Abstract
IntroductionConflicting results persist regarding the effectiveness of robotic-assisted gait training (RAGT) for functional gait recovery in post-stroke survivors. We used several machine learning algorithms to construct prediction models for the functional outcomes of robotic neurorehabilitation in adult patients.Methods and materialsData of 139 patients who underwent Lokomat training at Taipei Medical University Hospital were retrospectively collected. After screening for data completeness, records of 91 adult patients with acute or chronic neurological disorders were included in this study. Patient characteristics and quantitative data from Lokomat were incorporated as features to construct prediction models to explore early responses and factors associated with patient recovery.ResultsExperimental results using the random forest algorithm achieved the best area under the receiver operating characteristic curve of 0.9813 with data extracted from all sessions. Body weight (BW) support played a key role in influencing the progress of functional ambulation categories. The analysis identified negative correlations of BW support, guidance force, and days required to complete 12 Lokomat sessions with the occurrence of progress, while a positive correlation was observed with regard to speed.ConclusionsWe developed a predictive model for ambulatory outcomes based on patient characteristics and quantitative data on impairment reduction with early-stage robotic neurorehabilitation. RAGT is a customized approach for patients with different conditions to regain walking ability. To obtain a more-precise and clearer predictive model, collecting more RAGT training parameters and analyzing them for each individual disorder is a possible approach to help clinicians achieve a better understanding of the most efficient RAGT parameters for different patients.Trial registration: Retrospectively registered.
Highlights
Conflicting results persist regarding the effectiveness of robotic-assisted gait training (RAGT) for functional gait recovery in post-stroke survivors
We developed a predictive model for ambulatory outcomes based on patient characteristics and quan‐ titative data on impairment reduction with early-stage robotic neurorehabilitation
Significant differences observed in RAGT parameters in the two study groups Descriptive statistical analyses of continuous and categorical variables of the 91 patients are shown in Tables 1 and 2, respectively
Summary
Conflicting results persist regarding the effectiveness of robotic-assisted gait training (RAGT) for functional gait recovery in post-stroke survivors. Robotic-assisted gait training (RAGT) is expected to more effectively improve mobility, as it can provide a higher dose and more-intensive treatment than usual rehabilitation [5]. As early as 2009, randomized controlled trials showed that RAGT combined with regular physiotherapy was more effective for improving the functional ambulation capacity and neurological recovery than conventional therapy in patients after a subacute stroke [6]. A review article revealed that RAGT applications demonstrated a better effect than CGT in post-stroke patients [7]. Other studies demonstrated non-superior results of the effectiveness of RAGT for functional recovery of walking in survivors with different neurological disorders [9, 10]. Specific applications using RAGT devices to obtain optimal effects for patients remain unclear
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