Addition of EEG improves accuracy of a logistic model that uses neuropsychological and cardiovascular factors to identify dementia and MCI

Steven Michael Snyder, James Richard Hall, Sonya Lynn Cornwell, James David Falk

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

To investigate whether addition of EEG would improve accuracy of a logistic model that uses neuropsychological assessment and cardiovascular history to identify dementia and mild cognitive impairment (MCI) as a single group, we collected data and constructed logistic models from a sample of 78 normal adults and 33 patients (aged 50-85 years). To determine accuracy, we compared logistic regression results to a geriatrician's diagnosis of MCI or dementia that included Alzheimer's disease, vascular dementia or mixed dementia. We found that the addition of EEG (non-linear complexity) to a logistic model that included both neuropsychological assessment (ADAS-Cog) and cardiovascular history increased overall accuracy from 80% to 92%. The logistic model identified dementia and MCI as a single group comprised of the following subgroups (with accuracies): Alzheimer's disease (92%; 12/13), vascular dementia (73%; 8/11), mixed dementia (100%; 4/4), and mild cognitive impairment (80%; 4/5). Whereas the analysis is limited by small sample sizes and mixing of diverse pathologies, the findings do provide support that the subgroups may share changes in neuropsychological, cardiovascular, and electroencephalographic factors (specifically ADAS-Cog total score, cardiovascular history, and EEG complexity). Taken together, the study results provide support that EEG might complement the clinician's evaluation of dementia and MCI.

Original languageEnglish
Pages (from-to)97-102
Number of pages6
JournalPsychiatry Research
Volume186
Issue number1
DOIs
StatePublished - 30 Mar 2011

Keywords

  • Alzheimer's disease
  • Electroencephalography
  • Mild cognitive impairment
  • Vascular dementia

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