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April 13, 2021; 96 (15 Supplement) Saturday, April 17

Using a MoCA Score to Predict the Probability of Medical Decision-Making Capacity in Patients with Mild Cognitive Impairment (MCI) or Dementia (1844)

Noam Margalit, Jumana Alshaikh, James Mastrianni
First published April 13, 2021,
Noam Margalit
1University of Chicago Pritzker School of Medicine
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Jumana Alshaikh
2Johns Hopkins University
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James Mastrianni
3University of Chicago
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Citation
Using a MoCA Score to Predict the Probability of Medical Decision-Making Capacity in Patients with Mild Cognitive Impairment (MCI) or Dementia (1844)
Noam Margalit, Jumana Alshaikh, James Mastrianni
Neurology Apr 2021, 96 (15 Supplement) 1844;

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Abstract

Objective: To assess whether the MMSE and/or MoCA could be employed as screening tools to gauge medical decision-making capacity in patients with dementia

Background: Although cognition is routinely assessed at clinical visits using screening tools such as the Montreal Cognitive Assessment (MoCA) and Mini Mental State Exam (MMSE), a patient’s capacity to make their own medical decisions is not, and the available capacity assessment tools can be time consuming to use in practice. Employing the MoCA or MMSE as a concurrent screen for capacity could allow clinicians to better anticipate and direct goals of care conversations with patients and families.

Design/Methods: 127 patients with MCI or dementia were recruited from the University of Chicago Memory Center. The MMSE, MoCA, and a formal medical decision-making capacity assessment tool were administered to all patients. Univariate and multivariable logistic regression models were fit for MMSE, MoCA, age, and gender as predictors of capacity. ROC curves were generated to assess sensitivity and specificity. Cross-validation was performed to estimate the area under the ROC curve (AUC).

Results: The mean (±SD) MoCA score for the cohort was 16±7. The mean MMSE score was 20±7. Approximately 40% (51/127) of patients were determined to have capacity using the formal capacity tool. The best model to predict capacity incorporated the MoCA alone, and the cross-validated AUC was .898 (95% CI: .792–.929). The probability of having capacity with a MoCA of 22 was 75% (95% CI: 63%–87%). The probability of having capacity with a MoCA of 15 was 24% (95% CI: 13%–35%).

Conclusions: By employing the MoCA as a screen for capacity, clinicians can utilize a common clinical tool to better inform their conversations about goals of care with patients and families. Validation of these findings in an independent dataset is warranted.

Disclosure: Noam Margalit has nothing to disclose. Dr. Alshaikh has nothing to disclose. Dr. Mastrianni has received personal compensation in the range of $500-$4,999 for serving as a Consultant for CVS Caremark. The institution of Dr. Mastrianni has received research support from Brain Research Foundation.

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