Development of a Screening Algorithm for Alzheimer's Disease Using Categorical Fluency and Confrontational Naming Abilities
- Author:
Yeon Kyung CHI
1
;
Ji Won HAN
;
Sunyoung PARK
;
Tae Hui KIM
;
Jung Jae LEE
;
Seok Bum LEE
;
Joon Hyuk PARK
;
Jong Chul YOUN
;
Jeong Lan KIM
;
Seung Ho RYU
;
Jin Hyeong JHOO
;
Ki Woong KIM
Author Information
1. Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea. jwhanmd@snu.ac.kr
- Publication Type:Original Article
- Keywords:
Alzheimer's disease;
Boston Naming Test;
Categorical Fluency Test;
Mini-Mental State Examination;
Naming ability;
Screening;
Semantic memory
- MeSH:
Alzheimer Disease;
Dementia;
Humans;
Internet;
Language Tests;
Logistic Models;
Mass Screening;
Memory;
Semantics
- From:Journal of Korean Geriatric Psychiatry
2019;23(1):28-32
- CountryRepublic of Korea
- Language:English
-
Abstract:
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×HS(1st)−0.362×HS(2nd)+0.455×perseveration+1.329×HFCR(2nd)−0.489×MFCR(1st)−0.565×LFCR(3rd). 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.
