Multi-predictor modeling for predicting early Parkinson's disease and non-motor symptoms progression.

ABSTRACT:

Background

Identifying individuals with high-risk Parkinson's disease (PD) at earlier stages is an urgent priority to delay disease onset and progression. In the present study, we aimed to develop and validate clinical risk models using non-motor predictors to distinguish between early PD and healthy individuals. In addition, we constructed prognostic models for predicting the progression of non-motor symptoms [cognitive impairment, Rapid-eye-movement sleep Behavior Disorder (RBD), and depression] in de novo PD patients at 5 years of follow-up.

Methods

We retrieved the data from the Parkinson's Progression Markers Initiative (PPMI) database. After a backward variable selection approach to identify predictors, logistic regression analyses were applied for diagnosis mod

SUBMITTER: Dou K 

PROVIDER: S-EPMC9459236 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature