MovDisordV3, PascalFrancis, Corpus, bibRecord, 001007

Optimizing Algorithms to Identify Parkinson's Disease Cases Within an Administrative Database

Identifieur interne : 001007 ( PascalFrancis/Corpus ); précédent : 001006; suivant : 001008

Optimizing Algorithms to Identify Parkinson's Disease Cases Within an Administrative Database

Auteurs : Nicholas R. Szumski ; Eric M. Cheng

Source :

Movement disorders [ 0885-3185 ] ; 2009.

RBID : Pascal:09-0094106

Descripteurs français

Pascal (Inist)
- Maladie de Parkinson, Pathologie du système nerveux, Algorithme, Base de données, Valeur prédictive, Sensibilité, Spécificité.

English descriptors

KwdEn :
- Algorithm, Database, Nervous system diseases, Parkinson disease, Predictive value, Sensitivity, Specificity.

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 non-specialists 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.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

A01	`01`	`1`		`@0 0885-3185`
A03		`1`		`@0 Mov. disord.`
A05				`@2 24`
A06				`@2 1`
A08	`01`	`1`	`ENG`	`@1 Optimizing Algorithms to Identify Parkinson's Disease Cases Within an Administrative Database`
A11	`01`	`1`		`@1 SZUMSKI (Nicholas R.)`
A11	`02`	`1`		`@1 CHENG (Eric M.)`
A14	`01`			`@1 Department of Neurology, UCLA @2 Los Angeles, California @3 USA @Z 1 aut. @Z 2 aut.`
A14	`02`			`@1 Southwest Parkinson's Disease Research, Education, and Clinical Center (PADRECC) @2 Los Angeles, California @3 USA @Z 1 aut. @Z 2 aut.`
A14	`03`			`@1 Department of Neurology, VA Greater Los Angeles Healthcare System @2 Los Angeles, California @3 USA @Z 1 aut. @Z 2 aut.`
A20				`@1 51-56`
A21				`@1 2009`
A23	`01`			`@0 ENG`
A43	`01`			`@1 INIST @2 20953 @5 354000186473060060`
A44				`@0 0000 @1 © 2009 INIST-CNRS. All rights reserved.`
A45				`@0 15 ref.`
A47	`01`	`1`		`@0 09-0094106`
A60				`@1 P`
A61				`@0 A`
A64	`01`	`1`		`@0 Movement disorders`
A66	`01`			`@0 USA`
C01	`01`		`ENG`	@0 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 non-specialists 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.
C02	`01`	`X`		`@0 002B17`
C02	`02`	`X`		`@0 002B17G`
C03	`01`	`X`	`FRE`	`@0 Maladie de Parkinson @2 NM @5 01`
C03	`01`	`X`	`ENG`	`@0 Parkinson disease @2 NM @5 01`
C03	`01`	`X`	`SPA`	`@0 Parkinson enfermedad @2 NM @5 01`
C03	`02`	`X`	`FRE`	`@0 Pathologie du système nerveux @5 02`
C03	`02`	`X`	`ENG`	`@0 Nervous system diseases @5 02`
C03	`02`	`X`	`SPA`	`@0 Sistema nervioso patología @5 02`
C03	`03`	`X`	`FRE`	`@0 Algorithme @5 09`
C03	`03`	`X`	`ENG`	`@0 Algorithm @5 09`
C03	`03`	`X`	`SPA`	`@0 Algoritmo @5 09`
C03	`04`	`X`	`FRE`	`@0 Base de données @5 10`
C03	`04`	`X`	`ENG`	`@0 Database @5 10`
C03	`04`	`X`	`SPA`	`@0 Base dato @5 10`
C03	`05`	`X`	`FRE`	`@0 Valeur prédictive @5 11`
C03	`05`	`X`	`ENG`	`@0 Predictive value @5 11`
C03	`05`	`X`	`SPA`	`@0 Valor predictivo @5 11`
C03	`06`	`X`	`FRE`	`@0 Sensibilité @5 12`
C03	`06`	`X`	`ENG`	`@0 Sensitivity @5 12`
C03	`06`	`X`	`SPA`	`@0 Sensibilidad @5 12`
C03	`07`	`X`	`FRE`	`@0 Spécificité @5 13`
C03	`07`	`X`	`ENG`	`@0 Specificity @5 13`
C03	`07`	`X`	`SPA`	`@0 Especificidad @5 13`
C07	`01`	`X`	`FRE`	`@0 Pathologie de l'encéphale @5 37`
C07	`01`	`X`	`ENG`	`@0 Cerebral disorder @5 37`
C07	`01`	`X`	`SPA`	`@0 Encéfalo patología @5 37`
C07	`02`	`X`	`FRE`	`@0 Syndrome extrapyramidal @5 38`
C07	`02`	`X`	`ENG`	`@0 Extrapyramidal syndrome @5 38`
C07	`02`	`X`	`SPA`	`@0 Extrapiramidal síndrome @5 38`
C07	`03`	`X`	`FRE`	`@0 Maladie dégénérative @5 39`
C07	`03`	`X`	`ENG`	`@0 Degenerative disease @5 39`
C07	`03`	`X`	`SPA`	`@0 Enfermedad degenerativa @5 39`
C07	`04`	`X`	`FRE`	`@0 Pathologie du système nerveux central @5 40`
C07	`04`	`X`	`ENG`	`@0 Central nervous system disease @5 40`
C07	`04`	`X`	`SPA`	`@0 Sistema nervosio central patología @5 40`
N21				`@1 068`
N44	`01`			`@1 OTO`
N82				`@1 OTO`

