TY - JOUR
T1 - Validation of a Parkinson Disease Predictive Model in a Population-Based Study
AU - Faust, Irene M.
AU - Racette, Brad A.
AU - Searles Nielsen, Susan
N1 - Publisher Copyright:
© 2020 Irene M. Faust et al.
PY - 2020
Y1 - 2020
N2 - Parkinson disease (PD) has a relatively long prodromal period that may permit early identification to reduce diagnostic testing for other conditions when patients are simply presenting with early PD symptoms, as well as to reduce morbidity from fall-related trauma. Earlier identification also could prove critical to the development of neuroprotective therapies. We previously developed a PD predictive model using demographic and Medicare claims data in a population-based case-control study. The area under the receiver-operating characteristic curve (AUC) indicated good performance. We sought to further validate this PD predictive model. In a randomly selected, population-based cohort of 115,492 Medicare beneficiaries aged 66-90 and without PD in 2009, we applied the predictive model to claims data from the prior five years to estimate the probability of future PD diagnosis. During five years of follow-up, we used 2010-2014 Medicare data to determine PD and vital status and then Cox regression to investigate whether PD probability at baseline was associated with time to PD diagnosis. Within a nested case-control sample, we calculated the AUC, sensitivity, and specificity. A total of 2,326 beneficiaries developed PD. Probability of PD was associated with time to PD diagnosis (p<0.001, hazard ratio = 13.5, 95% confidence interval (CI) 10.6-17.3 for the highest vs. lowest decile of probability). The AUC was 83.3% (95% CI 82.5%-84.1%). At the cut point that balanced sensitivity and specificity, sensitivity was 76.7% and specificity was 76.2%. In an independent sample of additional Medicare beneficiaries, we again applied the model and observed good performance (AUC = 82.2%, 95% CI 81.1%-83.3%). Administrative claims data can facilitate PD identification within Medicare and Medicare-aged samples.
AB - Parkinson disease (PD) has a relatively long prodromal period that may permit early identification to reduce diagnostic testing for other conditions when patients are simply presenting with early PD symptoms, as well as to reduce morbidity from fall-related trauma. Earlier identification also could prove critical to the development of neuroprotective therapies. We previously developed a PD predictive model using demographic and Medicare claims data in a population-based case-control study. The area under the receiver-operating characteristic curve (AUC) indicated good performance. We sought to further validate this PD predictive model. In a randomly selected, population-based cohort of 115,492 Medicare beneficiaries aged 66-90 and without PD in 2009, we applied the predictive model to claims data from the prior five years to estimate the probability of future PD diagnosis. During five years of follow-up, we used 2010-2014 Medicare data to determine PD and vital status and then Cox regression to investigate whether PD probability at baseline was associated with time to PD diagnosis. Within a nested case-control sample, we calculated the AUC, sensitivity, and specificity. A total of 2,326 beneficiaries developed PD. Probability of PD was associated with time to PD diagnosis (p<0.001, hazard ratio = 13.5, 95% confidence interval (CI) 10.6-17.3 for the highest vs. lowest decile of probability). The AUC was 83.3% (95% CI 82.5%-84.1%). At the cut point that balanced sensitivity and specificity, sensitivity was 76.7% and specificity was 76.2%. In an independent sample of additional Medicare beneficiaries, we again applied the model and observed good performance (AUC = 82.2%, 95% CI 81.1%-83.3%). Administrative claims data can facilitate PD identification within Medicare and Medicare-aged samples.
UR - http://www.scopus.com/inward/record.url?scp=85080977290&partnerID=8YFLogxK
U2 - 10.1155/2020/2857608
DO - 10.1155/2020/2857608
M3 - Article
C2 - 32148753
AN - SCOPUS:85080977290
SN - 2042-0080
VL - 2020
JO - Parkinson's Disease
JF - Parkinson's Disease
M1 - 2857608
ER -