It's all about cognitive trajectory: Accuracy of the cognitive charts-MoCA in normal aging, MCI, and dementia

The Montreal Cognitive Assessment (MoCA) is an established cognitive screening tool in older adults. It remains unclear, however, how to interpret its scores over time and distinguish age-associated cognitive decline (AACD) from early neurodegeneration. We aimed to create cognitive charts using the MoCA for longitudinal evaluation of AACD in clinical practice.

We analyzed data from the National Alzheimer's Coordinating Center (9684 participants aged 60 years or older) who completed the MoCA at baseline. We developed a linear regression model for the MoCA score as a function of age and education. Based on this model, we generated the Cognitive Charts-MoCA designed to optimize accuracy for distinguishing participants with MCI and dementia from healthy controls. We validated our model using two separate data sets.

For longitudinal evaluation of the Cognitive Charts-MoCA, sensitivity (SE) was 89%, 95% confidence interval (CI): [86%, 92%] and specificity (SP) 79%, 95% CI: [77%, 81%], hence showing better performance than fixed cutoffs of MoCA (SE 82%, 95% CI: [79%, 85%], SP 68%, 95% CI: [67%, 70%]). For current cognitive status or baseline measurement, the Cognitive Charts-MoCA had a SE of 81%, 95% CI: [79%, 82%], SP of 84%, 95% CI: [83%, 85%] in distinguishing healthy controls from mild cognitive impairment or dementia. Results in two additional validation samples were comparable.

The Cognitive Charts-MoCA showed high validity and diagnostic accuracy for determining whether older individuals show abnormal performance on serial MoCAs. This innovative model allows longitudinal cognitive evaluation and enables prompt initiation of investigation and treatment when appropriate.