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논문 기본 정보

자료유형
학술저널
저자정보
Yeon Kyung Chi (Seoul National University Bundang Hospital) Ji Won Han (Department of Neuropsychiatry Seoul National University Bundang Hospital) Sunyoung Park (Department of Pathology Seoul National University College of Medicine Seoul) 김태희 (Department of Psychiatry Yonsei University Wonju Severance Christian Hospital) Jung Jae Lee (Department of Psychiatry Dankook University Hospital) Seok Bum Lee (Department of Psychiatry Dankook University College of Medicine Cheonan Korea) Joon Hyuk Park (Department of Psychiatry Jeju National University Hospital Jeju Korea) Jong Chul Youn (Department of Neuropsychiatry Kyunggi Provincial Hospital for the Elderly) Jeong Lan Kim (Department of Psychiatry School of Medicine Chungnam National University) Seung-Ho Ryu (Department of Psychiatry School of Medicine Konkuk University) Jin Hyeong Jhoo (Department of Psychiatry Kangwon National University Hospital) Ki Woong Kim (Departments of Neuropsychiatry Seoul National University Bundang Hospital Seongnam Korea)
저널정보
대한노인정신의학회 노인정신의학 노인정신의학 제23권 제1호
발행연도
2019.1
수록면
28 - 32 (5page)

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Objective:Declines in naming ability and semantic memory are well-known features of early Alzheimer’s disease (AD). We developed a new screening algorithm for AD using two brief language tests : the Categorical Fluency Test (CFT) and 15-item Boston Naming Test (BNT15). Methods:We administered the CFT, BNT15, and Mini-Mental State Examination (MMSE) to 150 AD patients with a Clinical Dementia Rating of 0.5 or 1 and to their age- and gender-matched cognitively normal controls. We developed a composite score for screening AD (LANGuage Composite score, LANG-C) that comprised demographic characteristics, BNT15 subindices, and CFT subindices. We compared the diagnostic accuracies of the LANG-C and MMSE using receiver operating curve analysis. Results:The LANG-C was calculated using the logit of test scores weighted by their coefficients from forward stepwise logistic regression models : logit (case)=12.608-0.107×age+1.111×gender+0.089×education-0.314×HS1st-0.362×HS2nd+0.455×perseveration+ 1.329×HFCR2nd-0.489×MFCR1st-0.565×LFCR3rd. The area under the curve of the LANG-C for diagnosing AD was good (0.894, 95% confidence interval=0.853-0.926 ; sensitivity=0.787, specificity=0.840), although it was smaller than that of the MMSE. Conclusion:The LANG-C, which is easy to automate using PC or smart devices and to deliver widely via internet, can be a good alternative for screening AD to MMSE.

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