Prediction model of Parkinson's disease based on antiparkinsonian drug claims.
Identifieur interne : 000912 ( PubMed/Curation ); précédent : 000911; suivant : 000913Prediction model of Parkinson's disease based on antiparkinsonian drug claims.
Auteurs : Frédéric Moisan [France] ; Véronique Gourlet ; Jean-Louis Mazurie ; Jean-Luc Dupupet ; Jean Houssinot ; Marcel Goldberg ; Ellen Imbernon ; Christophe Tzourio ; Alexis ElbazSource :
- American journal of epidemiology [ 1476-6256 ] ; 2011.
Descripteurs français
- Wicri :
- geographic : France.
English descriptors
- KwdEn :
- MESH :
- chemical , therapeutic use : Antiparkinson Agents.
- geographic , epidemiology : France.
- drug therapy : Parkinson Disease.
- epidemiology : Parkinson Disease.
- Aged, Databases, Factual, Female, Humans, Logistic Models, Male, Middle Aged, Models, Statistical, Prevalence, ROC Curve, Reproducibility of Results.
Abstract
Drug claims databases are increasingly available and provide opportunities to investigate epidemiologic questions. The authors used computerized drug claims databases from a social security system in 5 French districts to predict the probability that a person had Parkinson's disease (PD) based on patterns of antiparkinsonian drug (APD) use. Clinical information for a population-based sample of persons using APDs in 2007 was collected. The authors built a prediction model using demographic variables and APDs as predictors and investigated the additional predictive benefit of including information on dose and regularity of use. Among 1,114 APD users, 320 (29%) had PD and 794 (71%) had another diagnosis as determined by study neurologists. A logistic model including information on cumulative APD dose and regularity of use showed good performance (c statistic = 0.953, sensitivity = 92.5%, specificity = 86.4%). Predicted PD prevalence (among persons aged ≥18 years) was 6.66/1,000; correcting this estimate using sensitivity/specificity led to a similar figure (6.04/1,000). These data demonstrate that drug claims databases can be used to estimate the probability that a person is being treated for PD and that information on APD dose and regularity of use improves models' performances. Similar approaches could be developed for other conditions.
DOI: 10.1093/aje/kwr081
PubMed: 21606234
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last="Dupupet">Jean-Luc Dupupet</name></author><author><name sortKey="Houssinot, Jean" sort="Houssinot, Jean" uniqKey="Houssinot J" first="Jean" last="Houssinot">Jean Houssinot</name></author><author><name sortKey="Goldberg, Marcel" sort="Goldberg, Marcel" uniqKey="Goldberg M" first="Marcel" last="Goldberg">Marcel Goldberg</name></author><author><name sortKey="Imbernon, Ellen" sort="Imbernon, Ellen" uniqKey="Imbernon E" first="Ellen" last="Imbernon">Ellen Imbernon</name></author><author><name sortKey="Tzourio, Christophe" sort="Tzourio, Christophe" uniqKey="Tzourio C" first="Christophe" last="Tzourio">Christophe Tzourio</name></author><author><name sortKey="Elbaz, Alexis" sort="Elbaz, Alexis" uniqKey="Elbaz A" first="Alexis" last="Elbaz">Alexis Elbaz</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2011">2011</date><idno type="RBID">pubmed:21606234</idno><idno type="pmid">21606234</idno><idno type="doi">10.1093/aje/kwr081</idno><idno 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Gourlet</name></author><author><name sortKey="Mazurie, Jean Louis" sort="Mazurie, Jean Louis" uniqKey="Mazurie J" first="Jean-Louis" last="Mazurie">Jean-Louis Mazurie</name></author><author><name sortKey="Dupupet, Jean Luc" sort="Dupupet, Jean Luc" uniqKey="Dupupet J" first="Jean-Luc" last="Dupupet">Jean-Luc Dupupet</name></author><author><name sortKey="Houssinot, Jean" sort="Houssinot, Jean" uniqKey="Houssinot J" first="Jean" last="Houssinot">Jean Houssinot</name></author><author><name sortKey="Goldberg, Marcel" sort="Goldberg, Marcel" uniqKey="Goldberg M" first="Marcel" last="Goldberg">Marcel Goldberg</name></author><author><name sortKey="Imbernon, Ellen" sort="Imbernon, Ellen" uniqKey="Imbernon E" first="Ellen" last="Imbernon">Ellen Imbernon</name></author><author><name sortKey="Tzourio, Christophe" sort="Tzourio, Christophe" uniqKey="Tzourio C" first="Christophe" last="Tzourio">Christophe Tzourio</name></author><author><name sortKey="Elbaz, Alexis" sort="Elbaz, Alexis" 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qualifier="epidemiology" xml:lang="en"><term>France</term></keywords><keywords scheme="MESH" qualifier="drug therapy" xml:lang="en"><term>Parkinson Disease</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Parkinson Disease</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Aged</term><term>Databases, Factual</term><term>Female</term><term>Humans</term><term>Logistic Models</term><term>Male</term><term>Middle Aged</term><term>Models, Statistical</term><term>Prevalence</term><term>ROC Curve</term><term>Reproducibility of Results</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>France</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Drug claims databases are increasingly available and provide opportunities to investigate epidemiologic questions. The authors used computerized drug claims databases from a social security system in 5 French districts to predict the probability that a person had Parkinson's disease (PD) based on patterns of antiparkinsonian drug (APD) use. Clinical information for a population-based sample of persons using APDs in 2007 was collected. The authors built a prediction model using demographic variables and APDs as predictors and investigated the additional predictive benefit of including information on dose and regularity of use. Among 1,114 APD users, 320 (29%) had PD and 794 (71%) had another diagnosis as determined by study neurologists. A logistic model including information on cumulative APD dose and regularity of use showed good performance (c statistic = 0.953, sensitivity = 92.5%, specificity = 86.4%). Predicted PD prevalence (among persons aged ≥18 years) was 6.66/1,000; correcting this estimate using sensitivity/specificity led to a similar figure (6.04/1,000). These data demonstrate that drug claims databases can be used to estimate the probability that a person is being treated for PD and that information on APD dose and regularity of use improves models' performances. Similar approaches could be developed for other conditions.