Classifying Idiopathic Rapid Eye Movement Sleep Behavior Disorder, Controls, and Mild Parkinson's Disease Using Gait Parameters

Background Subtle gait changes associated with idiopathic rapid eye movement sleep behavior disorder (iRBD) could allow early detection of subjects with future synucleinopathies. Objective The aim of this study was to create a multiclass model, using statistical learning from probability distribution of gait parameters, to distinguish between patients with iRBD, healthy control subjects (HCs), and patients with Parkinson's disease (PD). Methods Gait parameters were collected in 21 participants with iRBD, 21 with PD, and 21 HCs, matched for age, sex, and education level. Lasso sparse linear regression explored gait features able to classify the three groups. Results The final model classified iRBD from HCs and from patients with PD equally well, with 95% accuracy, 100% sensitivity, and 90% specificity. Conclusions Gait parameters and a pretrained statistical model can robustly distinguish participants with iRBD from HCs and patients with PD. This could be used to screen subjects with future synucleinopathies in the general population and to identify a conversion threshold to PD. (c) 2022 International Parkinson and Movement Disorder Society

Automated summary

This study successfully developed a multiclass model using statistical learning to distinguish patients with idiopathic rapid eye movement sleep behavior disorder (iRBD), healthy control subjects (HCs), and patients with Parkinson's disease (PD). The results demonstrated that gait parameters and a pretrained statistical model can robustly differentiate participants with iRBD from HCs and patients with PD.