Format Inist (serveur)

NO :	PASCAL 09-0094106 INIST
ET :	Optimizing Algorithms to Identify Parkinson's Disease Cases Within an Administrative Database
AU :	SZUMSKI (Nicholas R.); CHENG (Eric M.)
AF :	Department of Neurology, UCLA/Los Angeles, California/Etats-Unis (1 aut., 2 aut.); Southwest Parkinson's Disease Research, Education, and Clinical Center (PADRECC)/Los Angeles, California/Etats-Unis (1 aut., 2 aut.); Department of Neurology, VA Greater Los Angeles Healthcare System/Los Angeles, California/Etats-Unis (1 aut., 2 aut.)
DT :	Publication en série; Niveau analytique
SO :	Movement disorders; ISSN 0885-3185; Etats-Unis; Da. 2009; Vol. 24; No. 1; Pp. 51-56; Bibl. 15 ref.
LA :	Anglais
EA :	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 non-specialists 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.
CC :	002B17; 002B17G
FD :	Maladie de Parkinson; Pathologie du système nerveux; Algorithme; Base de données; Valeur prédictive; Sensibilité; Spécificité
FG :	Pathologie de l'encéphale; Syndrome extrapyramidal; Maladie dégénérative; Pathologie du système nerveux central
ED :	Parkinson disease; Nervous system diseases; Algorithm; Database; Predictive value; Sensitivity; Specificity
EG :	Cerebral disorder; Extrapyramidal syndrome; Degenerative disease; Central nervous system disease
SD :	Parkinson enfermedad; Sistema nervioso patología; Algoritmo; Base dato; Valor predictivo; Sensibilidad; Especificidad
LO :	INIST-20953.354000186473060060
ID :	09-0094106

