Accuracy of the Cognitive Assessment Battery in a Primary Care Population

Abstract

Background: There are several cognitive assessment tools used in primary care, e.g., the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment. The Cognitive Assessment Battery (CAB) was introduced as a sensitive tool to detect cognitive decline in primary care. However, primary care validation is lacking. Therefore, we investigated the accuracy of the CAB in a primary care population. Objective: To investigate the accuracy of the CAB in a primary care population. Methods: Data from 46 individuals with cognitive impairment and 33 individuals who visited the primary care with somatic noncognitive symptoms were analyzed. They were investigated with the MMSE, the CAB, and a battery of neuropsychological tests; they also underwent consultation with a geriatric specialist. The accuracy of the CAB was assessed using c-statistics and the area under the receiver operating characteristic curve (AUC) was used to quantify the binary outcomes (“no cognitive impairment” or “cognitive impairment”). Results: The “cognitive impairment” group was significantly different from the unimpaired group for all the subtests of the CAB. When accuracy was based on binary significant reduction or not in one or several domains of the CAB, the AUC varied between 0.685 and 0.772. However, when a summation or logistic regression of several subcategories was performed, using the numerical values for each subcategory, the AUC was >0.9. For comparison, the AUC for the MMSE was 0.849. Conclusions: The accuracy of the CAB in a primary care population is poor to good when using binary cutoffs. Accuracy can be improved to high when using a summation or logistic regression of the numerical data of the subcategories. Considering CAB time, lack of adequate age norms, and a good accuracy for the MMSE, implementation of the CAB in primary care is not recommended at present based on the results of this study.