Robotic Kinematic measures of the arm in chronic Stroke: part 2 – strong correlation with clinical outcome measures

Abstract

Background A detailed sensorimotor evaluation is essential in planning effective, individualized therapy post-stroke. Robotic kinematic assay may offer better accuracy and resolution to understand stroke recovery. Here we investigate the added value of distal wrist measurement to a proximal robotic kinematic assay to improve its correlation with clinical upper extremity measures in chronic stroke. Secondly, we compare linear and nonlinear regression models. Methods Data was sourced from a multicenter randomized controlled trial conducted from 2012 to 2016, investigating the combined effect of robotic therapy and transcranial direct current stimulation (tDCS). 24 kinematic metrics were derived from 4 shoulder-elbow tasks and 35 metrics from 3 wrist and forearm evaluation tasks. A correlation-based feature selection was performed, keeping only features substantially correlated with the target attribute (R > 0.5.) Nonlinear models took the form of a multilayer perceptron neural network: one hidden layer and one linear output. Results Shoulder-elbow metrics showed a significant correlation with the Fugl Meyer Assessment (upper extremity, FMA-UE), with a R = 0.82 (P < 0.001) for the linear model and R = 0.88 (P < 0.001) for the nonlinear model. Similarly, a high correlation was found for wrist kinematics and the FMA-UE (R = 0.91 (P < 0.001) and R = 0.92 (P < 0.001) for the linear and nonlinear model respectively). The combined analysis produced a correlation of R = 0.91 (P < 0.001) for the linear model and R = 0.91 (P < 0.001) for the nonlinear model. Conclusions Distal wrist kinematics were highly correlated to clinical outcomes, warranting future investigation to explore our nonlinear wrist model with acute or subacute stroke populations. Trial registration http://www.clinicaltrials.gov. Actual study start date September 2012. First registered on 15 November 2012. Retrospectively registered. Unique identifiers: NCT01726673 and NCT03562663.

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

“…Our findings are in agreement with the studies of Agrafiotis et al ( Agrafiotis et al, 2021 ) and Moretti et al ( Moretti et al, 2021 ), although they analyzed chronic stroke patients. Both studies found a significant correlation between RMK metrics and clinical outcomes (upper limb Fugl-Meyer Assessment ( Agrafiotis et al, 2021 ; Moretti et al, 2021 ), Motor Power ( Agrafiotis et al, 2021 ), NIH stroke scale ( Agrafiotis et al, 2021 ), modified Rankin scale ( Agrafiotis et al, 2021 ), Wolf Motor Function Test ( Moretti et al, 2021 ), Barthel Index ( Moretti et al, 2021 ), and Medical Research Council score ( Moretti et al, 2021 ) demonstrating that traditional stroke assessment scales can be accurately reproduced by robotic measurements. The outcomes of these studies pave the way for the use of RMK data to reduce the sample size needed for future clinical trials on chronic stroke patients ( Agrafiotis et al, 2021 ) and to objectively, quantitatively, and rapidly assess impairments in body function ( Moretti et al, 2021 ).…”

“…Both studies found a significant correlation between RMK metrics and clinical outcomes (upper limb Fugl-Meyer Assessment (Agrafiotis et al, 2021;Moretti et al, 2021), Motor Power (Agrafiotis et al, 2021), NIH stroke scale (Agrafiotis et al, 2021), modified Rankin scale (Agrafiotis et al, 2021), Wolf Motor Function Test (Moretti et al, 2021), Barthel Index (Moretti et al, 2021), and Medical Research Council score (Moretti et al, 2021) demonstrating that traditional stroke assessment scales can be accurately reproduced by robotic measurements. The outcomes of these studies pave the way for the use of RMK data to reduce the sample size needed for future clinical trials on chronic stroke patients (Agrafiotis et al, 2021) and to objectively, quantitatively, and rapidly assess impairments in body function (Moretti et al, 2021). However, most published studies on this topic examined the relationships between technology-based metrics and clinical assessment outcomes measured close in time (e.g., concurrently) (Krebs et al, 2014;Grimm et al, 2021;Olesh et al, 2014;Wang et al, 1109).…”

“…The latter is in accordance with the kinematic approach to identify how the central nervous system represents and implements the motor control strategies necessary to obtain the movements in stroke patients ( Micera et al, 2005 ). RMK data were also processed to predict the clinical assessment outcomes ( Krebs et al, 2014 ; Duret et al, 2019 ; Agrafiotis et al, 2021 ; Goffredo et al, 2021 ; Grimm et al, 2021 ; Moretti et al, 2021 ), considering the importance of biomarkers of neurorehabilitation outcomes for evidence-based practice ( Langhorne et al, 2009 ; Scott and &Dukelow, 2011 ; Duret et al, 2015 ; Franceschini et al, 2018 ). In this context, the Predict REcovery Potential (PREP2) tool is the predominant predictor of upper limb functional outcomes from clinical assessment, Magnetic Resonance Imaging (MRI) and Transcranial Magnetic Stimulation (TMS) biomarkers, with an impact on rehabilitation planning and realistic treatment goal setting ( Stinear et al, 2017 ).…”

“…Considering the importance of quantitative indicators of upper limb function, Krebs et al and Moretti et al found that RMK metrics from a 2D robot can be biomarkers of clinical outcomes registered on the same day ( Krebs et al, 2014 ; Moretti et al, 2021 ). Their findings were consistent with those of Grimm et al who analyzed exoskeleton-based kinematics ( Grimm et al, 2021 ).…”