87.4 to 77.1% (P = 0.0001). We demonstrate that algorithms provide better identification of PD cases than using a single occurrence of the diagnostic code for PD, and modifications of such algorithms can be tuned to maximize parameters that best meet the goals of a particular database query. © 2008 Movement Disorder Society</div></front></TEI><istex><corpusName>wiley</corpusName><author><json:item><name>Nicholas R. Szumski MD</name><affiliations><json:string>Department of Neurology, UCLA, Los Angeles, California, USA</json:string><json:string>Southwest Parkinson's Disease Research, Education, and Clinical Center (PADRECC), Los Angeles, California, USA</json:string><json:string>Department of Neurology, VA Greater Los Angeles Healthcare System, Los Angeles, California, USA</json:string></affiliations></json:item><json:item><name>Eric M. Cheng MD, MS</name><affiliations><json:string>Department of Neurology, UCLA, Los Angeles, California, USA</json:string><json:string>Southwest Parkinson's Disease Research, Education, and Clinical Center (PADRECC), Los Angeles, California, USA</json:string><json:string>Department of Neurology, VA Greater Los Angeles Healthcare System, Los Angeles, California, USA</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>Parkinson's disease</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>ICD‐9‐CM codes</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>administrative data</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>predictive value</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>sensitivity</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>specificity</value></json:item></subject><language><json:string>eng</json:string></language><abstract>Patients assigned the diagnostic ICD‐9‐CM code for Parkinson's disease (PD) in an administrative database may not truly carry that diagnosis because of the various error sources. Improved ability to identify PD cases within databases may facilitate specific research goals. Experienced chart reviewers abstracted the working diagnosis of all 577 patients assigned diagnostic code 332.0 (PD) during 1 year at a VA Healthcare System. We then tested the ability of various algorithms making use of PD and non‐PD diagnostic codes, specialty of clinics visited, and medication prescription data to predict the abstracted working diagnosis. Chart review determined 436 (75.6%) patients to be PD or Possibly PD, and 141 (24.4%) to be Not PD. Our tiered consensus algorithm preferentially used data from specialists over nonspecialists improved PPV to 83.2% (P = 0.003 vs. baseline). When presence of a PD prescription was an additional criterion, PPV increased further to 88.2% (P = 0.04 vs. without medication criterion), but sensitivity decreased from 87.4 to 77.1% (P = 0.0001). We demonstrate that algorithms provide better identification of PD cases than using a single occurrence of the diagnostic code for PD, and modifications of such algorithms can be tuned to maximize parameters that best meet the goals of a particular database query. © 2008 Movement Disorder Society</abstract><qualityIndicators><score>6.016</score><pdfVersion>1.3</pdfVersion><pdfPageSize>612 x 810 pts</pdfPageSize><refBibsNative>true</refBibsNative><abstractCharCount>1372</abstractCharCount><pdfWordCount>3484</pdfWordCount><pdfCharCount>23146</pdfCharCount><pdfPageCount>6</pdfPageCount><abstractWordCount>211</abstractWordCount></qualityIndicators><title>Optimizing algorithms to identify Parkinson's disease cases within an administrative database</title><genre><json:string>Serial article</json:string></genre><host><volume>24</volume><pages><total>6</total><last>56</last><first>51</first></pages><issn><json:string>0885-3185</json:string></issn><issue>1</issue><subject><json:item><value>Research Article</value></json:item></subject><genre></genre><language><json:string>unknown</json:string></language><title>Movement Disorders</title><doi><json:string>10.1002/(ISSN)1531-8257</json:string></doi></host><publicationDate>2009</publicationDate><copyrightDate>2009</copyrightDate><doi><json:string>10.1002/mds.22283</json:string></doi><id>E84C44991A479B42B0866A516747EC8078F48D0A</id><fulltext><json:item><original>true</original><mimetype>application/pdf</mimetype><extension>pdf</extension><uri>https://api.istex.fr/document/E84C44991A479B42B0866A516747EC8078F48D0A/fulltext/pdf</uri></json:item><json:item><original>false</original><mimetype>application/zip</mimetype><extension>zip</extension><uri>https://api.istex.fr/document/E84C44991A479B42B0866A516747EC8078F48D0A/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/document/E84C44991A479B42B0866A516747EC8078F48D0A/fulltext/tei"><teiHeader type="text"><fileDesc><titleStmt><title level="a" type="main" xml:lang="en">Optimizing algorithms to identify Parkinson's disease cases within an administrative database</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><pubPlace>Hoboken</pubPlace><availability><p>Wiley Subscription Services, Inc., A Wiley Company</p></availability><date>2009</date></publicationStmt><notesStmt><note type="content">*Potential conflict of interest: None reported.