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

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.