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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.
GLOSSARY
- AUC=
- area under the receiver operator characteristic curve;
- BASF=
- beneficiary annual summary file;
- CPT=
- Current Procedural Terminology;
- ICD-9=
- International Classification of Diseases–9;
- PD=
- Parkinson disease;
- RBD=
- REM sleep behavior disorder
Footnotes
Go to Neurology.org for full disclosures. Funding information and disclosures deemed relevant by the authors, if any, are provided at the end of the article.
Supplemental data at Neurology.org
- Received November 19, 2016.
- Accepted in final form July 11, 2017.
- © 2017 American Academy of Neurology
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