Do predictors of motor recovery differ between robotic and conventional post-stroke rehabilitation?

BACKGROUND: While research on robotic rehabilitation has largely focused on evaluating device effectiveness, there remains a clear need to investigate patient characteristics that predict response to robot-assisted therapy. Recent advancements in machine learning (ML) techniques support such investigations by enabling the development of data-driven tools to assist in selecting appropriate rehabilitation treatments. This study aimed to develop two ML models, predicting two motor outcomes each, following either robot-assisted or conventional upper limb rehabilitation in post-stroke patients. It further sought to compare how baseline predictors contributed differently across the two treatment modalities. METHODS: We conducted a retrospective analysis of data from a previous randomized controlled trial evaluating robotic upper limb rehabilitation in post-stroke patients. Four ML algorithms were trained and validated using a nested cross-validation framework, with models developed separately for robotic and conventional treatment subgroups. Baseline predictors were used to estimate two post-treatment motor outcomes: Fugl-Meyer Assessment (FMA) and Action Research Arm Test (ARAT). SHAP analyses were performed to assess the contribution of each predictor to the models. RESULTS: After data cleaning, 99 patients in the conventional group and 91 in the robotic group were included. Prediction errors for ARAT score after training (expressed in median and absolute error) were 5.0 [6.0] in the robotic group and 3.0 [6.0] in the conventional group. For FMA prediction, results were 5.0 [5.0] and 5.0 [7.0], respectively. Baseline FMA was a strong predictor of ARAT outcomes in both groups. For FMA prediction, the presence of neglect emerged as more influential in the robotic group. Age was a key predictor of both outcomes, but only in the conventional group. CONCLUSIONS: The differing contributions of baseline predictors across treatment types provide clinically meaningful insights and support the development of clinical decision support systems aimed at optimizing rehabilitation strategies based on individual patient characteristics.