%0 Journal Article
%T Quantitative Analysis of White Matter Hyperintensities as a Predictor of 1-Year Risk for Ischemic Stroke Recurrence.
%A Sun Y
%A Xia W
%A Wei R
%A Dai Z
%A Sun X
%A Zhu J
%A Song B
%A Wang H
%J Neurol Ther
%V 0
%N 0
%D 2024 Aug 13
%M 39136813
%F 4.446
%R 10.1007/s40120-024-00652-3
%X <B>BACKGROUND: </B>This study evaluates the role of quantitative characteristics of white matter hyperintensities (WMHs) in predicting the 1-year recurrence risk of ischemic stroke.<BR><B>METHODS: </B>We conducted a retrospective analysis of 1061 patients with ischemic stroke from January 2018 to April 2021. WMHs were automatically segmented using a cluster-based method to quantify their volume and number of clusters (NoC). Additionally, two radiologists independently rated periventricular and deep WMHs using the Fazekas scale. The cohort was divided into a training set (70%) and a testing set (30%). We employed Cox proportional hazards models to develop predictors based on quantitative WMH characteristics, Fazekas scores, and clinical factors, and compared their performance using the concordance index (C-index).<BR><B>RESULTS: </B>A total of 180 quantitative variables related to WMHs were extracted. A higher NoC in deep white matter and brainstem, advanced age (> 90 years old), specific stroke subtypes, and absence of discharge antiplatelets showed stronger associations with the risk of ischemic stroke recurrence within 1 year. The nomogram incorporating quantitative WMHs data showed superior discrimination compared to those based on the Fazekas scale or clinical factors alone, with C-index values of 0.709 versus 0.647 and 0.648, respectively, in the testing set. Notably, a combined model including both WMHs and clinical factors achieved the highest predictive accuracy, with a C-index of 0.735 in the testing set.<BR><B>CONCLUSIONS: </B>Quantitative assessment of WMHs provides a valuable neuro-imaging tool for enhancing the prediction of ischemic stroke recurrence risk.