Background: Clinical progression rate is the typical primary endpoint measure in progressive supranuclear palsy (PSP) clinical trials. Objectives: This longitudinal multicohort study investigated whether baseline clinical severity and regional brain atrophy could predict clinical progression in PSP–Richardson's syndrome (PSP-RS). Methods: PSP-RS patients (n = 309) from the placebo arms of clinical trials (NCT03068468, NCT01110720, NCT02985879, NCT01049399) and DescribePSP cohort were included. We investigated associations of baseline clinical and volumetric magnetic resonance imaging (MRI) data with 1-year longitudinal PSP rating scale (PSPRS) change. Machine learning (ML) models were tested to predict individual clinical trajectories. Results: PSP-RS patients showed a mean PSPRS score increase of 10.3 points/yr. The frontal lobe volume showed the strongest association with subsequent clinical progression (β: −0.34, P < 0.001). However, ML models did not accurately predict individual progression rates (R2 <0.15). Conclusions: Baseline clinical severity and brain atrophy could not predict individual clinical progression, suggesting no need for MRI-based stratification of patients in future PSP trials. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
Brain Atrophy Does Not Predict Clinical Progression in Progressive Supranuclear Palsy
Quattrone, Andrea;
2025-01-01
Abstract
Background: Clinical progression rate is the typical primary endpoint measure in progressive supranuclear palsy (PSP) clinical trials. Objectives: This longitudinal multicohort study investigated whether baseline clinical severity and regional brain atrophy could predict clinical progression in PSP–Richardson's syndrome (PSP-RS). Methods: PSP-RS patients (n = 309) from the placebo arms of clinical trials (NCT03068468, NCT01110720, NCT02985879, NCT01049399) and DescribePSP cohort were included. We investigated associations of baseline clinical and volumetric magnetic resonance imaging (MRI) data with 1-year longitudinal PSP rating scale (PSPRS) change. Machine learning (ML) models were tested to predict individual clinical trajectories. Results: PSP-RS patients showed a mean PSPRS score increase of 10.3 points/yr. The frontal lobe volume showed the strongest association with subsequent clinical progression (β: −0.34, P < 0.001). However, ML models did not accurately predict individual progression rates (R2 <0.15). Conclusions: Baseline clinical severity and brain atrophy could not predict individual clinical progression, suggesting no need for MRI-based stratification of patients in future PSP trials. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


