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

Background 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. Methods 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. Results 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. Conclusions 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.

Automated summary

The Montreal Cognitive Assessment (MoCA) is a widely used cognitive screening tool for older adults. However, interpreting its scores over time and distinguishing between age-associated cognitive decline and early neurodegeneration remains unclear. In this study, the researchers aimed to create cognitive charts using the MoCA for longitudinal evaluation of age-associated cognitive decline in clinical practice. They developed a linear regression model and validated it using two separate data sets. The Cognitive Charts-MoCA showed high validity and diagnostic accuracy, providing a valuable tool for identifying abnormal cognitive performance in older individuals and initiating appropriate investigation and treatment.