A comparison of prediction approaches for identifying prodromal Parkinson disease

Authors

Mark N. Warden, Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, United States of America.
Susan Searles Nielsen, Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, United States of America.
Alejandra Camacho-Soto, Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, United States of America.
Roman Garnett, Department of Computer Science and Engineering, Washington University in Saint Louis, Saint Louis, Missouri, United States of America.
Brad A. Racette, Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, United States of America.

Document Type

Article

Abstract

Identifying people with Parkinson disease during the prodromal period, including via algorithms in administrative claims data, is an important research and clinical priority. We sought to improve upon an existing penalized logistic regression model, based on diagnosis and procedure codes, by adding prescription medication data or using machine learning. Using Medicare Part D beneficiaries age 66-90 from a population-based case-control study of incident Parkinson disease, we fit a penalized logistic regression both with and without Part D data. We also built a predictive algorithm using a random forest classifier for comparison. In a combined approach, we introduced the probability of Parkinson disease from the random forest, as a predictor in the penalized regression model. We calculated the receiver operator characteristic area under the curve (AUC) for each model. All models performed well, with AUCs ranging from 0.824 (simplest model) to 0.835 (combined approach). We conclude that medication data and random forests improve Parkinson disease prediction, but are not essential.

Medical Subject Headings

Aged; Aged, 80 and over; Case-Control Studies; Female; Humans; Male; Medicare; Middle Aged; Models, Theoretical; Parkinson Disease (diagnosis); Probability; Prodromal Symptoms; United States

Publication Date

1-1-2021

Publication Title

PloS one

E-ISSN

1932-6203

Volume

16

Issue

8

First Page

e0256592

PubMed ID

34437600

Digital Object Identifier (DOI)

10.1371/journal.pone.0256592

Recommended Citation

Warden, Mark N.; Searles Nielsen, Susan; Camacho-Soto, Alejandra; Garnett, Roman; and Racette, Brad A., "A comparison of prediction approaches for identifying prodromal Parkinson disease" (2021). Neurology. 1092.
https://scholar.barrowneuro.org/neurology/1092