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From the Department of Psychology (Dr. Tschanz and M.C. Norton), College of Family Life (Drs. Tschanz and Wyse, N. West, M.C. Norton, and R. Nickles), Utah State University; the Department of Psychiatry and Behavioral Sciences and the Bryan Alzheimer’s Disease Research Center (Drs. Welsh-Bohmer and Breitner), Duke University; the Institute of Clinical Neuroscience (Dr. Skoog), Section of Psychiatry, Göteborg University; and the Department of Mental Hygiene (Dr. Breitner), The Johns Hopkins University, Baltimore, MD.
Address correspondence and reprint requests to Dr. J.T. Tschanz, Utah State University, Cache County Memory Study, UMC 4440, Logan, UT 84322-4440; e-mail: joannt{at}cc.usu.edu
OBJECTIVE: To validate a neuropsychological algorithm for dementia diagnosis.
METHODS: We developed a neuropsychological algorithm in a sample of 1,023 elderly residents of Cache County, UT. We compared algorithmic and clinical dementia diagnoses both based on DSM-III-R criteria. The algorithm diagnosed dementia when there was impairment in memory and at least one other cognitive domain. We also tested a variant of the algorithm that incorporated functional measures that were based on structured informant reports.
RESULTS: Of 1,023 participants, 87% could be classified by the basic algorithm, 94% when functional measures were considered. There was good concordance between basic psychometric and clinical diagnoses (79% agreement, kappa = 0.57). This improved after incorporating functional measures (90% agreement, kappa = 0.76).
CONCLUSIONS: Neuropsychological algorithms may reasonably classify individuals on dementia status across a range of severity levels and ages and may provide a useful adjunct to clinical diagnoses in population studies.
Key words: Dementia—Neuropsychology—Psychometric classification.
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