600 Section - Leveraging the PREPARE Project to Model Disability Trajectories in Parkinson’s Disease: A ClinFIT-Based Multidimensional Longitudinal Analysis

Parkinson’s disease (PD) is a progressive neurodegenerative condition characterized by heterogeneous motor and non-motor manifestations that jointly contribute to disability progression. Traditional monitoring strategies, focused on motor symptoms, only partially capture the complexity of functional decline. Building on data collected within the PREPARE project, this study aimed to examine the evolution of disability in people with PD using the ClinFIT Generic-30, an ICF–based tool, and identify clinical and demographic predictors of disability progression, with particular attention to non-motor symptoms.

Design:

A single-centre retrospective cohort study was conducted. Clinical and functional data were extracted from the PREPARE database for 262 consecutive people with PD evaluated in routine clinical practice. Assessments were performed at baseline (T0), approximately 3 years (T1), and 6 years (T2) post-baseline. Functioning domains were analyzed longitudinally using the ClinFIT Generic-30 alongside standard clinical scales (UPDRS and NMSS). Regression analyses were used to identify independent predictors of disability progression, including age, sex, disease duration, Hoehn & Yahr stage, and comorbidity burden.

Results: ClinFIT scores showed a significant and progressive worsening of functioning over time across most domains, particularly mobility, self-care, domestic activities, and participation. Hoehn & Yahr stage was the strongest predictor of functional decline. Importantly, non-motor symptoms—including depression, anxiety, apathy, fatigue, daytime sleepiness, and urinary dysfunction—progressed significantly over time and independently predicted worsening disability beyond motor severity. Marked inter-individual variability in functional trajectories was observed, indicating heterogeneous patterns of disability progression

Conclusion:

Disability progression in PD is multidimensional and strongly influenced by non-motor symptoms and comorbidities. Integrating ClinFIT within large-scale datasets enables a more comprehensive, functioning-oriented monitoring of PD and supports big data approaches to model individualized disability trajectories. These findings highlight the need for multidimensional assessment frameworks to inform personalized rehabilitation strategies and prompt further research into early identification of high-risk functional phenotypes.