Reliability and validity of an algorithm for the diagnosis of normal cognition, mild cognitive impairment, and dementia: implications for multicenter research studies.

The traditional consensus diagnosis (ConsDx) of normal cognition, mild cognitive impairment (MCI), and dementia relies on the reconciliation of an informant-based report of cognitive and functional impairment by a physician diagnosis (PhyDx), and a neuropsychological diagnosis (NPDx). As this procedure may be labor intensive and influenced by the philosophy and biases of a clinician, the diagnostic algorithm (AlgDx) was developed to identify individuals as cognitively normal, with MCI, or dementia. The AlgDx combines the PhyDx with the NPDx, using a diagnostic algorithm that provides cognitive diagnoses, as defined by the National Alzheimer Coordinating Center/Uniform Data Set nomenclature. Reliability of the AlgDx was assessed in 532 community-dwelling elderly subjects by its concordance with the ConsDx and association with two biomarkers, medial temporal atrophy (MTA) scores of brain magnetic resonance imaging scans, and Apolipoprotein E (ApoE)-epsilon4 genotype. A high degree of concordance was observed between ConsDx and AlgDx with a weighted Cohen's kappa of 0.84. Concordance of the AlgDx to the same ConsDx categories ranged from 85% to 92%. Excellent discriminative validity was observed using AlgDx, MTA scores, and ApoE-epsilon4 allele frequencies, each of which distinguished subjects with amnestic MCI and dementia from normal subjects. The AlgDx of normal cognition, MCI, and dementia is a valid alternative that reduces time, effort, and biases associated with the ConsDx. The inherent reliability of a fixed algorithm, together with its efficiency and avoidance of individual bias, suggests the AlgDx may be used in longitudinal, multisite clinical trials, and population studies of MCI and dementia.