Optimizing algorithms to identify Parkinson's disease cases within an administrative database
Identifieur interne : 002A26 ( Main/Merge ); précédent : 002A25; suivant : 002A27Optimizing algorithms to identify Parkinson's disease cases within an administrative database
Auteurs : Nicholas R. Szumski [États-Unis] ; Eric M. Cheng [États-Unis]Source :
- Movement Disorders [ 0885-3185 ] ; 2009-01-15.
English descriptors
- KwdEn :
- Aged, Aged, 80 and over, Algorithms, Ambulatory Care Facilities (statistics & numerical data), Databases, Factual, Diagnostic Errors, Female, Humans, ICD‐9‐CM codes, International Classification of Diseases, Los Angeles (epidemiology), Male, Middle Aged, Outpatients (statistics & numerical data), Parkinson Disease (diagnosis), Parkinson Disease (epidemiology), Parkinson's disease, Sensitivity and Specificity, United States, United States Department of Veterans Affairs (organization & administration), administrative data, predictive value, sensitivity, specificity.
- MESH :
- geographic , epidemiology : Los Angeles.
- diagnosis : Parkinson Disease.
- epidemiology : Parkinson Disease.
- organization & administration : United States Department of Veterans Affairs.
- statistics & numerical data : Ambulatory Care Facilities, Outpatients.
- Aged, Aged, 80 and over, Algorithms, Databases, Factual, Diagnostic Errors, Female, Humans, International Classification of Diseases, Male, Middle Aged, Sensitivity and Specificity, United States.
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
Url:
- https://api.istex.fr/document/E84C44991A479B42B0866A516747EC8078F48D0A/fulltext/pdf
- http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2647991
DOI: 10.1002/mds.22283
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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>Aged</term><term>Aged, 80 and over</term><term>Algorithms</term><term>Ambulatory Care Facilities (statistics & numerical data)</term><term>Databases, Factual</term><term>Diagnostic Errors</term><term>Female</term><term>Humans</term><term>ICD‐9‐CM codes</term><term>International Classification of Diseases</term><term>Los Angeles (epidemiology)</term><term>Male</term><term>Middle Aged</term><term>Outpatients (statistics & numerical data)</term><term>Parkinson Disease (diagnosis)</term><term>Parkinson Disease (epidemiology)</term><term>Parkinson's disease</term><term>Sensitivity and Specificity</term><term>United States</term><term>United States Department of Veterans Affairs (organization & administration)</term><term>administrative data</term><term>predictive value</term><term>sensitivity</term><term>specificity</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Los Angeles</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Parkinson Disease</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Parkinson Disease</term></keywords><keywords scheme="MESH" qualifier="organization & administration" xml:lang="en"><term>United States Department of Veterans Affairs</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Ambulatory Care Facilities</term><term>Outpatients</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Aged</term><term>Aged, 80 and over</term><term>Algorithms</term><term>Databases, Factual</term><term>Diagnostic Errors</term><term>Female</term><term>Humans</term><term>International Classification of Diseases</term><term>Male</term><term>Middle Aged</term><term>Sensitivity and Specificity</term><term>United States</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><double doi="10.1002/mds.22283"><ISTEX><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Optimizing algorithms to identify Parkinson's disease cases within an administrative database</title><author><name sortKey="Szumski, Nicholas R" sort="Szumski, Nicholas R" uniqKey="Szumski N" first="Nicholas R." last="Szumski">Nicholas R. Szumski</name></author><author><name sortKey="Cheng, Eric M" sort="Cheng, Eric M" uniqKey="Cheng E" first="Eric M." last="Cheng">Eric M. Cheng</name></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:E84C44991A479B42B0866A516747EC8078F48D0A</idno><date when="2009" year="2009">2009</date><idno type="doi">10.1002/mds.22283</idno><idno type="url">https://api.istex.fr/document/E84C44991A479B42B0866A516747EC8078F48D0A/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">001771</idno><idno type="wicri:Area/Istex/Curation">001771</idno><idno type="wicri:Area/Istex/Checkpoint">000D67</idno><idno type="wicri:doubleKey">0885-3185:2009:Szumski N:optimizing:algorithms:to</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Optimizing algorithms to identify Parkinson's disease