Background: The Chinese version of the Mini-Mental State Examination (MMSE-C) and the Beijing version of the Montreal Cognitive Assessment (MoCA-BJ) are the most commonly used scales to screen for Alzheimer's disease (AD) among Chinese patients; however, their consistency varies according to populations and languages. Equivalent conversion of MMSE-C and MoCA-BJ scores is important for meta-analysis.

Materials And Methods: MMSE-C and MoCA-BJ scoring were performed on the enrolled patients with AD ( = 332). Consistency analysis of MMSE-C and MoCA-BJ scores of patients in the conversion groups was performed. The circle-arc method was used to convert the MMSE-C scores of the conversion groups into MoCA-BJ scores, and the conversion formula was generated. The MMSE-C data of the verification group was converted to MoCA-BJ according to the formula, and the consistency analysis of the original MoCA-BJ of the verification group and the converted MoCA-BJ was performed to verify the conversion model.

Results: The results of the consistency analysis of MMSE-C and MoCA-BJ in group A showed that the correlation coefficients of the total group, high education years subgroup, medium education years subgroup, and low education years subgroup were 0.905 ( < 0.001), 0.874 ( < 0.001), 0.949 ( < 0.001), and 0.874 ( < 0.001), respectively, with high consistency and statistical significance. After applying the circle-arc method for equivalent conversion, the consistency analysis results of the original and the converted MoCA-BJ of the patients in group B of the total group, high education years subgroup, medium education years subgroup, and low education years subgroup were 0.891 ( < 0.001), 0.894 ( < 0.001), 0.781 ( < 0.001), 0.909 ( < 0.001), respectively, with high consistency and statistical significance.

Conclusion: We established and validated a model of MMSE-C and MoCA-BJ score conversion for Chinese patients with AD using the circle-arc method. This model could be useful for multi-centers clinical trials and meta-analysis.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9537111PMC
http://dx.doi.org/10.3389/fpsyg.2022.990666DOI Listing

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