Externally validated model predicting gait independence after stroke showed fair performance and improved after updating

Logistic regression analysis and machine learning for predicting post-stroke gait independence: a retrospective study

This study investigated whether machine learning (ML) has better predictive accuracy than logistic regression analysis (LR) for gait independence at discharge in subacute stroke patients (n = 843) who could not walk independently at admission. We developed prediction models using LR and five ML algorithms-specifically, the decision tree (DT), support vector machine, artificial neural network, ensemble learning, and k-nearest neighbor methods. Functional Independence Measure sub-items were used to evaluate the ability to walk independently. Model predictive accuracies were evaluated using areas under receiver operating characteristic curves (AUCs) as well as accuracy, precision, recall, F1 score, and specificity. The AUC for DT (0.812) was significantly lower than those for the other algorithms (p < 0.01); however, the AUC for LR (0.895) did not differ significantly from those for the other models (0.893-0.903). Other performance metrics showed no substantial differences between LR and ML algorithms. In conclusion, the DT algorithm had significantly low predictive accuracy, and LR showed no significant difference in predictive accuracy compared with the other ML algorithms. As its predictive accuracy is similar to that of ML, LR can continue to be used for predicting the prognosis of gait independence, with additional advantages of being easily understandable and manually computable.

Motor rehabilitation after stroke: European Stroke Organisation (ESO) consensus-based definition and guiding framework

PURPOSE

To propose a consensus-based definition and framework for motor rehabilitation after stroke.

METHODS

An expert European working group reviewed the literature, attaining internal consensus after external feedback.

FINDINGS

Motor rehabilitation is defined as a process that engages people with stroke to benefit their motor function, activity capacity and performance in daily life. It is necessary for people with residual motor disability whose goal is to enhance their functioning, independence and participation. Motor rehabilitation operates through learning- and use-dependent mechanisms. The trajectory of motor recovery varies across patients and stages of recovery. Early behavioral restitution of motor function depends on spontaneous biological mechanisms. Further improvements in activities of daily living are achieved by compensations. Motor rehabilitation is guided by regular assessment of motor function and activity using consensus-based measures, including patient-reported outcomes. Results are discussed with the patient and their carers to set personal goals. During motor rehabilitation patients learn to optimize and adapt their motor, sensory and cognitive functioning through appropriately dosed repetitive, goal-oriented, progressive, task- and context-specific training. Motor rehabilitation supports people with stroke to maximize health, well-being and quality of life. The framework describes the International Classification of Functioning, Disability and Health in the context of stroke, describes neurobiological mechanisms of behavioral restitution and compensation, and summarizes recommendations for clinical assessment, prediction tools, and motor interventions with strong recommendations from clinical practice guidelines (2016-2022).

CONCLUSIONS

This definition and framework may guide clinical educators, inform clinicians on current recommendations and guidelines, and identify gaps in the evidence base.

Predictive discriminative accuracy of walking abilities at discharge for community ambulation levels at 6 months post-discharge among inpatients with subacute stroke

[Purpose] This study aimed to compare the predictive accuracy of walking ability at discharge among subacute stroke inpatients at 6 months post-discharge in terms of community ambulation level and establish optimal cut-off values. [Participants and Methods] This prospective observational study included 78 patients who completed follow-up assessments. Patients were classified into three groups based on the Modified Functional Walking Category (household/most limited community walkers, least limited community walkers, and unlimited community walkers) obtained by telephone survey at 6 months post-discharge. Predictive accuracy and cut-off values for discriminating among groups were calculated from 6-minute walking distance and comfortable walking speed measured at the time of discharge using receiver operating characteristic curves. [Results] Between household/most limited and least limited community walkers, 6-minute walking distance and comfortable walking speed offered similar predictive accuracy (area under the curve, 0.6-0.7), with cut-off values of 195 m and 0.56 m/s, respectively. Between least limited and unlimited community walkers, the areas under the curve were 0.896 for 6-minute walking distance and 0.844 for comfortable walking speed, with cut-off values of 299 m and 0.94 m/s, respectively. [Conclusion] Walking endurance and walking speed among inpatients with subacute stroke provided superior predictive accuracy for unlimited community walkers at 6 months post-discharge.

Walk the Talk: Current Evidence for Walking Recovery After Stroke, Future Pathways and a Mission for Research and Clinical Practice

Achieving safe, independent, and efficient walking is a top priority for stroke survivors to enable quality of life and future health. This narrative review explores the state of the science in walking recovery after stroke and potential for development. The importance of targeting walking capacity and performance is explored in relation to individual stroke survivor gait recovery, applying a common language, measurement, classification, prediction, current and future intervention development, and health care delivery. Findings are summarized in a model of current and future stroke walking recovery research and a mission statement is set for researchers and clinicians to drive the field forward to improve the lives of stroke survivors and their carers.

External Validation of the Early Prediction of Functional Outcome After Stroke Prediction Model for Independent Gait at 3 Months After Stroke

Introduction

The Early Prediction of Functional Outcome after Stroke (EPOS) model for independent gait is a tool to predict between days 2 and 9 poststroke whether patients will regain independent gait 6 months after stroke. External validation of the model is important to determine its clinical applicability and generalizability by testing its performance in an independent cohort. Therefore, this study aimed to perform a temporal and geographical external validation of the EPOS prediction model for independent gait after stroke but with the endpoint being 3 months instead of the original 6 months poststroke.

Methods

Two prospective longitudinal cohort studies consisting of patients with first-ever stroke admitted to a Swiss hospital stroke unit. Sitting balance and strength of the paretic leg were tested at days 1 and 8 post-stroke in Cohort I and at days 3 and 9 in Cohort II. Independent gait was assessed 3 months after symptom onset. The performance of the model in terms of discrimination (area under the receiver operator characteristic (ROC) curve; AUC), classification, and calibration was assessed.

Results

In Cohort I [N = 39, median age: 74 years, 33% women, median National Institutes of Health Stroke Scale (NIHSS) 9], the AUC (95% confidence interval (CI)] was 0.675 (0.510, 0.841) on day 1 and 0.921 (0.811, 1.000) on day 8. For Cohort II (N = 78, median age: 69 years, 37% women, median NIHSS 8), this was 0.801 (0.684, 0.918) on day 3 and 0.846 (0.741, 0.951) on day 9.

Discussion and Conclusion

External validation of the EPOS prediction model for independent gait 3 months after stroke resulted in an acceptable performance from day 3 onward in mild-to-moderately affected patients with first-ever stroke without severe prestroke disability. The impact of applying this model in clinical practice should be investigated within this subgroup of patients with stroke. To improve the generalizability of patients with recurrent stroke and those with more severe, neurological comorbidities, the performance of the EPOS model within these patients should be determined across different geographical areas.