A predictive model to identify Parkinson disease from administrative claims data

Susan Searles Nielsen, From the Department of Neurology (S.S.N., M.N.W., A.C.-S., B.A.W., B.A.R.), Washington University School of Medicine, St. Louis, MO; Departments of Neurology and Biostatistics and Epidemiology (A.W.W.), University of Pennsylvania School of Medicine, Philadelphia; and School of Public Health, Faculty of Health Sciences (B.A.R.), University of the Witwatersrand, Parktown, South Africa.
Mark N. Warden, From the Department of Neurology (S.S.N., M.N.W., A.C.-S., B.A.W., B.A.R.), Washington University School of Medicine, St. Louis, MO; Departments of Neurology and Biostatistics and Epidemiology (A.W.W.), University of Pennsylvania School of Medicine, Philadelphia; and School of Public Health, Faculty of Health Sciences (B.A.R.), University of the Witwatersrand, Parktown, South Africa.
Alejandra Camacho-Soto, From the Department of Neurology (S.S.N., M.N.W., A.C.-S., B.A.W., B.A.R.), Washington University School of Medicine, St. Louis, MO; Departments of Neurology and Biostatistics and Epidemiology (A.W.W.), University of Pennsylvania School of Medicine, Philadelphia; and School of Public Health, Faculty of Health Sciences (B.A.R.), University of the Witwatersrand, Parktown, South Africa.
Allison W. Willis, From the Department of Neurology (S.S.N., M.N.W., A.C.-S., B.A.W., B.A.R.), Washington University School of Medicine, St. Louis, MO; Departments of Neurology and Biostatistics and Epidemiology (A.W.W.), University of Pennsylvania School of Medicine, Philadelphia; and School of Public Health, Faculty of Health Sciences (B.A.R.), University of the Witwatersrand, Parktown, South Africa.
Brenton A. Wright, From the Department of Neurology (S.S.N., M.N.W., A.C.-S., B.A.W., B.A.R.), Washington University School of Medicine, St. Louis, MO; Departments of Neurology and Biostatistics and Epidemiology (A.W.W.), University of Pennsylvania School of Medicine, Philadelphia; and School of Public Health, Faculty of Health Sciences (B.A.R.), University of the Witwatersrand, Parktown, South Africa.
Brad A. Racette, From the Department of Neurology (S.S.N., M.N.W., A.C.-S., B.A.W., B.A.R.), Washington University School of Medicine, St. Louis, MO; Departments of Neurology and Biostatistics and Epidemiology (A.W.W.), University of Pennsylvania School of Medicine, Philadelphia; and School of Public Health, Faculty of Health Sciences (B.A.R.), University of the Witwatersrand, Parktown, South Africa. racetteb@wustl.edu.

Document Type

Article

Abstract

OBJECTIVE: To use administrative medical claims data to identify patients with incident Parkinson disease (PD) prior to diagnosis. METHODS: Using a population-based case-control study of incident PD in 2009 among Medicare beneficiaries aged 66-90 years (89,790 cases, 118,095 controls) and the elastic net algorithm, we developed a cross-validated model for predicting PD using only demographic data and 2004-2009 Medicare claims data. We then compared this model to more basic models containing only demographic data and diagnosis codes for constipation, taste/smell disturbance, and REM sleep behavior disorder, using each model's receiver operator characteristic area under the curve (AUC). RESULTS: We observed all established associations between PD and age, sex, race/ethnicity, tobacco smoking, and the above medical conditions. A model with those predictors had an AUC of only 0.670 (95% confidence interval [CI] 0.668-0.673). In contrast, the AUC for a predictive model with 536 diagnosis and procedure codes was 0.857 (95% CI 0.855-0.859). At the optimal cut point, sensitivity was 73.5% and specificity was 83.2%. CONCLUSIONS: Using only demographic data and selected diagnosis and procedure codes readily available in administrative claims data, it is possible to identify individuals with a high probability of eventually being diagnosed with PD.

Medical Subject Headings

Aged; Aged, 80 and over; Case-Control Studies; Female; Humans; Male; Medicare (statistics & numerical data); Olfaction Disorders (etiology); Parkinson Disease (complications, diagnosis, epidemiology); Predictive Value of Tests; ROC Curve; Retrospective Studies; Sleep Wake Disorders (etiology); United States (epidemiology)

Publication Date

10-3-2017

Publication Title

Neurology

E-ISSN

1526-632X

Volume

89

Issue

14

First Page

1448

Last Page

1456

PubMed ID

28864676

Digital Object Identifier (DOI)

10.1212/WNL.0000000000004536

Link to Full Text

Share

COinS