Can We Predict the Motor Performance of Patients With Parkinson's Disease Based on Their Symptomatology?

Karina Lebel; Christian Duval; Etienne Goubault; Sarah Bogard; Pierre J Blanchet

doi:10.3389/fbioe.2020.00189

Can We Predict the Motor Performance of Patients With Parkinson's Disease Based on Their Symptomatology?

Front Bioeng Biotechnol. 2020 Mar 24:8:189. doi: 10.3389/fbioe.2020.00189. eCollection 2020.

Authors

Karina Lebel^{1

2}, Christian Duval^{3

4}, Etienne Goubault^{3

4}, Sarah Bogard^{3

4}, Pierre J Blanchet^{5

6}

Affiliations

¹ Département de Génie électrique et de Génie Informatique, Faculté de Génie, Université de Sherbrooke, Sherbrooke, QC, Canada.
² Centre de Recherche sur le Vieillissement, Sherbrooke, QC, Canada.
³ Laboratoire de Simulation et Modélisation du Mouvement, École de Kinésiologie et des Sciences de l'activité physique, Université de Montréal, Montreal, QC, Canada.
⁴ Centre de Recherche Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada.
⁵ Faculté de Médecine Dentaire, Université de Montréal, Montreal, QC, Canada.
⁶ Centre Hospitalier de l'Université de Montréal (C.H.U. Montreal), Montreal, QC, Canada.

Abstract

Introduction: Parkinson's disease hinders the ability of a person to perform daily activities. However, the varying impact of specific symptoms and their interactions on a person's motor repertoire is not understood. The current study investigates the possibility to predict global motor disabilities based on the patient symptomatology and medication. Methods: A cohort of 115 patients diagnosed with Parkinson's disease (mean age = 67.0 ± 8.7 years old) participated in the study. Participants performed different tasks, including the Timed-Up & Go, eating soup and the Purdue Pegboard test. Performance on these tasks was judged using timing, number of errors committed, and count achieved. K-means method was used to cluster the overall performance and create different motor performance groups. Symptomatology was objectively assessed for each participant from a combination of wearable inertial sensors (bradykinesia, tremor, dyskinesia) and clinical assessment (rigidity, postural instability). A multinomial regression model was derived to predict the performance cluster membership based on the patients' symptomatology, socio-demographics information and medication. Results: Clustering exposed four distinct performance groups: normal behavior, slightly affected in fine motor tasks, affected only in TUG, and affected in all areas. The statistical model revealed that low to moderate level of dyskinesia increased the likelihood of being in the normal group. A rise in postural instability and rest tremor increase the chance to be affected in TUG. Finally, LEDD did not help distinguishing between groups, but the presence of Amantadine as part of the medication regimen appears to decrease the likelihood of being part of the groups affected in TUG. Conclusion: The approach allowed to demonstrate the potential of using clinical symptoms to predict the impact of Parkinson's disease on a person's mobility performance.

Keywords: K-means; Parkinson; clustering; mobility; motor impact.