Development and External Validation of Motor-intention Integrated Prediction Model for Upper Extremity Motor Recovery After Intention-driven Robotic-hand Training for Chronic Stroke.

Abstract

To derive and validate a prediction model for minimal clinically important differences (MCID) in upper extremity (UE) motor function after intention-driven robotic-hand training using residual voluntary EMG signals from affected UE. A prospective longitudinal multicenter cohort study. We collected pre-intervention candidate predictors: demographics, clinical characteristics, Fugl-Meyer assessment of UE (FMAUE), Action Research Arm Test scores, and motor-intention of flexor digitorum and extensor digitorum (ED) measured by EMG during maximal voluntary contraction (MVC). For EMG measures, recognizing challenges for stroke survivors to move paralyzed hand, peak signals were extracted from eight time-windows during MVC-EMG (0.1s-5s) to identify subjects' motor-intention. Classification And Regression Tree algorithm was employed to predict survivors with MCID of FMAUE. Relationship between predictors and motor-improvements was further investigated. Nine rehabilitation centers. Chronic stroke survivors (N=131), including 87 for Derivation-sample, and 44 for Validation-sample. All participants underwent 20-session robotic-hand training (40min/session, 3-5sessions/week). Prediction efficacies of models were assessed by area under the receiver operating characteristic curve (AUC). The best effective model was final model and validated using AUC and overall accuracy. The best model comprised FMAUE (cut-off score: 46) and peak activity of ED from one-second MVC-EMG (MVC-EMG 4.604 times higher than resting-EMG), which demonstrated significantly higher prediction accuracy (AUC: 0.807) than other time-windows or solely using clinical-scores (AUC: 0.595). In external validation, this model displayed robust prediction (AUC: 0.916). Significant quadratic relationship was observed between ED-EMG and FMAUE increases. This study presents a prediction model for intention-driven robotic-hand training in chronic stroke survivors. It highlights significance of capturing motor-intention through 1-second EMG-window as a predictor for MCID improvement in UE motor-function after 20-session robotic-training. Survivors in two conditions showed high percentage of clinical motor-improvement: moderate-to-high motor-intention and low-to-moderate function; as well as high-intention and high-function.