</note><note>Southwest Parkinson's Disease Research, Education, and Clinical Center (PADRECC)</note><note>NINDS - No. K23NS058571;</note></notesStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a" type="main" xml:lang="en">Optimizing algorithms to identify Parkinson's disease cases within an administrative database</title><author><persName><forename type="first">Nicholas R.</forename><surname>Szumski</surname><roleName type="degree">MD</roleName></persName><note type="correspondence"><p>Correspondence: UCLA Department of Neurology, 710 Westwood Plaza, RNRC/A‐153, Los Angeles, CA 90095</p></note><affiliation>Department of Neurology, UCLA, Los Angeles, California, USA</affiliation><affiliation>Southwest Parkinson's Disease Research, Education, and Clinical Center (PADRECC), Los Angeles, California, USA</affiliation><affiliation>Department of Neurology, VA Greater Los Angeles Healthcare System, Los Angeles, California, USA</affiliation></author><author><persName><forename type="first">Eric M.</forename><surname>Cheng</surname><roleName type="degree">MD, MS</roleName></persName><affiliation>Department of Neurology, UCLA, Los Angeles, California, USA</affiliation><affiliation>Southwest Parkinson's Disease Research, Education, and Clinical Center (PADRECC), Los Angeles, California, USA</affiliation><affiliation>Department of Neurology, VA Greater Los Angeles Healthcare System, Los Angeles, California, USA</affiliation></author></analytic><monogr><title level="j">Movement Disorders</title><title level="j" type="abbrev">Mov. 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Improved ability to identify PD cases within databases may facilitate specific research goals. Experienced chart reviewers abstracted the working diagnosis of all 577 patients assigned diagnostic code 332.0 (PD) during 1 year at a VA Healthcare System. We then tested the ability of various algorithms making use of PD and non‐PD diagnostic codes, specialty of clinics visited, and medication prescription data to predict the abstracted working diagnosis. Chart review determined 436 (75.6%) patients to be PD or Possibly PD, and 141 (24.4%) to be Not PD. Our tiered consensus algorithm preferentially used data from specialists over nonspecialists improved PPV to 83.2% (P = 0.003 vs. baseline). When presence of a PD prescription was an additional criterion, PPV increased further to 88.2% (P = 0.04 vs. without medication criterion), but sensitivity decreased from 87.4 to 77.1% (P = 0.0001). We demonstrate that algorithms provide better identification of PD cases than using a single occurrence of the diagnostic code for PD, and modifications of such algorithms can be tuned to maximize parameters that best meet the goals of a particular database query. © 2008 Movement Disorder Society</p></abstract><textClass xml:lang="en"><keywords scheme="keyword"><list><head>Keywords</head><item><term>Parkinson's disease</term></item><item><term>ICD‐9‐CM codes</term></item><item><term>administrative data</term></item><item><term>predictive value</term></item><item><term>sensitivity</term></item><item><term>specificity</term></item></list></keywords></textClass><textClass><keywords scheme="Journal Subject"><list><head>Article category</head><item><term>Research Article</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2008-04-30">Received</change><change when="2008-07-23">Registration</change><change when="2009-01-15">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><original>false</original><mimetype>text/plain</mimetype><extension>txt</extension><uri>https://api.istex.fr/document/E84C44991A479B42B0866A516747EC8078F48D0A/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Wiley, elements deleted: body"><istex:xmlDeclaration>version="1.0" encoding="UTF-8" standalone="yes"</istex:xmlDeclaration><istex:document><component version="2.0" type="serialArticle" xml:lang="en"><header><publicationMeta level="product"><publisherInfo><publisherName>Wiley Subscription Services, Inc., A Wiley Company</publisherName><publisherLoc>Hoboken</publisherLoc></publisherInfo><doi registered="yes">10.1002/(ISSN)1531-8257</doi><issn type="print">0885-3185</issn><issn type="electronic">1531-8257</issn><idGroup><id type="product" value="MDS"></id></idGroup><titleGroup><title type="main" xml:lang="en" sort="MOVEMENT DISORDERS">Movement Disorders</title><title type="short">Mov. 