Aims: To develop and validate a conversion table between the MMSE and the MoCA using Rasch analysis in older adults undergoing selective surgery and examine its diagnostic accuracy in detecting cognitive impairment. Design: Cross-sectional study. Methods: Older patients [N = 129; age 66.0 (4.6) years, education 7.7 (3.5) years] undergoing elective surgery were recruited from December 2017 to June 2018. All participants completed the MMSE and MoCA and 113 of them completed a battery of neuropsychological tests. Common person linking based on Rasch analysis was performed to develop the conversion table. The conversions were validated by calculating the intraclass correlation coefficient (ICC), score differences between actual and converted scores, and root mean squared error of the difference (RMSE). The diagnostic accuracy of the conversions for detecting cognitive impairment was also tested. Results: The MoCA [person measure: 1.3 (1.1) logits] was better targeted to the patients than the MMSE [person measure: 3.2 (1.3) logits]. Conversion from MoCA to MMSE scores (ICC 0.84, 95% CI 0.77-0.88; RMSE 1.36) was more precise than conversion from MMSE to MoCA (ICC 0.82, 95% CI 0.75-0.87; RMSE 2.56). Conversion from MoCA to MMSE demonstrated better diagnostic accuracy in detecting cognitive impairment than the actual MMSE, whereas conversion from MMSE to MoCA exhibited the opposite pattern. Conclusion: Conversion from MoCA to MMSE was more precise and had better diagnostic accuracy in detecting pre-operative cognitive impairment in older patients undergoing selective surgery than conversion from MMSE into MoCA. Impact: The finding is useful for interpreting, comparing, and integrating cognitive measurements in surgical settings and clinical research. Statistically sound conversion between MoCA and MMSE based on Rasch analysis is now possible for surgical setting and clinical research.

Objective:This study aimed to identify predictors of limitations in basic activities of daily living (BADL) among people with severe disabilities.Methods:4075 long-term care beneficiaries with severe disabilities in Guangzhou, China, were included during July 2018 and March 2019. BADL was assessed using the Barthel index (BI). Muscle strength was measured by using the Lovett Rating Scale. Age, gender, comorbidities, and muscle strengths were collected as independent variables. Chi-square Automatic Interaction Detector (CHAID) method was used to examine associations between independent variables and item scores of the BI.Results:Muscle strength and history of stroke were parent node and child node for most of BADL limitations, respectively. Upper limb muscle strength (≤ 3) was a major predictor for dependence in feeding, grooming, toileting, dressing, and transfer, while lower limb muscle strength (≤ 3) was a major predictor for limitation in mobility.Conclusions:Muscle strength was the strongest predictor of BADLs among people with severe disability. Muscle strength grading may be optimal for designing supporting strategies for people with severe disabilities.