Deakin University
Browse

File(s) under permanent embargo

Development of a new tool to correlate stroke outcome with infarct topography: a proof-of-concept study

journal contribution
posted on 2010-01-01, 00:00 authored by T G Phan, J Chen, G Donnan, V Srikanth, Amanda WoodAmanda Wood, D C Reutens
Improving the ability to assess potential stroke deficit may aid the selection of patients most likely to benefit from acute stroke therapies. Methods based only on 'at risk' volumes or initial neurological condition do predict eventual outcome, but not perfectly. Given the close relationship between anatomy and function in the brain, we performed a proof-of-concept study to examine how well stroke outcome correlated with infarct location and extent. A prospective study of 60 patients with ischemic stroke (38 in the training set and 22 in the validation set), using an implementation of partial least squares with penalized logistic regression (PLS-PLR), was performed. The method yielded a model relating location of infarction (on a voxel-by-voxel basis) and neurological deficits. The area under the receiver operating characteristics curve (AUC) method was used to assess the accuracy of the method for predicting outcome. In the validation phase, this model indicated the presence of neglect (AUC 0.89), aphasia (AUC 0.79), right-arm motor deficit (0.94), and right-leg motor deficit (AUC 0.94) but less accurately indicated left-arm motor deficit (0.52) and left-leg motor deficit (0.69). The model indicated no to mild disability (Rankin ≤ 2) versus moderate to severe disability (Rankin > 2) with AUC 0.78. In this proof-of-concept study, we have demonstrated that stroke outcome correlates well with infarct location raising the possibility of accurate prediction of neurological deficit in the individual stroke patient using only information on infarct location and multivariate regression methods.

History

Journal

NeuroImage

Volume

49

Issue

1

Pagination

127 - 133

Publisher

Elsevier

Location

Amsterdam, The Netherlands

ISSN

1053-8119

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2009, Elsevier