Predicting level-2 cognitive outcomes and research clinic diagnosis of MCI and Dementia in Parkinson’s Disease from the MoCA: Guidance on the selection of optimal MoCA cutoffs

The Montreal Cognitive Assessment (MOCA) is frequently used in cohort studies into Parkinson’s disease (PD) as it is a simple and inexpensive tool for assessing the cognitive state of patients. However, cut-off values for distinguishing between normal cognition, mild cognitive impairment (MCI) and dementia differ across the literature. We comprehensively evaluate the accuracy of the MoCA for stratifying patients and investigate whether stratification can be improved by including additional routinely collected information.

Methods

We use longitudinal data from PD and healthy control participants of the PPMI cohort which – in addition to the MoCA – conducts detailed level-2 cognitive testing and records diagnoses of MCI and dementia made at the research clinics. The ability of the MoCA to predict level-2 outcomes and diagnoses was assessed using the AUC. Multilevel logistic regression was used to predict higher-order outcomes from MoCA scores in conjunction with other routinely collected information on basic demographics, functional impairment (MDS-UPDRS 1.1) and/or MoCA subscores. Model performance was compared using the AUC. Optimal cut-offs for patient stratification w.r.t. Youden’s J, equal proportions, screening and diagnosis were derived.

Results

Data from 1094 PD patients and of 267 healthy control participants was analyzed. MoCA total scores predicted level-2 cognitive impairment in PD (2 impaired domains or more) with an AUC of 0.86 (95% CI 0.84–0.88). Youden’s J was maximized at cutoff ≤ 24 with sensitivity 74.7 (70.5-79.3) and specificity 83.1 (82.0-84.2); cutoff ≤ 21 equated proportions. Prediction of level-2 cognitive outcomes was not substantially improved by additional covariates, but functional impairment/MDS-UPDRS 1.1 was found to be superior over the MoCA for predicting research clinic diagnosis of PD-MCI or PDD. MDS-UPDRS 1.1 ≥ 1 discriminated diagnosis of any impairment from no impairment with sensitivity 81.9 (79.7, 84.2) and specificity 71.5 (69.9, 73.0); a combination of MDS-UPDRS 1.1 ≥ 1 and MoCA ≤ 26 equated proportions. A combination of MDS-UPDRS 1.1 ≥ 2 and MoCA ≤ 20 equated proportions for discrimination of dementia from no dementia.

Discussion

Optimal cutoffs for PD-MCI or PDD on the MoCA scale depend on their purpose. In research studies with no immediate effect on patients, we recommend considering a cutoff that equates proportions. Identifying suitable cutoffs from the literature - for research or in clinic use - needs to take into account the respective PD population.