Xiaodong Chen1, Lei Wei1, Jihui Wang2, Yilong Shan3, Wei Cai4, Xuejiao Men1, Sanxin Liu1, Zhuang Kang5, Zhengqi Lu6, Vincent C T Mok7, Aimin Wu1. 1. Department of Neurology, The Third Affiliated Hospital of Sun Yat-sen University, Tianhe Road No. 600, Guangzhou, 510630, Guangdong, China. 2. Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China. 3. Department of Rehabilitation Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China. 4. Department of Clinical Immunology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China. 5. Department of Radiology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China. 6. Department of Neurology, The Third Affiliated Hospital of Sun Yat-sen University, Tianhe Road No. 600, Guangzhou, 510630, Guangdong, China. lzq1828@outlook.com. 7. Department of Medicine and Therapeutics, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong, Hong Kong, China.
Abstract
PURPOSE: Visibility of deep medullary veins (DMVs) seen at SWI is predictive of poor prognosis in ischemic stroke. Few attentions have been paid to DMVs in atherosclerotic cerebral small vessel disease (aCSVD) which is attributed to long-term imbalanced microhemodynamics. We conducted this retrospective study to explore the association between DMVs profiles and aCSVD risk factors, neuroimaging markers. METHODS: Two hundred and two patients identified as aCSVD from January 2017 to March 2019 were included in the study. Their demographic, clinical, laboratory, and neuroimaging data were reviewed. The quantity and morphology of DMVs were assessed with a 5-grade (range 0~4) visual rating scale. Total CSVD burden was calculated with an ordinal "SVD score" (range 0~4). Spearman rank correlation and multivariable logistic regression analysis were performed to determine the association between DMV scale and CSVD markers. RESULTS: DMV scale showed strong positive correlation with CSVD burden (rs = 0.629, P < 0.001). Age (OR 1.078, 95% CI 1.015-1.145, P = 0.015) and hypertension (OR 2.629, 95% CI 1.024-6.749, P = 0.045) were two demographic risk factors for high DMV scale. Among CSVD neuroimaging markers, periventricular WMH (OR 2.925, 95% CI 1.464-5.845, P = 0.002), deep WMH (OR 2.872, 95% CI 1.174-7.022, P = 0.021), lacunae (OR 1.961, 95% CI 1.181-3.254, P = 0.009), and cerebral atrophy (OR 2.046, 95% CI 1.079-3.880, P = 0.028) were associated with high DMV scale after adjusting for clinical and metabolic confounders. CONCLUSION: Multifactorial association between DMV scale and epidemiological, radiological contributors of aCSVD suggests DMV's involved pathomechanism may participate in aCSVD development. Attach importance to DMV radiological profile in aCSVD will provide more neuroimaging information for diagnosis and prognosis.
PURPOSE: Visibility of deep medullary veins (DMVs) seen at SWI is predictive of poor prognosis in ischemic stroke. Few attentions have been paid to DMVs in atherosclerotic cerebral small vessel disease (aCSVD) which is attributed to long-term imbalanced microhemodynamics. We conducted this retrospective study to explore the association between DMVs profiles and aCSVD risk factors, neuroimaging markers. METHODS: Two hundred and two patients identified as aCSVD from January 2017 to March 2019 were included in the study. Their demographic, clinical, laboratory, and neuroimaging data were reviewed. The quantity and morphology of DMVs were assessed with a 5-grade (range 0~4) visual rating scale. Total CSVD burden was calculated with an ordinal "SVD score" (range 0~4). Spearman rank correlation and multivariable logistic regression analysis were performed to determine the association between DMV scale and CSVD markers. RESULTS: DMV scale showed strong positive correlation with CSVD burden (rs = 0.629, P < 0.001). Age (OR 1.078, 95% CI 1.015-1.145, P = 0.015) and hypertension (OR 2.629, 95% CI 1.024-6.749, P = 0.045) were two demographic risk factors for high DMV scale. Among CSVD neuroimaging markers, periventricular WMH (OR 2.925, 95% CI 1.464-5.845, P = 0.002), deep WMH (OR 2.872, 95% CI 1.174-7.022, P = 0.021), lacunae (OR 1.961, 95% CI 1.181-3.254, P = 0.009), and cerebral atrophy (OR 2.046, 95% CI 1.079-3.880, P = 0.028) were associated with high DMV scale after adjusting for clinical and metabolic confounders. CONCLUSION: Multifactorial association between DMV scale and epidemiological, radiological contributors of aCSVD suggests DMV's involved pathomechanism may participate in aCSVD development. Attach importance to DMV radiological profile in aCSVD will provide more neuroimaging information for diagnosis and prognosis.
Entities:
Keywords:
CSVD burden; Cerebral small vessel disease; Deep medullary vein; Neuroimaging marker
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