J Korean Geriatr Psychiatry.  2019 Apr;23(1):28-32. 10.0000/jkgp.2019.23.1.28.

Development of a Screening Algorithm for Alzheimer's Disease Using Categorical Fluency and Confrontational Naming Abilities

Affiliations
  • 1Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea. jwhanmd@snu.ac.kr
  • 2Department of Pathology, Seoul National University College of Medicine, Seoul, Korea.
  • 3Department of Psychiatry, Yonsei University Wonju Severance Christian Hospital, Wonju, Korea.
  • 4Department of Psychiatry, Dankook University Hospital, Cheonan, Korea.
  • 5Department of Neuropsychiatry, JeJu National University Hospital, Jeju, Korea.
  • 6Department of Neuropsychiatry, Kyunggi Provincial Hospital for the Elderly, Yongin, Korea.
  • 7Department of Psychiatry, Chungnam National University Hospital, Daejeon, Korea.
  • 8Department of Psychiatry, School of Medicine, Konkuk University, Konkuk University Medical Center, Seoul, Korea.
  • 9Department of Psychiatry, Kangwon National University School of Medicine, Chuncheon, Korea.
  • 10Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea.
  • 11Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Korea.

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.

Keyword

Alzheimer's disease; Boston Naming Test; Categorical Fluency Test; Mini-Mental State Examination; Naming ability; Screening; Semantic memory
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