Heterogeneous samples reflect the clinical reality more accurately as healthcare professionals face the challenge to identify truly impaired patients from a pool of individuals with a suspected neurocognitive disorder (NCD), irrespective of the underlying cause. In addition, excluding patients who are difficult to diagnose induces several forms of bias and may lead to an overestimation of diagnostic accuracy. Furthermore, in most validation studies, a rather homogenous patient sample was recruited (e.g., only patients with probable Alzheimer’s disease (AD) according to McKhann criteria, exclusion of patients with medical comorbidities), which does not reflect the clinical reality, where patient populations are typically heterogeneous, and medical comorbidities are frequent. Optimal cut-offs are likely to be sample-dependent and specific to the individual study and should therefore be validated in independent samples. Moreover, most previous authors defined “optimal cut-offs,” which aim at finding the best balance between sensitivity and specificity, as opposed to conventional cut-offs that are based on clinical standards (e.g., test performance 1–2 SD below the normative mean ). However, the psychometric properties of any screening test are not fixed characteristics, but depend on the clinical context, limiting the transferability of these cut-offs to other settings. Consequently, new cut-offs have been proposed for various patient populations and languages (see for an overview). However, while the initially proposed cut-off (25/26 points) has shown good sensitivity for mild cognitive impairment (MCI) (i.e., ≥ 83%), this cut-off was found to have a specificity of 66% or less in various different studies, implying a potentially unacceptably high number of false-positive classifications. It correlates well with extensive neuropsychological test batteries and covers most of the cognitive domains outlined in the Diagnostic and Statistical Manual, 5th Edition (DSM-5). The Montreal Cognitive Assessment (MoCA) has gained popularity for cognitive screening. In the context of clinical research, accurate cognitive assessment tools are needed for an adequate selection of participants, since erroneous inclusion or exclusion of individuals may bias study findings. Early detection of dementia is crucial for an implementation of therapeutic strategies in the earliest disease stages, and reliable cognitive screening tools play an important role in this process of identifying individuals with cognitive impairment. Using two separate cut-offs for the MoCA combined with scores in an indecisive area enhances the accuracy of cognitive screening.Ī steep increase in the prevalence of dementia is expected, associated with social, economic, and societal challenges. Scores between these two cut-offs require further examinations. Introducing two separate cut-offs increased diagnostic accuracies with 92% specificity (23/24 points) and 91% sensitivity (26/27 points). Compared to the original MoCA cut-off, the cut-off of 23/24 points had higher specificity (92% vs 63%), but lower sensitivity (65% vs 86%). ResultsĪ cut-off of 23/24 on the MoCA had better correct classification rates than the MMSE and the original MoCA cut-off. Cut-offs were identified based on (a) Youden’s index and (b) the 10th percentile of the control group. Methodsĭata were analyzed from 496 Memory Clinic outpatients (447 individuals with a neurocognitive disorder 49 with cognitive normal findings) and from 283 normal controls. We aim to revise the cut-off on the German MoCA for its use in clinical routine. The Montreal Cognitive Assessment (MoCA) has good sensitivity for mild cognitive impairment, but specificity is low when the original cut-off (25/26) is used.

Moca test score interpretation