Development of a Screening Algorithm for Alzheimer's Disease Using Categorical Verbal Fluency

We developed a weighted composite score of the categorical verbal fluency test (CVFT) that can more easily and widely screen Alzheimer's disease (AD) than the mini-mental status examination (MMSE). We administered the CVFT using animal category and MMSE to 423 community-dwelling mild probable AD patients and their age- and gender-matched cognitively normal controls. To enhance the diagnostic accuracy for AD of the CVFT, we obtained a weighted composite score from subindex scores of the CVFT using a logistic regression model: logit (case) = 1.160+0.474× gender +0.003× age +0.226× education level – 0.089× first-half score – 0.516× switching score -0.303× clustering score +0.534× perseveration score. The area under the receiver operating curve (AUC) for AD of this composite score AD was 0.903 (95% CI = 0.883 – 0.923), and was larger than that of the age-, gender- and education-adjusted total score of the CVFT (p<0.001). In 100 bootstrapped re-samples, the composite score consistently showed better diagnostic accuracy, sensitivity and specificity for AD than the total score. Although AUC for AD of the CVFT composite score was slightly smaller than that of the MMSE (0.930, p = 0.006), the CVFT composite score may be a good alternative to the MMSE for screening AD since it is much briefer, cheaper, and more easily applicable over phone or internet than the MMSE.