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">21606234</PMID><DateCreated><Year>2011</Year><Month>07</Month><Day>22</Day></DateCreated><DateCompleted><Year>2011</Year><Month>09</Month><Day>20</Day></DateCompleted><DateRevised><Year>2011</Year><Month>07</Month><Day>22</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1476-6256</ISSN><JournalIssue CitedMedium="Internet"><Volume>174</Volume><Issue>3</Issue><PubDate><Year>2011</Year><Month>Aug</Month><Day>01</Day></PubDate></JournalIssue><Title>American journal of epidemiology</Title><ISOAbbreviation>Am. J. Epidemiol.</ISOAbbreviation></Journal><ArticleTitle>Prediction model of Parkinson's disease based on antiparkinsonian drug claims.</ArticleTitle><Pagination><MedlinePgn>354-63</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/aje/kwr081</ELocationID><Abstract><AbstractText>Drug claims databases are increasingly available and provide opportunities to investigate epidemiologic questions. The authors used computerized drug claims databases from a social security system in 5 French districts to predict the probability that a person had Parkinson's disease (PD) based on patterns of antiparkinsonian drug (APD) use. Clinical information for a population-based sample of persons using APDs in 2007 was collected. The authors built a prediction model using demographic variables and APDs as predictors and investigated the additional predictive benefit of including information on dose and regularity of use. Among 1,114 APD users, 320 (29%) had PD and 794 (71%) had another diagnosis as determined by study neurologists. A logistic model including information on cumulative APD dose and regularity of use showed good performance (c statistic = 0.953, sensitivity = 92.5%, specificity = 86.4%). Predicted PD prevalence (among persons aged ≥18 years) was 6.66/1,000; correcting this estimate using sensitivity/specificity led to a similar figure (6.04/1,000). These data demonstrate that drug claims databases can be used to estimate the probability that a person is being treated for PD and that information on APD dose and regularity of use improves models' performances. Similar approaches could be developed for other conditions.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Moisan</LastName><ForeName>Frédéric</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>Unité–Neuroepidemiology, INSERM, Paris, France. frederic.moisan@upmc.fr</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gourlet</LastName><ForeName>Véronique</ForeName><Initials>V</Initials></Author><Author ValidYN="Y"><LastName>Mazurie</LastName><ForeName>Jean-Louis</ForeName><Initials>JL</Initials></Author><Author ValidYN="Y"><LastName>Dupupet</LastName><ForeName>Jean-Luc</ForeName><Initials>JL</Initials></Author><Author ValidYN="Y"><LastName>Houssinot</LastName><ForeName>Jean</ForeName><Initials>J</Initials></Author><Author ValidYN="Y"><LastName>Goldberg</LastName><ForeName>Marcel</ForeName><Initials>M</Initials></Author><Author ValidYN="Y"><LastName>Imbernon</LastName><ForeName>Ellen</ForeName><Initials>E</Initials></Author><Author ValidYN="Y"><LastName>Tzourio</LastName><ForeName>Christophe</ForeName><Initials>C</Initials></Author><Author ValidYN="Y"><LastName>Elbaz</LastName><ForeName>Alexis</ForeName><Initials>A</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2011</Year><Month>05</Month><Day>23</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Am J Epidemiol</MedlineTA><NlmUniqueID>7910653</NlmUniqueID><ISSNLinking>0002-9262</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D000978">Antiparkinson Agents</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000978" MajorTopicYN="N">Antiparkinson Agents</DescriptorName><QualifierName UI="Q000627" MajorTopicYN="Y">therapeutic use</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D016208" MajorTopicYN="N">Databases, Factual</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005602" MajorTopicYN="N" Type="Geographic">France</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016015" MajorTopicYN="N">Logistic Models</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015233" MajorTopicYN="N">Models, Statistical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010300" MajorTopicYN="N">Parkinson Disease</DescriptorName><QualifierName UI="Q000188" MajorTopicYN="Y">drug therapy</QualifierName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D015995" MajorTopicYN="N">Prevalence</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012372" MajorTopicYN="N">ROC Curve</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015203" MajorTopicYN="N">Reproducibility of Results</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2011</Year><Month>5</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2011</Year><Month>5</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2011</Year><Month>9</Month><Day>21</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">21606234</ArticleId><ArticleId IdType="pii">kwr081</ArticleId><ArticleId IdType="doi">10.1093/aje/kwr081</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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