Geon Ha Kim1, Jae Hong Lee, Sang Won Seo, Byoung Seok Ye, Hanna Cho, Hee Jin Kim, Young Noh, Cindy W Yoon, Ju Hee Chin, Seung Jun Oh, Jae Seung Kim, Yearn Seong Choe, Kyung Han Lee, Sung Tae Kim, Jee Hyang Jeong, Duk L Na. 1. From the Department of Neurology (G.H.K., J.H.J.), Ewha Womans University Mokdong Hospital, Ewha Womans University School of Medicine, Seoul; Departments of Neurology (J.H.L.) and Nuclear Medicine (S.J.O., J.S.K.), Asan Medical Center, University of Ulsan College of Medicine, Seoul; Departments of Neurology (S.W.S., B.S.Y., H.C., H.J.K., J.H.C., D.L.N.), Nuclear Medicine (Y.S.C., K.H.L.), and Radiology (S.T.K.), Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul; Department of Neurology (Y.N.), Gachon University Gil Medical Center, Incheon; and Department of Neurology (C.W.Y.), Inha University Hospital, Inha University School of Medicine, Incheon, Korea.
Abstract
OBJECTIVE: The purpose of this study was to propose new criteria for differentiating Pittsburgh compound B (PiB)-negative from PiB-positive subcortical vascular dementia (SVaD) using clinical and MRI variables. METHODS: We measured brain amyloid deposition using PiB-PET in 77 patients with SVaD. All patients met DSM-IV criteria for vascular dementia and had severe white matter hyperintensities on MRI, defined as a cap or band ≥ 10 mm as well as a deep white matter lesion ≥ 25 mm. Eleven models were considered to differentiate PiB(-) from PiB(+) SVaD using 4 variables, including age, number of lacunes, medial temporal atrophy (MTA), and APOE ε4. The ideal cutoff values in each of the 11 models were selected using the highest Youden index. RESULTS: A total of 49 of 77 patients (63.6%) tested negative for PiB retention, while 28 (36.4%) tested positive for PiB retention. The ideal model for differentiating PiB(-) from PiB(+) SVaD was as follows: age ≤ 75 years, ≥ 5 lacunes, and MTA ≤ 3, which together yielded an accuracy of 67.5%. CONCLUSION: When patients meet the DSM-IV criteria for vascular dementia and also have severe white matter hyperintensities, younger age, greater number of lacunes, and lesser MTA, these are predictive of a PiB(-) scan in patients with SVaD. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that the combination of younger age, greater number of lacunes, and lesser MTA identifies patients with SVaD at lower risk of Alzheimer disease pathology.
OBJECTIVE: The purpose of this study was to propose new criteria for differentiating Pittsburgh compound B (PiB)-negative from PiB-positive subcortical vascular dementia (SVaD) using clinical and MRI variables. METHODS: We measured brain amyloid deposition using PiB-PET in 77 patients with SVaD. All patients met DSM-IV criteria for vascular dementia and had severe white matter hyperintensities on MRI, defined as a cap or band ≥ 10 mm as well as a deep white matter lesion ≥ 25 mm. Eleven models were considered to differentiate PiB(-) from PiB(+) SVaD using 4 variables, including age, number of lacunes, medial temporal atrophy (MTA), and APOE ε4. The ideal cutoff values in each of the 11 models were selected using the highest Youden index. RESULTS: A total of 49 of 77 patients (63.6%) tested negative for PiB retention, while 28 (36.4%) tested positive for PiB retention. The ideal model for differentiating PiB(-) from PiB(+) SVaD was as follows: age ≤ 75 years, ≥ 5 lacunes, and MTA ≤ 3, which together yielded an accuracy of 67.5%. CONCLUSION: When patients meet the DSM-IV criteria for vascular dementia and also have severe white matter hyperintensities, younger age, greater number of lacunes, and lesser MTA, these are predictive of a PiB(-) scan in patients with SVaD. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that the combination of younger age, greater number of lacunes, and lesser MTA identifies patients with SVaD at lower risk of Alzheimer disease pathology.
Authors: Ko Woon Kim; Hunki Kwon; Young-Eun Kim; Cindy W Yoon; Yeo Jin Kim; Yong Bum Kim; Jong Min Lee; Won Tae Yoon; Hee Jin Kim; Jin San Lee; Young Kyoung Jang; Yeshin Kim; Hyemin Jang; Chang-Seok Ki; Young Chul Youn; Byoung-Soo Shin; Oh Young Bang; Gyeong-Moon Kim; Chin-Sang Chung; Seung Joo Kim; Duk L Na; Marco Duering; Hanna Cho; Sang Won Seo Journal: Sci Rep Date: 2019-01-28 Impact factor: 4.379
Authors: Christian Lambert; Eva Zeestraten; Owen Williams; Philip Benjamin; Andrew J Lawrence; Robin G Morris; Andrew D Mackinnon; Thomas R Barrick; Hugh S Markus Journal: Neuroimage Clin Date: 2018-06-20 Impact factor: 4.881