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  • Journal of the International Neuropsychological Society, Volume 16, Issue 4
  • July 2010, pp. 651-660

Predicting cognitive decline and conversion to Alzheimer’s disease in older adults using the NAB List Learning test

  • BRANDON E. GAVETT (a1) (a2) (a3), AL OZONOFF (a2) (a4), VLADA DOKTOR (a2), JOSEPH PALMISANO (a2) (a5), ANIL K. NAIR (a1) (a2), ROBERT C. GREEN (a1) (a2) (a6) (a7), ANGELA L. JEFFERSON (a1) (a2) (a8) (a9) and ROBERT A. STERN (a1) (a2) (a3)
  • DOI:
  • Published online: 01 April 2010

To validate the Neuropsychological Assessment Battery (NAB) List Learning test as a predictor of future multi-domain cognitive decline and conversion to Alzheimer’s disease (AD), participants from a longitudinal research registry at a national AD Center were, at baseline, assigned to one of three groups (control, mild cognitive impairment [MCI], or AD), based solely on a diagnostic algorithm for the NAB List Learning test (Gavett et al., 2009), and followed for 1–3 years. Rate of change on common neuropsychological tests and time to convert to a consensus diagnosis of AD were evaluated to test the hypothesis that these outcomes would differ between groups (AD>MCI>control). Hypotheses were tested using linear regression models (n = 251) and Cox proportional hazards models (n = 265). The AD group declined significantly more rapidly than controls on Mini-Mental Status Examination (MMSE), animal fluency, and Digit Symbol; and more rapidly than the MCI group on MMSE and Hooper Visual Organization Test. The MCI group declined more rapidly than controls on animal fluency and CERAD Trial 3. The MCI and AD groups had significantly shorter time to conversion to a consensus diagnosis of AD than controls. The predictive validity of the NAB List Learning algorithm makes it a clinically useful tool for the assessment of older adults. (JINS, 2010, 16, 651–660.)

Corresponding author
*Correspondence and reprint requests to: Brandon E. Gavett, Instructor of Neurology, Boston University School of Medicine, 72 East Concord Street B-7800, Boston, MA 02118-2526. E-mail:
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