Constructing Prediction Models for Freezing of Gait by Nomogram and Machine Learning: A Longitudinal Study.

ABSTRACT: Objectives: Although risk factors for freezing of gait (FOG) have been reported, there are still few prediction models based on cohorts that predict FOG. This 1-year longitudinal study was aimed to identify the clinical measurements closely linked with FOG in Chinese patients with Parkinson's disease (PD) and construct prediction models based on those clinical measurements using Cox regression and machine learning. Methods: The study enrolled 967 PD patients without FOG in the Hoehn and Yahr (H&Y) stage 1-3 at baseline. The development of FOG during follow-up was the end-point. Neurologists trained in movement disorders collected information from the patients on a PD medication regimen and their clinical characteristics. The cohort was assessed on the same clinical scales, an

SUBMITTER: Xu K 

PROVIDER: S-EPMC8686836 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature