BACKGROUND:Robotic walking training improves probability to reach an autonomous walking in non-ambulant patients affected by subacute stroke. However, little information is available regarding the prognostic factors for identifying best responder patients. The purpose of the present study is therefore to investigate the clinical features of patients with subacute stroke that might benefit more from robotic walking therapy. METHODS:One hundred subacute inpatients randomized inrobotic or conventional gait training were assessed at baseline and after 4 weeks of training performed 5 times per week. Forward Binary Logistic Regression was performed using functional ambulation category (FAC) as dependent variable and as independent variables: trunk function (trunk control test), global ability (Barthel Index), age, sex, time from stroke and beginning of rehabilitation, side and type of stroke, and in the first analysis also type of treatment. RESULTS: The parameters that have a significant effect on the FAC-score at discharge were a higher BI-score at admission, a higher TCT-score at admission, a short time from the ictus and a robotic therapy. The variance explained by these four factors was 78%. When the two groups were separately analysed for type of treatment, a higher BI-score and a short time from stroke resulted in good prognosis for conventional therapy, whereas only a high TCT-score improved efficacy of robotic training. CONCLUSION: Efficacy of robotic walking training was not associated with global ability at admission. Hence, more severely disabled patients may obtain greater benefit from robotic training, independently by other factors, except the need of a residual trunk control that was identified as a good prognostic factor for robotic walking training.
RCT Entities:
BACKGROUND: Robotic walking training improves probability to reach an autonomous walking in non-ambulant patients affected by subacute stroke. However, little information is available regarding the prognostic factors for identifying best responder patients. The purpose of the present study is therefore to investigate the clinical features of patients with subacute stroke that might benefit more from robotic walking therapy. METHODS: One hundred subacute inpatients randomized in robotic or conventional gait training were assessed at baseline and after 4 weeks of training performed 5 times per week. Forward Binary Logistic Regression was performed using functional ambulation category (FAC) as dependent variable and as independent variables: trunk function (trunk control test), global ability (Barthel Index), age, sex, time from stroke and beginning of rehabilitation, side and type of stroke, and in the first analysis also type of treatment. RESULTS: The parameters that have a significant effect on the FAC-score at discharge were a higher BI-score at admission, a higher TCT-score at admission, a short time from the ictus and a robotic therapy. The variance explained by these four factors was 78%. When the two groups were separately analysed for type of treatment, a higher BI-score and a short time from stroke resulted in good prognosis for conventional therapy, whereas only a high TCT-score improved efficacy of robotic training. CONCLUSION: Efficacy of robotic walking training was not associated with global ability at admission. Hence, more severely disabled patients may obtain greater benefit from robotic training, independently by other factors, except the need of a residual trunk control that was identified as a good prognostic factor for robotic walking training.