Erika A Pugh1, Emily C Kemp2, Christopher H van Dyck3, Adam P Mecca2, Emily S Sharp4. 1. Alzheimer's Disease Research Unit, Yale University School of Medicine, New Haven, CT, USA; Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA. Electronic address: erika.pugh@yale.edu. 2. Alzheimer's Disease Research Unit, Yale University School of Medicine, New Haven, CT, USA; Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA. 3. Alzheimer's Disease Research Unit, Yale University School of Medicine, New Haven, CT, USA; Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA. 4. Alzheimer's Disease Research Unit, Yale University School of Medicine, New Haven, CT, USA; Department of Neurology, Yale University School of Medicine, New Haven, CT, USA; Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
Abstract
OBJECTIVE: To investigate optimal cutoff scores and the effects of normative adjustments on the performance of the Montreal Cognitive Assessment (MoCA) as a screening instrument for Mild Cognitive Impairment (MCI) and dementia due to Alzheimer's disease (AD-dementia). METHODS: 499 adults 48 to 91 years-old enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI) and were administered the MoCA during baseline. Participants were classified as either cognitively normal (CN), MCI, or AD-dementia by clinical assessment. Receiver operating characteristic (ROC) analyses were performed using raw MoCA scores, education-adjusted MoCA scores, and a regression-based adjustment derived from the National Alzheimer's Coordinating Center data (NACC). Test performance characteristics were calculated for various cutoffs after each normative correction method. RESULTS: Areas under the curve (AUC) were similar for raw, education-adjusted, and NACC-adjusted MoCA scores, and demonstrated minimal improvement when adjustments of increasing complexity were applied. Our results suggest that the optimal cutoff score for distinguising MCI is 24 and for distinguising AD-dementia is 22. CONCLUSIONS: This study adds to the understanding of how normative adjustments affect the sensitivity and specificity of the MoCA. Suggested corrections based on education alone do not yield improved test characteristics, but small improvements are attained when a regression-based correction that accounts for age, sex, and education is applied. Furthermore, optimal cutoffs for distinguishing CN from MCI or CN from AD-dementia were lower than previously reported. Optimal cutoffs to detect MCI and AD-dementia may vary in different populations, and further study is needed to determine appropriate use of the MoCA as a screening tool.
OBJECTIVE: To investigate optimal cutoff scores and the effects of normative adjustments on the performance of the Montreal Cognitive Assessment (MoCA) as a screening instrument for Mild Cognitive Impairment (MCI) and dementia due to Alzheimer's disease (AD-dementia). METHODS: 499 adults 48 to 91 years-old enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI) and were administered the MoCA during baseline. Participants were classified as either cognitively normal (CN), MCI, or AD-dementia by clinical assessment. Receiver operating characteristic (ROC) analyses were performed using raw MoCA scores, education-adjusted MoCA scores, and a regression-based adjustment derived from the National Alzheimer's Coordinating Center data (NACC). Test performance characteristics were calculated for various cutoffs after each normative correction method. RESULTS: Areas under the curve (AUC) were similar for raw, education-adjusted, and NACC-adjusted MoCA scores, and demonstrated minimal improvement when adjustments of increasing complexity were applied. Our results suggest that the optimal cutoff score for distinguising MCI is 24 and for distinguising AD-dementia is 22. CONCLUSIONS: This study adds to the understanding of how normative adjustments affect the sensitivity and specificity of the MoCA. Suggested corrections based on education alone do not yield improved test characteristics, but small improvements are attained when a regression-based correction that accounts for age, sex, and education is applied. Furthermore, optimal cutoffs for distinguishing CN from MCI or CN from AD-dementia were lower than previously reported. Optimal cutoffs to detect MCI and AD-dementia may vary in different populations, and further study is needed to determine appropriate use of the MoCA as a screening tool.
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