Prognostic Modeling of Parkinson's Disease Progression Using Early Longitudinal Patterns of Change.

ABSTRACT:

Background

Predicting Parkinson's disease (PD) progression may enable better adaptive and targeted treatment planning.

Objective

Develop a prognostic model using multiple, easily acquired longitudinal measures to predict temporal clinical progression from Hoehn and Yahr (H&Y) stage 1 or 2 to stage 3 in early PD.

Methods

Predictive longitudinal measures of PD progression were identified by the joint modeling method. Measures were extracted by multivariate functional principal component analysis methods and used as covariates in Cox proportional hazards models. The optimal model was developed from the Parkinson's Progression Marker Initiative (PPMI) data set and confirmed with external validation from the Longitudinal and Biomarker Study in PD (LABS-PD) study.

Result

SUBMITTER: Ren X 

PROVIDER: S-EPMC8688189 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature