A predictive model to identify Parkinson disease from administrative claims data.

Related Articles

A predictive model to identify Parkinson disease from administrative claims data.

Neurology. 2017 Sep 01;:

Authors: Searles Nielsen S, Warden MN, Camacho-Soto A, Willis AW, Wright BA, Racette BA

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.

PMID: 28864676 [PubMed - as supplied by publisher]

from # All Medicine by Alexandros G. Sfakianakis via Alexandros G.Sfakianakis on Inoreader http://ift.tt/2wy54S4
via IFTTT