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 subpopulations 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, infarct location (subcortical vs cortical), diffusion weighted imaging infarct volume, time to reassessment, 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 random 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 congruent with 0.70 x initial impairment). CONCLUSIONS: Clinical variables only moderately predict motor recovery. Regression diagnostics demonstrated the existence of a subpopulation 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.
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 subpopulations 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, infarct location (subcortical vs cortical), diffusion weighted imaging infarct volume, time to reassessment, 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 random 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 congruent with 0.70 x initial impairment). CONCLUSIONS: Clinical variables only moderately predict motor recovery. Regression diagnostics demonstrated the existence of a subpopulation 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.
Authors: Steven R Zeiler; Robert Hubbard; Ellen M Gibson; Tony Zheng; Kwan Ng; Richard O'Brien; John W Krakauer Journal: Neurorehabil Neural Repair Date: 2015-12-31 Impact factor: 3.919
Authors: Ari L Harris; Jessica Elder; Nicholas D Schiff; Jonathan D Victor; Andrew M Goldfine Journal: Transl Stroke Res Date: 2013-10-19 Impact factor: 6.829
Authors: Anne K Rehme; Lukas J Volz; Delia-Lisa Feis; Simon B Eickhoff; Gereon R Fink; Christian Grefkes Journal: Hum Brain Mapp Date: 2015-08-19 Impact factor: 5.038
Authors: Kelsi K Hirai; Benjamin N Groisser; William A Copen; Aneesh B Singhal; Judith D Schaechter Journal: J Neurosci Methods Date: 2015-09-16 Impact factor: 2.390
Authors: Steven R Zeiler; Ellen M Gibson; Robert E Hoesch; Ming Y Li; Paul F Worley; Richard J O'Brien; John W Krakauer Journal: Stroke Date: 2013-01-15 Impact factor: 7.914