Predicting language improvement in acute stroke patients presenting with aphasia: a multivariate logistic model using...

Prediction of functional outcome immediately after stroke onset can guide optimal management. Most prognostic grading scales to date, however, have been based on established global metrics such as total NIHSS score, admission infarct volume, or intracranial occlusion on CTA. Our purpose was to construct a more focused, location-weighted multivariate model for the prediction of early aphasia improvement, based not only on traditional clinical and imaging parameters, but also on atlas-based structure/function correlation specific to the clinical deficit, using CT perfusion imaging.

Fifty-eight consecutive patients with aphasia due to first-time ischemic stroke of the left hemisphere were included. Language function was assessed on the basis of the patients admission and discharge NIHSS scores and clinical records. All patients had brain CTP and CTA within 9 hours of symptom onset. For image analysis, all CTPs were automatically co-registered to MNI-152 brain space and parcellated into mirrored cortical and subcortical regions. Multiple logistic regression analysis was used to find independent imaging and clinical predictors of language recovery.

By the time of discharge, 21 (36%) patients demonstrated improvement of language. Independent factors predicting improvement in language included rCBF of the angular gyrus GM (BA 39) and the lower third of the insular ribbon, proximal cerebral artery occlusion on admission CTA, and aphasia score on the admission NIHSS examination. Using these 4 variables, we developed a multivariate logistic regression model that could estimate the probability of early improvement in aphasia and predict functional outcome with 91% accuracy.

An imaging-based location-weighted multivariate model was developed to predict early language improvement of patients with aphasia by using admission data collected within 9 hours of stroke onset. This pilot model should be validated in a larger, prospective study; however, the semiautomated atlas-based analysis of brain CTP, along with the statistical approach, could be generalized for prediction of other outcome measures in patients with stroke.