Dementia diagnoses from clinical and neuropsychological data compared

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Abstract
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.