A cut score of <24 reduces the likelihood of misclassifying normal AA individuals as impaired than the traditional cut score. This study provides a MoCA cut score to help differentiate persons with MCI from NC in a community-dwelling AA sample. In comparison, the traditional cut score of <26 had higher sensitivity (84%), similar accuracy (76%), but much lower specificity (58%). 01) and an optimal cut score of <24 maximized sensitivity (72%), specificity (84%), and provided 76% diagnostic accuracy. The area under the curve (AUC) was significant (MoCA =. Demographics were non-significant in regression models. The MCI group was slightly older (MMCI = 64.76, MNC = 62.33 p =. ROC results were compared with previously published MoCA cut scores. Receiver operating characteristic (ROC) curve analysis determined a cut score to distinguish MCI from NC based on optimal sensitivity, specificity, diagnostic accuracy, and greatest perpendicular distance above the identity line.
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Logistic regression models utilized sex, education, age, and MoCA score to predict MCI versus NC. To establish a cut score for the Montreal Cognitive Assessment (MoCA) that distinguishes mild cognitive impairment (MCI) from normal cognition (NC) in a community-based African American (AA) sample.Ī total of 135 AA participants, from a larger aging study, diagnosed MCI (n = 90) or NC (n = 45) via consensus diagnosis using clinical history, Clinical Dementia Rating score, and comprehensive neuropsychological testing.