Robot-assisted gait training on functional ambulation in patients with stroke: a retrospective real-world analysis across five robotic devices

 Have your competent? doctor check all these and report back which ones they have implemented and why. Oh NO, NOTHING WAS DONE! So, you DON'T have a functioning stroke doctor, do you?

robot-assisted gait training (28 posts to December 2018,but referring to research back to 2009)

Robot-assisted gait training on functional ambulation in patients with stroke: a retrospective real-world analysis across five robotic devices

• Dougho Park,
• Dahyeon Koo,
• Gwang-Min Park,
• Won Hyuk Chang,
• Yong Wook Kim,
• Seo Yeon Yoon,
• Deog Young Kim &
• Dae Hyun Kim 

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Abstract

Background

Robot-assisted gait training (RAGT) is increasingly used in stroke rehabilitation. However, its comparative effectiveness across device types remains unclear. This study explored the effectiveness of RAGT compared to conventional treatment using real-world clinical data.

Methods

This retrospective cohort study included 2,947 patients with stroke who received inpatient rehabilitation at a single center between 2015 and 2022. Patients were divided into RAGT and conventional treatment groups. Functional improvement was defined as a gain of ≥ 1 point in the Functional Ambulation Category. Multivariable logistic regression and classification and regression tree (CART) models were applied to identify predictors of favorable outcomes (effectors) and to explore clinical decision-making for device selection.

Results

The RAGT group showed poorer baseline function but had a similar proportion of effectors (58.0%) compared to the control group (62.5%). Overall, RAGT was not associated with effector outcome versus control. Among RAGT devices, Morning Walk (adjusted odds ratio [aOR] = 3.25; 95% confidence interval [CI]: 1.79–5.87) and Exowalk (aOR = 4.42; 95% CI: 1.84–10.64) were significantly associated with higher odds of effector status compared to control, whereas Erigo was associated with lower odds (aOR = 0.58; 95% CI: 0.35–0.96). CART models identified clinical features, such as the Berg Balance Scale, the Mini-Mental Status Examination, and the Fugl-Meyer Assessment, as key predictors of effector status and revealed patterns in robotic device allocation.

Conclusion

While RAGT was not superior to conventional treatment, certain devices were associated with improved gait function. These findings highlight the importance of tailoring robotic interventions.