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Jennifer Burke University of Adelaide Associate Professor Jane Mathias University of Adelaide

Neuropsychological Differentiation of Alzheimer’s Disease and Vascular Dementia: A Meta-Analysis. Jennifer Burke University of Adelaide Associate Professor Jane Mathias University of Adelaide. Background.

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Jennifer Burke University of Adelaide Associate Professor Jane Mathias University of Adelaide

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  1. Neuropsychological Differentiation of Alzheimer’s Disease and Vascular Dementia: A Meta-Analysis Jennifer Burke University of Adelaide Associate Professor Jane Mathias University of Adelaide

  2. Background • The differential diagnosis of Alzheimer’s Disease (AD) and vascular dementia (VaD) is difficult • Cognitive assessments play an important role in differential diagnosis • There is a lack of clear evidence regarding which cognitive tests accurately discriminate between AD and VaD

  3. Aim • Undertake a meta-analytic review of research comparing the cognitive deficits of persons with AD and VaD

  4. Literature search Comprehensive search: • PsycINFO and PubMed databases • January 1989 to September 2006.

  5. Inclusion criteria Examined groups with AD & VaD Cognitive tests were administered to both groups These tests were not used for diagnosis Data enabling the calculation of effect sizes Participants did not have any other neurological or psychiatric disorder Was published in English

  6. Data collection The data extracted from each study included: • study characteristics • participants characteristics • cognitive tests • test data for AD & VaD groups

  7. Effect size calculations • Cohen’s d effect sizes were calculated for every cognitive test in every study • d measures the standardised mean difference between two groups • small effect: d = .2medium effect: d = .5 large effect: d = .8

  8. Calculations • Mean effect sizes were then calculated for all studies that used a given measure • Effect sizes were weighted to take into account sample size

  9. Calculations • Percentage overlap in scores (%OL) • 95% confidence intervals • Fail-safe N –measures the number of studies with small effects that are required to overturn a finding • Heterogeneity was tested

  10. Data interpretation For a test to be useful for differential diagnosis, it had to: • d > .8 (large effect) • 95% CI ≠ 0 • a large Nfs score

  11. Demographics • N = 81 studies • 119 cognitive tests were used • Participant recruitment: hospital inpatient & outpatients (34%) • AD: 68% females, VaD: 53% females • AD and VaD groups were comparable: education, time since diagnosis, depression • AD group was younger and had lower MMSE • Majority of studies used published diagnostic criteria

  12. Tests examined by > one study

  13. Results • d ranged from 0 to 1.1 • %OL ranged from 40 to 100 • At best, cognitive tests are limited in their ability to discriminate between AD & VaD

  14. Results • Tests examined by one study • 13 cognitive tests showed large group differences • Commonly used tests did not effectively discriminate between AD and VaD patients

  15. Conclusions • Cognitive tests must be used cautiously and in conjunction with other diagnostic information • Inadequate diagnostic criteria • VaD is a heterogeneous disease • Overlapping aetiologies • There are a number tests that may prove suitable for assisting with differential diagnosis

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