Cerebral Small Vessel Disease MRI Features Do Not Improve the Prediction of Stroke Outcome. (26th January 2021) - Coutureau, Juliette; Asselineau, Julien; Perez, Paul; Kuchcinski, Gregory; Sagnier, Sharmila; Renou, Pauline; Munsch, Fanny; Lopes, Renaud; Henon, Hilde; Bordet, Regis; Dousset, Vincent; Sibon, Igor; et al.
Abstract : 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 patients with stroke. Methods: White matter hyperintensity, lacunes, perivascular spaces, microbleeds, and atrophy were quantified in 2 prospective datasets of 428 and 197 patients with first-ever stroke, using MRI collected 24 to 72 hours after stroke onset. Functional, cognitive, and psychological status were assessed at the 3- to 6-month follow-up. The predictive accuracy (in terms of calibration and discrimination) of age, baseline NIH Stroke Scale score (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 2 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 (area under the curve [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.