scholarly journals Development and validation of a predictive model to identify patients at risk of severe COPD exacerbations using administrative claims data

2018 ◽  
Vol Volume 13 ◽  
pp. 2121-2130 ◽  
Author(s):  
Srinivas Annavarapu ◽  
Seth Goldfarb ◽  
Melissa Gelb ◽  
Chad Moretz ◽  
Andrew Renda ◽  
...  
Keyword(s):  
At Risk ◽  
Predictive Model ◽  
Claims Data ◽  
Administrative Claims ◽  
Administrative Claims Data ◽  
Copd Exacerbations ◽  
Patients At Risk ◽  
Development And Validation ◽  
Severe Copd

scholarly journals A predictive model to identify Parkinson disease from administrative claims data

Neurology ◽  
2017 ◽  
Vol 89 (14) ◽  
pp. 1448-1456 ◽  
Author(s):  
Susan Searles Nielsen ◽  
Mark N. Warden ◽  
Alejandra Camacho-Soto ◽  
Allison W. Willis ◽  
Brenton A. Wright ◽  
...  
Keyword(s):  
Parkinson Disease ◽  
Predictive Model ◽  
Claims Data ◽  
Demographic Data ◽  
Area Under The Curve ◽  
Rem Sleep Behavior Disorder ◽  
Administrative Claims ◽  
Sleep Behavior ◽  
Administrative Claims Data ◽  
Procedure Codes

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.

Sign in / Sign up

Export Citation Format

Share Document