Predictive Value of Upper Extremity Outcome Measures After Stroke-A Systematic Review and Metaregression Analysis

Predicting self-perceived manual ability at three and six months after stroke: A prospective longitudinal study

BACKGROUND

Recovery of manual ability is a critical issue in rehabilitation. Currently, little is known regarding the baseline predictors of self-perceived manual ability, which could capture information on individual's perceived functional ability, especially in carrying-out routine tasks outside clinical settings.

OBJECTIVE

To identify baseline predictors, which can be easily obtained within clinical settings, of self-perceived manual ability at three and six months after discharge from a stroke unit.

METHODS

A 6-month longitudinal study was carried-out. Participants were recruited from a stroke unit of a public hospital. The dependent outcome was self-perceived manual ability, and the following predictors were investigated: age, stroke severity, upper-limb motor impairments, cognitive function, muscle strength, and functional capacity. Linear regression analyses were employed to identify multivariate predictors of manual ability at three and six months after discharge (α=5%).

RESULTS

Participated 131 individuals, 69 women (mean age of 60 years). Regression analyses revealed that stroke severity and age accounted for 31% and 47% of the variance in manual ability at three and six months after stroke, respectively. Stroke severity was the best predictor of manual ability at three (R2=29%; F=44.7; p<0.0001) and six months (R2=45%; F=88.2; p<0.0001) after stroke, respectively.

CONCLUSION

Stroke severity showed to be the best predictor of manual ability at both three and six months after stroke. Although significant, age added little to the explained variance.

Predictors of manual dexterity at 3 and 6 months after stroke: integration of clinical, neurophysiological, and neuroimaging factors

This retrospective study aimed to predict dexterity at 3 and 6 months post-stroke by integrating clinical, neurophysiological, and neuroimaging factors. We included 126 patients with first-ever, unilateral, and supratentorial stroke. Demographic, stroke characteristics, and initial clinical assessment variables [Mini-mental state examination and Fugl-Meyer Assessment Upper Extremity (FMA-UE)] were evaluated 2 weeks after stroke. Dexterity, measured using the Manual Function Test (MFT) hand subscore, was the primary outcome. The neurophysiological variables, upper limb somatosensory evoked potential (SEP) and motor evoked potential (MEP), were assessed 2 weeks post-stroke. The neuroimaging variable, fractional anisotropy (FA) of the corticospinal tract (CST), was assessed 3 weeks post-stroke. Multiple regression analysis revealed significant predictors for improved dexterity at 3 and 6 months post-stroke, including younger age, higher FMA-UE score, presence of waveforms in the SEP and MEP, and higher FA values in the CST (adjusted R 2  = 0.776, P  < 0.001 at 3 months; adjusted R 2  = 0.668, P  < 0.001 at 6 months; where MEP, SEP, and FA accounted together for an additional 0.079 and 0.166 of variance beyond age and FMA-UE, respectively). Subgroup analysis was conducted by categorizing the participants based on their initial hand function: those with no hand function (MFT hand subscore = 0) (N = 60) and those with a score >0 (N = 51). Initial FMA-UE was a primary predictive factor regardless of the time point or initial severity, whereas the presence of MEP was a significant predictor only in the group with no initial hand dexterity.

Using Wearable Inertial Sensors to Estimate Clinical Scores of Upper Limb Movement Quality in Stroke

Neurorehabilitation is progressively shifting from purely in-clinic treatment to therapy that is provided in both clinical and home-based settings. This transition generates a pressing need for assessments that can be performed across the entire continuum of care, a need that might be accommodated by application of wearable sensors. A first step toward ubiquitous assessments is to augment validated and well-understood standard clinical tests. This route has been pursued for the assessment of motor functioning, which in clinical research and practice is observation-based and requires specially trained personnel. In our study, 21 patients performed movement tasks of the Action Research Arm Test (ARAT), one of the most widely used clinical tests of upper limb motor functioning, while trained evaluators scored each task on pre-defined criteria. We collected data with just two wrist-worn inertial sensors to guarantee applicability across the continuum of care and used machine learning algorithms to estimate the ARAT task scores from sensor-derived features. Tasks scores were classified with approximately 80% accuracy. Linear regression between summed clinical task scores (across all tasks per patient) and estimates of sum task scores yielded a good fit (R 2 = 0.93; range reported in previous studies: 0.61-0.97). Estimates of the sum scores showed a mean absolute error of 2.9 points, 5.1% of the total score, which is smaller than the minimally detectable change and minimally clinically important difference of the ARAT when rated by a trained evaluator. We conclude that it is feasible to obtain accurate estimates of ARAT scores with just two wrist worn sensors. The approach enables administration of the ARAT in an objective, minimally supervised or remote fashion and provides the basis for a widespread use of wearable sensors in neurorehabilitation.