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Improved ability to identify PD cases within databases may facilitate specific research goals. Experienced chart reviewers abstracted the working diagnosis of all 577 patients assigned diagnostic code 332.0 (PD) during 1 year at a VA Healthcare System. We then tested the ability of various algorithms making use of PD and non‐PD diagnostic codes, specialty of clinics visited, and medication prescription data to predict the abstracted working diagnosis. Chart review determined 436 (75.6%) patients to be PD or Possibly PD, and 141 (24.4%) to be Not PD. Our tiered consensus algorithm preferentially used data from specialists over nonspecialists improved PPV to 83.2% (<i>P</i> = 0.003 vs. baseline). When presence of a PD prescription was an additional criterion, PPV increased further to 88.2% (<i>P</i> = 0.04 vs. without medication criterion), but sensitivity decreased from 87.4 to 77.1% (<i>P</i> = 0.0001). We demonstrate that algorithms provide better identification of PD cases than using a single occurrence of the diagnostic code for PD, and modifications of such algorithms can be tuned to maximize parameters that best meet the goals of a particular database query. © 2008 Movement Disorder Society</p></abstract></abstractGroup></contentMeta><noteGroup><note xml:id="fn1"><p>Potential conflict of interest: None reported.</p></note></noteGroup></header></component></istex:document></istex:metadataXml><!--Version 0.6 générée le 4-12-2015--><mods version="3.6"><titleInfo lang="en"><title>Optimizing algorithms to identify Parkinson's disease cases within an administrative database</title></titleInfo><titleInfo type="abbreviated" lang="en"><title>Identifying PD in an Administrative Database</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Optimizing algorithms to identify Parkinson's disease cases within an administrative database</title></titleInfo><name type="personal"><namePart type="given">Nicholas R.</namePart><namePart type="family">Szumski</namePart><namePart type="termsOfAddress">MD</namePart><affiliation>Department of Neurology, UCLA, Los Angeles, California, USA</affiliation><affiliation>Southwest Parkinson's Disease Research, Education, and Clinical Center (PADRECC), Los Angeles, California, USA</affiliation><affiliation>Department of Neurology, VA Greater Los Angeles Healthcare System, Los Angeles, California, USA</affiliation><description>Correspondence: UCLA Department of Neurology, 710 Westwood Plaza, RNRC/A‐153, Los Angeles, CA 90095</description><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Eric M.</namePart><namePart type="family">Cheng</namePart><namePart type="termsOfAddress">MD, MS</namePart><affiliation>Department of Neurology, UCLA, Los Angeles, California, USA</affiliation><affiliation>Southwest Parkinson's Disease Research, Education, and Clinical Center (PADRECC), Los Angeles, California, USA</affiliation><affiliation>Department of Neurology, VA Greater Los Angeles Healthcare System, Los Angeles, California, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre authority="originalCategForm">article</genre><originInfo><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><place><placeTerm type="text">Hoboken</placeTerm></place><dateIssued encoding="w3cdtf">2009-01-15</dateIssued><dateCaptured encoding="w3cdtf">2008-04-30</dateCaptured><dateValid encoding="w3cdtf">2008-07-23</dateValid><copyrightDate encoding="w3cdtf">2009</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType><extent unit="tables">3</extent><extent unit="references">15</extent><extent unit="words">4743</extent></physicalDescription><abstract lang="en">Patients assigned the diagnostic ICD‐9‐CM code for Parkinson's disease (PD) in an administrative database may not truly carry that diagnosis because of the various error sources. Improved ability to identify PD cases within databases may facilitate specific research goals. Experienced chart reviewers abstracted the working diagnosis of all 577 patients assigned diagnostic code 332.0 (PD) during 1 year at a VA Healthcare System. We then tested the ability of various algorithms making use of PD and non‐PD diagnostic codes, specialty of clinics visited, and medication prescription data to predict the abstracted working diagnosis. Chart review determined 436 (75.6%) patients to be PD or Possibly PD, and 141 (24.4%) to be Not PD. Our tiered consensus algorithm preferentially used data from specialists over nonspecialists improved PPV to 83.2% (P = 0.003 vs. baseline). When presence of a PD prescription was an additional criterion, PPV increased further to 88.2% (P = 0.04 vs. without medication criterion), but sensitivity decreased from 87.4 to 77.1% (P = 0.0001). We demonstrate that algorithms provide better identification of PD cases than using a single occurrence of the diagnostic code for PD, and modifications of such algorithms can be tuned to maximize parameters that best meet the goals of a particular database query. © 2008 Movement Disorder Society</abstract><note type="content">*Potential conflict of interest: None reported.</note><note type="funding">Southwest Parkinson's Disease Research, Education, and Clinical Center (PADRECC)</note><note type="funding">NINDS - No. K23NS058571; </note><subject lang="en"><genre>Keywords</genre><topic>Parkinson's disease</topic><topic>ICD‐9‐CM codes</topic><topic>administrative data</topic><topic>predictive value</topic><topic>sensitivity</topic><topic>specificity</topic></subject><relatedItem type="host"><titleInfo><title>Movement Disorders</title></titleInfo><titleInfo type="abbreviated"><title>Mov. 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