A predictive model to identify Parkinson disease from administrative claims data

Susan Searles Nielsen, Mark N. Warden, Alejandra Camacho-Soto, Allison W. Willis, Brenton A. Wright, Brad A. Racette

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

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.

Original languageEnglish
Pages (from-to)1448-1456
Number of pages9
JournalNeurology
Volume89
Issue number14
DOIs
StatePublished - Oct 3 2017

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