Prediction of individual progression rate in Parkinson's disease using clinical measures and biomechanical measures of gait and postural stability

TL;DR

This study develops machine learning models using clinical and biomechanical gait measures to predict individual Parkinson's disease progression over two years, aiding personalized prognosis and clinical trial enrichment.

Contribution

It introduces a neural network model that effectively predicts PD progression rate using combined clinical and biomechanical data, with high accuracy and explained variance.

Findings

Neural network achieved 71% PPV for fast progressors.

Model explained 37% of variance in progression rate.

Machine learning can predict individual PD progression effectively.

Abstract

Parkinson's disease (PD) is a common neurological disorder characterized by gait impairment. PD has no cure, and an impediment to developing a treatment is the lack of any accepted method to predict disease progression rate. The primary aim of this study was to develop a model using clinical measures and biomechanical measures of gait and postural stability to predict an individual's PD progression over two years. Data from 160 PD subjects were utilized. Machine learning models, including XGBoost and Feed Forward Neural Networks, were developed using extensive model optimization and cross-validation. The highest performing model was a neural network that used a group of clinical measures, achieved a PPV of 71% in identifying fast progressors, and explained a large portion (37%) of the variance in an individual's progression rate on held-out test data. This demonstrates the potential to predict individual PD progression rate and enrich trials by analyzing clinical and biomechanical measures with machine learning.