Optimizing Algorithms to Identify Parkinson's Disease Cases Within an Administrative Database
Identifieur interne : 001007 ( PascalFrancis/Corpus ); précédent : 001006; suivant : 001008Optimizing Algorithms to Identify Parkinson's Disease Cases Within an Administrative Database
Auteurs : Nicholas R. Szumski ; Eric M. ChengSource :
- Movement disorders [ 0885-3185 ] ; 2009.
Descripteurs français
- Pascal (Inist)
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 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.
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Pour connaître la documentation sur le format Inist Standard.
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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 |
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Pascal:09-0094106Le document en format XML
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<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>
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<ET>Optimizing Algorithms to Identify Parkinson's Disease Cases Within an Administrative Database</ET>
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<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>
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