Links to Exploration step

Pascal:09-0094106

Le document en format XML

<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" level="a">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><inist:fA14 i1="01"><s1>Department of Neurology, UCLA</s1>
<s2>Los Angeles, California</s2>
<s3>USA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
</affiliation>
<affiliation><inist:fA14 i1="02"><s1>Southwest Parkinson's Disease Research, Education, and Clinical Center (PADRECC)</s1>
<s2>Los Angeles, California</s2>
<s3>USA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
</affiliation>
<affiliation><inist:fA14 i1="03"><s1>Department of Neurology, VA Greater Los Angeles Healthcare System</s1>
<s2>Los Angeles, California</s2>
<s3>USA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
</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><inist:fA14 i1="01"><s1>Department of Neurology, UCLA</s1>
<s2>Los Angeles, California</s2>
<s3>USA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
</affiliation>
<affiliation><inist:fA14 i1="02"><s1>Southwest Parkinson's Disease Research, Education, and Clinical Center (PADRECC)</s1>
<s2>Los Angeles, California</s2>
<s3>USA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
</affiliation>
<affiliation><inist:fA14 i1="03"><s1>Department of Neurology, VA Greater Los Angeles Healthcare System</s1>
<s2>Los Angeles, California</s2>
<s3>USA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
</affiliation>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">INIST</idno>
<idno type="inist">09-0094106</idno>
<date when="2009">2009</date>
<idno type="stanalyst">PASCAL 09-0094106 INIST</idno>
<idno type="RBID">Pascal:09-0094106</idno>
<idno type="wicri:Area/PascalFrancis/Corpus">001007</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">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><inist:fA14 i1="01"><s1>Department of Neurology, UCLA</s1>
<s2>Los Angeles, California</s2>
<s3>USA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
</affiliation>
<affiliation><inist:fA14 i1="02"><s1>Southwest Parkinson's Disease Research, Education, and Clinical Center (PADRECC)</s1>
<s2>Los Angeles, California</s2>
<s3>USA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
</affiliation>
<affiliation><inist:fA14 i1="03"><s1>Department of Neurology, VA Greater Los Angeles Healthcare System</s1>
<s2>Los Angeles, California</s2>
<s3>USA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
</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><inist:fA14 i1="01"><s1>Department of Neurology, UCLA</s1>
<s2>Los Angeles, California</s2>
<s3>USA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
</affiliation>
<affiliation><inist:fA14 i1="02"><s1>Southwest Parkinson's Disease Research, Education, and Clinical Center (PADRECC)</s1>
<s2>Los Angeles, California</s2>
<s3>USA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
</affiliation>
<affiliation><inist:fA14 i1="03"><s1>Department of Neurology, VA Greater Los Angeles Healthcare System</s1>
<s2>Los Angeles, California</s2>
<s3>USA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
</affiliation>
</author>
</analytic>
<series><title level="j" type="main">Movement disorders</title>
<title level="j" type="abbreviated">Mov. disord.</title>
<idno type="ISSN">0885-3185</idno>
<imprint><date when="2009">2009</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
<seriesStmt><title level="j" type="main">Movement disorders</title>
<title level="j" type="abbreviated">Mov. disord.</title>
<idno type="ISSN">0885-3185</idno>
</seriesStmt>
</fileDesc>
<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithm</term>
<term>Database</term>
<term>Nervous system diseases</term>
<term>Parkinson disease</term>
<term>Predictive value</term>
<term>Sensitivity</term>
<term>Specificity</term>
</keywords>
<keywords scheme="Pascal" xml:lang="fr"><term>Maladie de Parkinson</term>
<term>Pathologie du   système nerveux</term>
<term>Algorithme</term>
<term>Base de données</term>
<term>Valeur prédictive</term>
<term>Sensibilité</term>
<term>Spécificité</term>
</keywords>
</textClass>
</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 non-specialists 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.</div>
</front>
</TEI>
<inist><standard h6="B"><pA><fA01 i1="01" i2="1"><s0>0885-3185</s0>
</fA01>
<fA03 i2="1"><s0>Mov. disord.</s0>
</fA03>
<fA05><s2>24</s2>
</fA05>
<fA06><s2>1</s2>
</fA06>
<fA08 i1="01" i2="1" l="ENG"><s1>Optimizing Algorithms to Identify Parkinson's Disease Cases Within an Administrative Database</s1>
</fA08>
<fA11 i1="01" i2="1"><s1>SZUMSKI (Nicholas R.)</s1>
</fA11>
<fA11 i1="02" i2="1"><s1>CHENG (Eric M.)</s1>
</fA11>
<fA14 i1="01"><s1>Department of Neurology, UCLA</s1>
<s2>Los Angeles, California</s2>
<s3>USA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</fA14>
<fA14 i1="02"><s1>Southwest Parkinson's Disease Research, Education, and Clinical Center (PADRECC)</s1>
<s2>Los Angeles, California</s2>
<s3>USA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</fA14>
<fA14 i1="03"><s1>Department of Neurology, VA Greater Los Angeles Healthcare System</s1>
