Based on the available evidence , motor evoked potentials at rest were the only biomarker that predicted motor outcome in individuals with severe arm impairment following stroke .
To build an underst and ing of the neurobiology underpinning arm recovery in people with severe arm impairment due to stroke , we conducted a pooled individual data systematic review to : 1 ) characterize brain biomarkers ; 2 ) determine relationship(s ) between biomarkers and motor outcome ; and 3 ) establish relationship(s ) between biomarkers and motor recovery .
Inter-individual Variability in the Capacity for Motor Recovery After Ischemic Stroke
Background . Motor recovery after stroke is predicted only moderately by clinical variables , implying that there is still a substantial amount of unexplained , biologically meaningful variability in recovery . Regression diagnostics can indicate whether this is associated simply with Gaussian error or instead with multiple sub population s that vary in their relationships to the clinical variables . Objective . To perform regression diagnostics on a linear model for recovery versus clinical predictors . Methods . Forty-one patients with ischemic stroke were studied . Impairment was assessed using the upper extremity Fugl-Meyer Motor Score . Motor recovery was defined as the change in the upper extremity Fugl-Meyer Motor Score from 24 to 72 hours after stroke to 3 or 6 months later . The clinical predictors in the model were age , gender , infa rct location ( subcortical vs cortical ) , diffusion weighted imaging infa rct volume , time to re assessment , and acute upper extremity Fugl-Meyer Motor Score . Regression diagnostics included a Kolmogorov-Smirnov test for Gaussian errors and a test for outliers using Studentized deleted residuals . Results . In the r and om sample , clinical variables explained only 47 % of the variance in recovery . Among the patients with the most severe initial impairment , there was a set of regression outliers who recovered very poorly . With the outliers removed , explained variance in recovery increased to 89 % , and recovery was well approximated by a proportional relationship with initial impairment ( recovery ≅ 0.70 × initial impairment ) . Conclusions . Clinical variables only moderately predict motor recovery . Regression diagnostics demonstrated the existence of a sub population of outliers with severe initial impairment who show little recovery . When these outliers were removed , clinical variables were good predictors of recovery among the remaining patients , showing a tight proportional relationship to initial impairment
Based on the available evidence , motor evoked potentials at rest were the only biomarker that predicted motor outcome in individuals with severe arm impairment following stroke .
To build an underst and ing of the neurobiology underpinning arm recovery in people with severe arm impairment due to stroke , we conducted a pooled individual data systematic review to : 1 ) characterize brain biomarkers ; 2 ) determine relationship(s ) between biomarkers and motor outcome ; and 3 ) establish relationship(s ) between biomarkers and motor recovery .