Baseline robot-measured kinematic metrics predict discharge rehabilitation outcomes in individuals with subacute stroke

Michela Goffredo; Stefania Proietti; Sanaz Pournajaf; Daniele Galafate; Matteo Cioeta; Domenica Le Pera; Federico Posteraro; Marco Franceschini

doi:10.3389/fbioe.2022.1012544

Baseline robot-measured kinematic metrics predict discharge rehabilitation outcomes in individuals with subacute stroke

Front Bioeng Biotechnol. 2022 Dec 6:10:1012544. doi: 10.3389/fbioe.2022.1012544. eCollection 2022.

Authors

Michela Goffredo¹, Stefania Proietti^{2

3}, Sanaz Pournajaf¹, Daniele Galafate¹, Matteo Cioeta¹, Domenica Le Pera¹, Federico Posteraro⁴, Marco Franceschini^{1

3}

Affiliations

¹ Department of Neurological and Rehabilitation Sciences, IRCCS San Raffaele Roma, Rome, Italy.
² Unit of Clinical and Molecular Epidemiology, IRCCS San Raffaele Roma, Rome, Italy.
³ Department of Human Sciences and Promotion of the Quality of Life, San Raffaele University, Rome, Italy.
⁴ Rehabilitation Department, Versilia Hospital, Camaiore, Italy.

Abstract

Background: The literature on upper limb robot-assisted therapy showed that robot-measured metrics can simultaneously predict registered clinical outcomes. However, only a limited number of studies correlated pre-treatment kinematics with discharge motor recovery. Given the importance of predicting rehabilitation outcomes for optimizing physical therapy, a predictive model for motor recovery that incorporates multidirectional indicators of a patient's upper limb abilities is needed. Objective: The aim of this study was to develop a predictive model for rehabilitation outcome at discharge (i.e., muscle strength assessed by the Motricity Index of the affected upper limb) based on multidirectional 2D robot-measured kinematics. Methods: Re-analysis of data from 66 subjects with subacute stroke who underwent upper limb robot-assisted therapy with an end-effector robot was performed. Two least squares error multiple linear regression models for outcome prediction were developed and differ in terms of validation procedure: the Split Sample Validation (SSV) model and the Leave-One-Out Cross-Validation (LOOCV) model. In both models, the outputs were the discharge Motricity Index of the affected upper limb and its sub-items assessing elbow flexion and shoulder abduction, while the inputs were the admission robot-measured metrics. Results: The extracted robot-measured features explained the 54% and 71% of the variance in clinical scores at discharge in the SSV and LOOCV validation procedures respectively. Normalized errors ranged from 22% to 35% in the SSV models and from 20% to 24% in the LOOCV models. In all models, the movement path error of the trajectories characterized by elbow flexion and shoulder extension was the significant predictor, and all correlations were significant. Conclusion: This study highlights that motor patterns assessed with multidirectional 2D robot-measured metrics are able to predict clinical evalutation of upper limb muscle strength and may be useful for clinicians to assess, manage, and program a more specific and appropriate rehabilitation in subacute stroke patients.

Keywords: biomarkers; kinematics; motor recovery; predictors; robot-assisted therapy; stroke; upper extremity.