<s2>Los Angeles, California</s2>
<s3>USA</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</fA14>
<fA20><s1>51-56</s1>
</fA20>
<fA21><s1>2009</s1>
</fA21>
<fA23 i1="01"><s0>ENG</s0>
</fA23>
<fA43 i1="01"><s1>INIST</s1>
<s2>20953</s2>
<s5>354000186473060060</s5>
</fA43>
<fA44><s0>0000</s0>
<s1>© 2009 INIST-CNRS. All rights reserved.</s1>
</fA44>
<fA45><s0>15 ref.</s0>
</fA45>
<fA47 i1="01" i2="1"><s0>09-0094106</s0>
</fA47>
<fA60><s1>P</s1>
</fA60>
<fA61><s0>A</s0>
</fA61>
<fA64 i1="01" i2="1"><s0>Movement disorders</s0>
</fA64>
<fA66 i1="01"><s0>USA</s0>
</fA66>
<fC01 i1="01" l="ENG"><s0>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 non-specialists 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.</s0>
</fC01>
<fC02 i1="01" i2="X"><s0>002B17</s0>
</fC02>
<fC02 i1="02" i2="X"><s0>002B17G</s0>
</fC02>
<fC03 i1="01" i2="X" l="FRE"><s0>Maladie de Parkinson</s0>
<s2>NM</s2>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="X" l="ENG"><s0>Parkinson disease</s0>
<s2>NM</s2>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="X" l="SPA"><s0>Parkinson enfermedad</s0>
<s2>NM</s2>
<s5>01</s5>
</fC03>
<fC03 i1="02" i2="X" l="FRE"><s0>Pathologie du   système nerveux</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="X" l="ENG"><s0>Nervous system diseases</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="X" l="SPA"><s0>Sistema nervioso patología</s0>
<s5>02</s5>
</fC03>
<fC03 i1="03" i2="X" l="FRE"><s0>Algorithme</s0>
<s5>09</s5>
</fC03>
<fC03 i1="03" i2="X" l="ENG"><s0>Algorithm</s0>
<s5>09</s5>
</fC03>
<fC03 i1="03" i2="X" l="SPA"><s0>Algoritmo</s0>
<s5>09</s5>
</fC03>
<fC03 i1="04" i2="X" l="FRE"><s0>Base de données</s0>
<s5>10</s5>
</fC03>
<fC03 i1="04" i2="X" l="ENG"><s0>Database</s0>
<s5>10</s5>
</fC03>
<fC03 i1="04" i2="X" l="SPA"><s0>Base dato</s0>
<s5>10</s5>
</fC03>
<fC03 i1="05" i2="X" l="FRE"><s0>Valeur prédictive</s0>
<s5>11</s5>
</fC03>
<fC03 i1="05" i2="X" l="ENG"><s0>Predictive value</s0>
<s5>11</s5>
</fC03>
<fC03 i1="05" i2="X" l="SPA"><s0>Valor predictivo</s0>
<s5>11</s5>
</fC03>
<fC03 i1="06" i2="X" l="FRE"><s0>Sensibilité</s0>
<s5>12</s5>
</fC03>
<fC03 i1="06" i2="X" l="ENG"><s0>Sensitivity</s0>
<s5>12</s5>
</fC03>
<fC03 i1="06" i2="X" l="SPA"><s0>Sensibilidad</s0>
<s5>12</s5>
</fC03>
<fC03 i1="07" i2="X" l="FRE"><s0>Spécificité</s0>
<s5>13</s5>
</fC03>
<fC03 i1="07" i2="X" l="ENG"><s0>Specificity</s0>
<s5>13</s5>
</fC03>
<fC03 i1="07" i2="X" l="SPA"><s0>Especificidad</s0>
<s5>13</s5>
</fC03>
<fC07 i1="01" i2="X" l="FRE"><s0>Pathologie de l'encéphale</s0>
<s5>37</s5>
</fC07>
<fC07 i1="01" i2="X" l="ENG"><s0>Cerebral disorder</s0>
<s5>37</s5>
</fC07>
<fC07 i1="01" i2="X" l="SPA"><s0>Encéfalo patología</s0>
<s5>37</s5>
</fC07>
<fC07 i1="02" i2="X" l="FRE"><s0>Syndrome extrapyramidal</s0>
<s5>38</s5>
</fC07>
<fC07 i1="02" i2="X" l="ENG"><s0>Extrapyramidal syndrome</s0>
<s5>38</s5>
</fC07>
<fC07 i1="02" i2="X" l="SPA"><s0>Extrapiramidal síndrome</s0>
<s5>38</s5>
</fC07>
<fC07 i1="03" i2="X" l="FRE"><s0>Maladie dégénérative</s0>
<s5>39</s5>
</fC07>
<fC07 i1="03" i2="X" l="ENG"><s0>Degenerative disease</s0>
<s5>39</s5>
</fC07>
<fC07 i1="03" i2="X" l="SPA"><s0>Enfermedad degenerativa</s0>
<s5>39</s5>
</fC07>
<fC07 i1="04" i2="X" l="FRE"><s0>Pathologie du   système nerveux central</s0>
<s5>40</s5>
</fC07>
<fC07 i1="04" i2="X" l="ENG"><s0>Central nervous system disease</s0>
<s5>40</s5>
</fC07>
<fC07 i1="04" i2="X" l="SPA"><s0>Sistema nervosio central patología</s0>
<s5>40</s5>
</fC07>
<fN21><s1>068</s1>
</fN21>
<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
</fN82>
</pA>
</standard>
<server><NO>PASCAL 09-0094106 INIST</NO>
<ET>Optimizing Algorithms to Identify Parkinson's Disease Cases Within an Administrative Database</ET>
<AU>SZUMSKI (Nicholas R.); CHENG (Eric M.)</AU>
<AF>Department of Neurology, UCLA/Los Angeles, California/Etats-Unis (1 aut., 2 aut.); Southwest Parkinson's Disease Research, Education, and Clinical Center (PADRECC)/Los Angeles, California/Etats-Unis (1 aut., 2 aut.); Department of Neurology, VA Greater Los Angeles Healthcare System/Los Angeles, California/Etats-Unis (1 aut., 2 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>Movement disorders; ISSN 0885-3185; Etats-Unis; Da. 2009; Vol. 24; No. 1; Pp. 51-56; Bibl. 15 ref.</SO>
<LA>Anglais</LA>
<EA>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 non-specialists 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.</EA>
<CC>002B17; 002B17G</CC>
<FD>Maladie de Parkinson; Pathologie du   système nerveux; Algorithme; Base de données; Valeur prédictive; Sensibilité; Spécificité</FD>
<FG>Pathologie de l'encéphale; Syndrome extrapyramidal; Maladie dégénérative; Pathologie du   système nerveux central</FG>
<ED>Parkinson disease; Nervous system diseases; Algorithm; Database; Predictive value; Sensitivity; Specificity</ED>
<EG>Cerebral disorder; Extrapyramidal syndrome; Degenerative disease; Central nervous system disease</EG>
<SD>Parkinson enfermedad; Sistema nervioso patología; Algoritmo; Base dato; Valor predictivo; Sensibilidad; Especificidad</SD>
<LO>INIST-20953.354000186473060060</LO>
<ID>09-0094106</ID>
</server>
</inist>
</record>

Pour manipuler ce document sous Unix (Dilib)

EXPLOR_STEP=$WICRI_ROOT/Wicri/Santé/explor/MovDisordV3/Data/PascalFrancis/Corpus

HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 001007 | SxmlIndent | more

HfdSelect -h $EXPLOR_AREA/Data/PascalFrancis/Corpus/biblio.hfd -nk 001007 | SxmlIndent | more

Pour mettre un lien sur cette page dans le réseau Wicri

{{Explor lien
   |wiki=    Wicri/Santé
   |area=    MovDisordV3
   |flux=    PascalFrancis
   |étape=   Corpus
   |type=    RBID
   |clé=     Pascal:09-0094106
   |texte=   Optimizing Algorithms to Identify Parkinson's Disease Cases Within an Administrative Database
}}

This area was generated with Dilib version V0.6.23.
Data generation: Sun Jul 3 12:29:32 2016. Site generation: Wed Feb 14 10:52:30 2024

	Movement Disorders (revue)
	Attention, ce site est en cours de développement ! Attention, site généré par des moyens informatiques à partir de corpus bruts. Les informations ne sont donc pas validées.

Movement Disorders (revue)

Optimizing Algorithms to Identify Parkinson's Disease Cases Within an Administrative Database

Optimizing Algorithms to Identify Parkinson's Disease Cases Within an Administrative Database

Source :

Descripteurs français

English descriptors

Abstract

Notice en format standard (ISO 2709)

Format Inist (serveur)

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri