Objective: To develop multivariate models for prediction of early motor deficit improvement in acute stroke patients with focal extremity paresis, using admission clinical and imaging data.
Methods: Eighty consecutive patients with motor deficit due to first-ever unilateral stroke underwent CT perfusion (CTP) within 9 hours of symptom onset. Limb paresis was prospectively assessed using admission and discharge NIH Stroke Scale (NIHSS) scoring. CTP scans were coregistered to the MNI-152 brain space and subsegmented to 146 pairs of cortical/subcortical regions based on preset atlases. Stepwise multivariate binary logistic regressions were performed to determine independent clinical and imaging predictors of paresis improvement.
Results: The rates of early motor deficit improvement were 18/49 (37%), 15/42 (36%), 8/25 (32%), and 7/23 (30%) for the right arm, right leg, left arm, and left leg, respectively. Admission NIHSS was the only independent clinical predictor of early limb motor deficit improvement. Relative CTP values of the inferior frontal lobe white matter, lower insular cortex, superior temporal gyrus, retrolenticular portion of internal capsule, postcentral gyrus, precuneus parietal gyri, putamen, and caudate nuclei were also independent predictors of motor improvement of different limbs. The multivariate predictive models of motor function improvement for each limb had 84%-92% accuracy, 79%-100% positive predictive value, 75%-94% negative predictive value, 83%-88% sensitivity, and 80%-100% specificity.
Conclusions: We developed pilot multivariate models to predict early motor functional improvement in acute stroke patients using admission NIHSS and atlas-based location-weighted CTP data. These models serve as a "proof-of-concept" for prospective location-weighted imaging prediction of clinical outcome in acute stroke.