Modeling and validation in Parkinson's disease patients with frailty.

ABSTRACT:

Introduction

Parkinson's disease (PD) is the second most common neurodegenerative disorder. The risk of frailty is significantly higher in patients with PD than in age-matched individuals without PD. This study aimed to develop a machine learning-based predictive model for frailty in PD.

Methods

We conducted a cross-sectional study of early- and middle-stage PD patients recruited from June 2024 to June 2025 at Shenzhen People's Hospital. Frailty was assessed using the Fried criteria (five components: gait speed, grip strength, physical activity, fatigue, and weight loss). A total of 42 demographic and clinical variables, including disease history, Montreal cognitive assessment (MoCA), and unified Parkinson's disease rating scale (MDS-UPDRS) scores, were collected and compare

SUBMITTER: Li G 

PROVIDER: S-EPMC12719481 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature