A predictive model to identify Parkinson disease from administrative claims data

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.

Figure 1. Distribution of predicted probability of Parkinson disease (PD) by true PD case status

(A) Basic predictive model: predicted by age (2 linear splines), sex, race/ethnicity (7 categories), ever/never smoking, total number of unique ICD-9 diagnosis codes, constipation (ICD-9 564, 564.0, 564.00, 564.01, 564.02, 564.09), REM sleep behavior disorder (RBD) (ICD-9 327.42), and anosmia/hyposmia (included in ICD-9 781.1 as smell and taste disturbances). (B) Full predictive model: predicted by age (2 linear splines), sex, race/ethnicity (7 categories), ever/never smoking, total number of unique ICD-9 diagnosis codes, and 536 diagnosis or procedure codes including codes for RBD (ICD-9 327.42) and selected codes for constipation (table e-1).

Figure 2. Parkinson disease predictive model receiver operator characteristic (ROC) curve

Nonparametric ROC curve for the full predictive model: predicted by age (2 linear splines), sex, race/ethnicity (7 categories), ever/never smoking, total number of unique ICD-9 diagnosis codes, and 536 diagnosis or procedure codes including codes for RBD (ICD-9 327.42) and selected codes for constipation (table e-1). RBD = REM sleep behavior disorder.