Mapping Cognitive Trajectories and Detecting Early Dementia Using the Mini-Mental State Examination Cognitive Charts: Application to the French Three-City Cohort.

The Cognitive Quotient (QuoCo) classification algorithm monitoring decline on age- and education-adjusted Mini-Mental State Examination (MMSE)-derived cognitive charts has proved superior to the conventionally-used cut-off for identifying incident dementia; however, it remains to be tested in different settings. Data were drawn from the Three-City Cohort to 1) assess the screening accuracy of the QuoCo, and 2) compare its performance to that of serial MMSE tests applying different cut-offs. For the QuoCo, sensitivity was 74.2 (95% CI: 71.4-76.8) and specificity 84.1 (83.6-84.7) and for the MMSE < 24, 64.1 (61.1-67.0) and 94.8 (94.4-95.1), respectively; whereas overall accuracy and sensitivity was highest for MMSE cut-offs <25 and <26. User-friendly charts for mapping cognitive trajectories over visits with an alert for potentially 'abnormal' decline can be of practical use and encourage regular monitoring in primary care where the <24 cut-off is still widely used despite its poor sensitivity.

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.

Validation and diagnostic accuracy of predictive curves for age-associated longitudinal cognitive decline in older adults.

BACKGROUND: The Mini-Mental State Examination continues to be used frequently to screen for cognitive impairment in older adults, but it remains unclear how to interpret changes in its score over time to distinguish age-associated cognitive decline from an early degenerative process. We aimed to generate cognitive charts for use in clinical practice for longitudinal evaluation of age-associated cognitive decline. METHODS: We used data from the Canadian Study of Health and Aging from 7569 participants aged 65 years or older who completed a Mini-Mental State Examination at baseline, and at 5 and 10 years later to develop a linear regression model for the Mini-Mental State Examination score as a function of age and education. Based on this model, we generated cognitive charts designed to optimize accuracy for distinguishing participants with dementia from healthy controls. We validated our model using a separate data set of 6501 participants from the National Alzheimer's Coordinating Center's Uniform Data Set. RESULTS: For baseline measurement, the cognitive charts had a sensitivity of 80% (95% confidence interval [CI] 75% to 84%) and a specificity of 89% (95% CI 88% to 90%) for distinguishing healthy controls from participants with dementia. Similar sensitivities and specificities were observed for a decline over time greater than 1 percentile zone from the first measurement. Results in the validation sample were comparable, albeit with lower sensitivities. Negative predictive value was 99%. INTERPRETATION: Our innovative model, which factors in age and education, showed validity and diagnostic accuracy for determining whether older patients show abnormal performance on serial Mini-Mental State Examination measurements. Similar to growth curves used in pediatrics, cognitive charts allow longitudinal cognitive evaluation and enable prompt initiation of investigation and treatment when appropriate.