Long-Term Forecasting of a Motor Outcome Following Rehabilitation in Chronic Stroke via a Hierarchical Bayesian Model of Motor Learning

Background Given the heterogeneity of stroke, it is important to determine the best course of motor therapy for each patient, i.e., to personalize rehabilitation based on predictions of long-term outcomes. Here, we propose a Hierarchical Bayesian dynamical (i.e., state-space) model of motor learning to forecast long-term changes in a motor outcome due to rehabilitation in the chronic phase post-stroke. Methods The model incorporates the effects of clinician-supervised training, self-training, and forgetting. In addition, to improve forecasting early in rehabilitation, when data are sparse or unavailable, we use a hierarchical Bayesian structure, which incorporates prior information from similar patients. We use this dynamical model to re-analyze Motor Activity Log (MAL) data of participants with chronic stroke included in two clinical trials: 1) the DOSE trial, in which participants were assigned to a 0, 15, 30, or 60-hour dose condition (data of 40 participants analyzed), and 2) the EXCITE trial, in which participants were assigned a 60-hour dose, in either an immediate or a delayed condition (95 participants analyzed). Results For both datasets, the dynamical model accounts well for individual trajectory in the MAL during and outside of training and better fits the data than other simpler models without the effects of either supervised training, self-training or forgetting or (static) regression models. We then show how the model can be used to forecast the MAL of new participants up to 8 months ahead and how the hierarchical structure improves the accuracy of the predictions early in training when data are sparse. Finally, we verify that this model, despite its simplicity, can reproduce previous findings of the DOSE trial on the efficiency, efficacy, and retention of motor therapy. Conclusion In future work, such forecasting models can be simulated for different stages of recovery, dosages, and training schedules to optimize rehabilitation for each person. ### Competing Interest Statement The authors have declared no competing interest. ### Clinical Trial DOSE clinical trial ID [NCT01749358][1]. EXCITE clinical trial ID [NCT00057018][2] ### Funding Statement This work was funded by NIH grants R56 NS100528 and R21NS120274 to NS and P41-EB001978 and the Alfred E. Mann Institute at USC to DD. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Institutional Review Board of the Universty of Southern Californial gave ethical approval for the DOSE clinical trial. The Institutional Review Boards of the following seven participating sites gave ethical approvals for the EXCITE clinical trial: Emory University (Georgia) The Ohio State University (Ohio) University of Alabama at Birmingham (Alabama) University of Florida at Gainsville (Florida) University of Southern California (California) University of North Carolina at Chapel Hill (North Carolina) Wake Forest University (North Carolina) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT01749358&atom=%2Fmedrxiv%2Fearly%2F2022%2F10%2F21%2F2022.10.20.22280926.atom [2]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT00057018&atom=%2Fmedrxiv%2Fearly%2F2022%2F10%2F21%2F2022.10.20.22280926.atom