Long-Term Stroke Recurrence after Transient Ischemic Attack: Implications of Etiology / 대한뇌졸중학회지

BACKGROUND AND PURPOSE: To analyze long-term stroke recurrence (SR) characteristics after transient ischemic attack (TIA) according to initial etiological classification. METHODS: A prospective cohort of 706 TIA patients was followed up in a single tertiary stroke center. Endpoint was SR. Etiologic subgroup was determined according to the evidence-based causative classification system. Location of TIA and SR was recorded as right, left, or posterior territory. Disability stroke recurrence (DSR) was defined as modified Rankin Scale (mRS) score >1 or a one-point increase in those with previous mRS >1 at 3-month follow-up. RESULTS: During a follow-up of 3,493 patient-years (mean follow-up of 58.9±35.9 months), total SR was 125 (17.7%), corresponding to 3.6 recurrences per 100 patient-years. The etiology subgroups with a higher risk of SR were the unclassified (more than one cause) and large-artery atherosclerosis (LAA) categories. Of the SR cases, 88 (70.4%) had the same etiology as the index TIA; again, LAA etiology was the most frequent (83.9%). Notably, cardioaortic embolism was the most frequent cause (62.5%) of SR in the subgroup of 24 patients with undetermined TIA. Overall, SR occurred in the same territory in 74 of 125 patients (59.2%), with significant differences between etiological TIA subgroups (P=0.015). Eighty-two of 125 (65.6%) with SR had DSR, without differences between etiologies (P=0.453). CONCLUSIONS: SR occurred mainly with the same etiology and location as initial TIA, although undetermined TIA was associated with a high proportion of cardioaortic embolism SR. More than half of the recurrences caused some disability, regardless of etiology.

Cerebral Small Vessel Disease: A Review Focusing on Pathophysiology, Biomarkers, and Machine Learning Strategies / 대한뇌졸중학회지

Cerebral small vessel disease (cSVD) has a crucial role in lacunar stroke and brain hemorrhages and is a leading cause of cognitive decline and functional loss in elderly patients. Based on underlying pathophysiology, cSVD can be subdivided into amyloidal and non-amyloidal subtypes. Genetic factors of cSVD play a pivotal role in terms of unraveling molecular mechanism. An important pathophysiological mechanism of cSVD is blood-brain barrier leakage and endothelium dysfunction which gives a clue in identification of the disease through circulating biological markers. Detection of cSVD is routinely carried out by key neuroimaging markers including white matter hyperintensities, lacunes, small subcortical infarcts, perivascular spaces, cerebral microbleeds, and brain atrophy. Application of neural networking, machine learning and deep learning in image processing have increased significantly for correct severity of cSVD. A linkage between cSVD and other neurological disorder, such as Alzheimer’s and Parkinson’s disease and non-cerebral disease, has also been investigated recently. This review draws a broad picture of cSVD, aiming to inculcate new insights into its pathogenesis and biomarkers. It also focuses on the role of deep machine strategies and other dimensions of cSVD by linking it with several cerebral and non-cerebral diseases as well as recent advances in the field to achieve sensitive detection, effective prevention and disease management.