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