cases within an administrative database</title><author><name sortKey="Szumski, Nicholas R" sort="Szumski, Nicholas R" uniqKey="Szumski N" first="Nicholas R." last="Szumski">Nicholas R. Szumski</name><affiliation wicri:level="2"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Neurology, UCLA, Los Angeles, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation><affiliation wicri:level="2"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Southwest Parkinson's Disease Research, Education, and Clinical Center (PADRECC), Los Angeles, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation><affiliation wicri:level="2"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Neurology, VA Greater Los Angeles Healthcare System, Los Angeles, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Cheng, Eric M" sort="Cheng, Eric M" uniqKey="Cheng E" first="Eric M." last="Cheng">Eric M. Cheng</name><affiliation wicri:level="2"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Neurology, UCLA, Los Angeles, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation><affiliation wicri:level="2"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Southwest Parkinson's Disease Research, Education, and Clinical Center (PADRECC), Los Angeles, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation><affiliation wicri:level="2"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Neurology, VA Greater Los Angeles Healthcare System, Los Angeles, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author></analytic><monogr></monogr><series><title level="j">Movement Disorders</title><title level="j" type="abbrev">Mov. 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><PMC><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Optimizing Algorithms to Identify Parkinson’s Disease Cases Within an Administrative Database</title><author><name sortKey="Szumski, Nicholas R" sort="Szumski, Nicholas R" uniqKey="Szumski N" first="Nicholas R." last="Szumski">Nicholas R. Szumski</name><affiliation wicri:level="2"><nlm:aff id="A1">Department of Neurology, UCLA, Los Angeles, California, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Neurology, UCLA, Los Angeles, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation><affiliation wicri:level="2"><nlm:aff id="A2">Southwest Parkinson’s Disease Research, Education, and Clinical Center (PADRECC), Los Angeles, California, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Southwest Parkinson’s Disease Research, Education, and Clinical Center (PADRECC), Los Angeles, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation><affiliation wicri:level="2"><nlm:aff id="A3">Department of Neurology, VA Greater Los Angeles Healthcare System, Los Angeles, California, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Neurology, VA Greater Los Angeles Healthcare System, Los Angeles, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Cheng, Eric M" sort="Cheng, Eric M" uniqKey="Cheng E" first="Eric M." last="Cheng">Eric M. Cheng</name><affiliation wicri:level="2"><nlm:aff id="A1">Department of Neurology, UCLA, Los Angeles, California, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Neurology, UCLA, Los Angeles, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation><affiliation wicri:level="2"><nlm:aff id="A2">Southwest Parkinson’s Disease Research, Education, and Clinical Center (PADRECC), Los Angeles, California, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Southwest Parkinson’s Disease Research, Education, and Clinical Center (PADRECC), Los Angeles, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation><affiliation wicri:level="2"><nlm:aff id="A3">Department of Neurology, VA Greater Los Angeles Healthcare System, Los Angeles, California, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Neurology, VA Greater Los Angeles Healthcare System, Los Angeles, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">18816696</idno><idno type="pmc">2647991</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2647991</idno><idno type="RBID">PMC:2647991</idno><idno type="doi">10.1002/mds.22283</idno><date when="2009">2009</date><idno type="wicri:Area/Pmc/Corpus">000037</idno><idno type="wicri:Area/Pmc/Curation">000037</idno><idno type="wicri:Area/Pmc/Checkpoint">000398</idno><idno type="wicri:source">PubMed</idno><idno type="wicri:Area/PubMed/Corpus">002015</idno><idno type="wicri:Area/PubMed/Curation">002015</idno><idno type="wicri:Area/PubMed/Checkpoint">001C72</idno><idno type="wicri:Area/Ncbi/Merge">002348</idno><idno type="wicri:Area/Ncbi/Curation">002348</idno><idno type="wicri:Area/Ncbi/Checkpoint">002348</idno><idno