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P3‐158: Heterogeneity of visual deficits in posterior cortical atrophy
Author(s) -
Crutch Sebastian J.,
Lehmann Manja,
Barnes Josephine,
Fox Nick,
Warrington Elizabeth
Publication year - 2010
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2010.05.1656
Subject(s) - posterior cortical atrophy , visual processing , psychology , perception , cognition , visual perception , cognitive psychology , neuroscience , disease , dementia , medicine , pathology
Subjects had a mean age of 72.9 yrs, had 16.2 yrs of education, and were predominately Caucasian (88%). 340 individuals remained normal at follow-up and were randomly divided into Robust norm and test groups. The remaining 35 subjects declined to MCI (n 1⁄4 23) or AD (n 1⁄4 12) (NL-DEC). Norms were developed on the Robust group using linear regression models including education, gender, age, race and number of test administrations . Any significant two way interactions were added into the equation in a stepwise fashion. Z scores were generated for the test group by subtracting the predicted value (via the linear regression model) from the individual test score and dividing by the standard deviation of the residuals. Results: At the first UDS time point, a stepwise logistic regression model determined the UDS z score combination that best predicted group membership (NL-NL vs NL-DEC). The final model included the cross sectional MMSE, Trails B time, BNT and Logical Memory II, and resulted in an overall accuracy of 85% (specificity 1⁄4 85%, sensitivity 1⁄4 87%). Excessive z score change over time for any variable did not increment the cross sectional model. Conclusions: Robust UDS norms can serve to determine cognitive impairment in ADC participants and may be useful for identifying individuals at-risk for future decline. Such individuals would be candidates for early interventions.