Background and Purpose-On top of functional outcome, accurate prediction of cognitive outcome for stroke patients is an unmet need with major implications for clinical management. We investigated whether stroke location may contribute independent prognostic value to multifactorial predictive models of functional and cognitive outcomes. Methods-Four hundred twenty-eight consecutive patients with ischemic stroke were prospectively assessed with magnetic resonance imaging at 24 to 72 hours and at 3 months for functional outcome using the modified Rankin Scale and cognitive outcome using the Montreal Cognitive Assessment (MoCA). Statistical maps of functional and cognitive eloquent regions were derived from the first 215 patients (development sample) using voxel-based lesion-symptom mapping. We used multivariate logistic regression models to study the influence of stroke location (number of eloquent voxels from voxel-based lesion-symptom mapping maps), age, initial National Institutes of Health Stroke Scale and stroke volume on modified Rankin Scale and MoCA. The second part of our cohort was used as an independent replication sample. Results-In univariate analyses, stroke location, age, initial National Institutes of Health Stroke Scale, and stroke volume were all predictive of poor modified Rankin Scale and MoCA. In multivariable analyses, stroke location remained the strongest independent predictor of MoCA and significantly improved the prediction compared with using only age, initial National Institutes of Health Stroke Scale, and stroke volume (area under the curve increased from 0.697-0.771; difference=0.073; 95% confidence interval, 0.008-0.155). In contrast, stroke location did not persist as independent predictor of modified Rankin Scale that was mainly driven by initial National Institutes of Health Stroke Scale (area under the curve going from 0.840 to 0.835). Similar results were obtained in the replication sample. Conclusions-Stroke location is an independent predictor of cognitive outcome (MoCA) at 3 months post stroke.

Objective:To determine whether the total small vessel disease (SVD) score adds information to the prediction of stroke outcome compared to validated predictors, we tested different predictive models of outcome in stroke patients.Methods:White matter hyperintensity, lacunes, perivascular spaces, microbleeds, and atrophy were quantified in two prospective datasets of 428 and 197 first-ever stroke patients, using MRI collected 24-to-72 hours after stroke onset. Functional, cognitive, and psychological status were assessed at the 3–6 month follow-up. The predictive accuracy (in terms of calibration and discrimination) of age, baseline NIHSS, and infarct volume was quantified (model-1) on dataset-1, the total SVD score was added (model-2), and the improvement in predictive accuracy was evaluated. These two models were also developed in dataset-2 for replication. Finally, in model-3, the MRI features of cerebral SVD were included rather than the total SVD score.Results:Model-1 showed excellent performance for discriminating poor vs. good functional outcomes (AUC=0.915), and fair performance for identifying cognitively impaired and depressed patients (AUCs, 0.750 and 0.688 respectively). A higher SVD score was associated with a poorer outcome (odds ratio=1.30 [1.07, 1.58], p=0.0090 at best for functional outcome). However, adding the total SVD score (model-2) or individual MRI features (model-3) did not improve the prediction over model-1. Results for dataset-2 were similar.Conclusions:Cerebral SVD was independently associated with functional, cognitive, and psychological outcomes, but had no clinically relevant added value to predict the individual outcomes of patients when compared to the usual predictors, such as age and baseline NIHSS.