type="wicri:doubleKey">0885-3185:2009:Szumski N:optimizing:algorithms:to</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Optimizing Algorithms to Identify Parkinson’s Disease Cases Within an Administrative Database</title><author><name sortKey="Szumski, Nicholas R" sort="Szumski, Nicholas R" uniqKey="Szumski N" first="Nicholas R." last="Szumski">Nicholas R. Szumski</name><affiliation wicri:level="2"><nlm:aff id="A1">Department of Neurology, UCLA, Los Angeles, California, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Neurology, UCLA, Los Angeles, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation><affiliation wicri:level="2"><nlm:aff id="A2">Southwest Parkinson’s Disease Research, Education, and Clinical Center (PADRECC), Los Angeles, California, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Southwest Parkinson’s Disease Research, Education, and Clinical Center (PADRECC), Los Angeles, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation><affiliation wicri:level="2"><nlm:aff id="A3">Department of Neurology, VA Greater Los Angeles Healthcare System, Los Angeles, California, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Neurology, VA Greater Los Angeles Healthcare System, Los Angeles, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Cheng, Eric M" sort="Cheng, Eric M" uniqKey="Cheng E" first="Eric M." last="Cheng">Eric M. Cheng</name><affiliation wicri:level="2"><nlm:aff id="A1">Department of Neurology, UCLA, Los Angeles, California, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Neurology, UCLA, Los Angeles, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation><affiliation wicri:level="2"><nlm:aff id="A2">Southwest Parkinson’s Disease Research, Education, and Clinical Center (PADRECC), Los Angeles, California, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Southwest Parkinson’s Disease Research, Education, and Clinical Center (PADRECC), Los Angeles, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation><affiliation wicri:level="2"><nlm:aff id="A3">Department of Neurology, VA Greater Los Angeles Healthcare System, Los Angeles, California, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Neurology, VA Greater Los Angeles Healthcare System, Los Angeles, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author></analytic><series><title level="j">Movement disorders : official journal of the Movement Disorder Society</title><idno type="ISSN">0885-3185</idno><idno type="eISSN">1531-8257</idno><imprint><date when="2009">2009</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Aged</term><term>Aged, 80 and over</term><term>Algorithms</term><term>Ambulatory Care Facilities (statistics & numerical data)</term><term>Databases, Factual</term><term>Diagnostic Errors</term><term>Female</term><term>Humans</term><term>International Classification of Diseases</term><term>Los Angeles (epidemiology)</term><term>Male</term><term>Middle Aged</term><term>Outpatients (statistics & numerical data)</term><term>Parkinson Disease (diagnosis)</term><term>Parkinson Disease (epidemiology)</term><term>Sensitivity and Specificity</term><term>United States</term><term>United States Department of Veterans Affairs (organization & administration)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Los Angeles</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Parkinson Disease</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Parkinson Disease</term></keywords><keywords scheme="MESH" qualifier="organization & administration" xml:lang="en"><term>United States Department of Veterans Affairs</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Ambulatory Care Facilities</term><term>Outpatients</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Aged</term><term>Aged, 80 and over</term><term>Algorithms</term><term>Databases, Factual</term><term>Diagnostic Errors</term><term>Female</term><term>Humans</term><term>International Classification of Diseases</term><term>Male</term><term>Middle Aged</term><term>Sensitivity and Specificity</term><term>United States</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="P2">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% (<italic>P</italic> = 0.003 vs. baseline). When presence of a PD prescription was an additional criterion, PPV increased further to 88.2% (<italic>P</italic> = 0.04 vs. without medication criterion), but sensitivity decreased from 87.4 to 77.1% (<italic>P</italic> = 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.</p></div></front></TEI></PMC></double></record>Pour manipuler ce document sous Unix (Dilib)
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