RESUMEN
OBJECTIVE: To avoid misdiagnosing mild cognitive impairment (MCI), knowledge of the multivariate base rates (MVBRs) of low scores on neuropsychological tests is crucial. Base rates have typically been determined from normative population samples, which may differ from clinically referred samples. The current study addresses this limitation by calculating the MVBR of low or high cognitive scores in older adults who presented to a memory clinic experiencing subjective cognitive decline but were not diagnosed with MCI. METHOD: We determined the MVBRs on the Kaplan-Baycrest Neurocognitive Assessment for 107 cognitively healthy older adults (M age = 75.81), by calculating the frequency of patients producing n scores below or above different cut-off values (i.e., 1, 1.5, 2.0, 2.5 SD from the mean), stratifying by education and gender. RESULTS: Performing below or above cut-off was common, with more stringent cut-offs leading to lower base rates (≥1 low scores occurred in 84.1% of older adults at -1 SD, 55.1% at -1.5 SD, and 39.3% at -2 SD below the mean; ≥1 high scores occurred in 80.4% of older adults at +1 SD, 35.5% at +1.5 SD, and 16.8% at +2 SD above the mean). Higher education was associated with varying base rates. Overall, the MVBR of obtaining a low cognitive test score was higher in this clinic sample, compared with prior studies of normative samples. CONCLUSIONS: MVBRs for clinically referred older adults experiencing memory complaints provide a diagnostic benefit, helping to prevent attributing normal variability to cognitive impairment and limiting false positive diagnoses.
Asunto(s)
Disfunción Cognitiva , Humanos , Anciano , Pruebas Neuropsicológicas , Disfunción Cognitiva/diagnóstico , Disfunción Cognitiva/psicología , EscolaridadRESUMEN
OBJECTIVES: The diagnostic entity of mild cognitive impairment (MCI) is heterogeneous, highlighting the need for data-driven classification approaches to identify patient subgroups. However, these approaches can be strongly determined by sample characteristics and selected measures. Here, we applied a cluster analysis to an MCI patient database from a neuropsychology clinic to determine whether the inclusion of patients with MCI with vascular pathology would result in a different classification of subgroups. METHODS: Participants diagnosed with MCI (n = 166), vascular cognitive impairment-no dementia (n = 26), and a group of older adults with subjective cognitive concerns but no objective impairment (n = 144) were assessed using a full neuropsychological battery and other clinical measures. Cognitive measures were analyzed using a hierarchical cluster analysis and then a k-means approach, with resulting clusters compared on a range of demographic and clinical variables. RESULTS: We found a 4-factor solution: a cognitively intact cluster, a globally impaired cluster, an amnestic/visuospatial impairment cluster, and a mild, mixed-domain cluster. Interestingly, group differences in self-reported multilingualism emerged in the derived clusters that were not observed when comparing diagnostic groups. CONCLUSIONS: Our results were generally consistent with previous studies using cluster analysis in MCI. Including patients with primarily cerebrovascular disease resulted in subtle differences in the derived clusters and revealed new insights into shared cognitive profiles of patients beyond diagnostic categories. These profiles should be further explored to develop individualized assessment and treatment approaches.