Prediction equation for the mini-mental state examination: influence of education, age, and sex

Association between baseline Mini-Mental State Examination score and dementia incidence in a cohort of oldest old

BACKGROUND

The Brazilian population has aged rapidly. The oldest old, defined as persons aged 80 years or older, is the fastest growing segment of the Brazilian population. Several instruments have been used to assess the cognitive performance of the older people and predict dementia. One of the most commonly used is the Mini-Mental State Examination (MMSE).

OBJECTIVE

The aim of this study was to investigate the relationship between baseline MMSE score and the incidence of dementia in a Brazilian cohort of independent oldest old.

METHODS

Sociodemographic data and serial cognitive assessment of 248 older adults were analyzed.

RESULTS

Mean follow-up time of subjects was 4.0(±1.9) years, 71.4% were women, and mean MMSE score at entry was 25(±3.5). Mean MMSE scores at baseline were significantly higher (p=0.001) in the cognitively intact group than in those who developed dementia. The logistic regression showed that for a one point increase in MMSE score at baseline there was a 10% reduction in the probability of dementia.

CONCLUSIONS

In the Brazilian scenario of a rapidly growing population of oldest old, the extensive use of the MMSE gives rise to the need not only to determine its effectiveness for screening dementia, but also to interpret its score in terms of future conversion to dementia.

Predicting Mini-Mental Status Examination Scores through Paralinguistic Acoustic Features of Spontaneous Speech

Speech analysis could provide an indicator of cognitive health and help develop clinical tools for automatically detecting and monitoring cognitive health progression. The Mini Mental Status Examination (MMSE) is the most widely used screening tool for cognitive health. But the manual operation of MMSE restricts its screening within primary care facilities. An automatic screening tool has the potential to remedy this situation. This study aims to assess the association between acoustic features of spontaneous speech and assess whether acoustic features can be used to automatically predict MMSE score. We assessed the effectiveness of paralinguistic feature set for MMSE score prediction on a balanced sample of DementiaBank's Pitt spontaneous speech dataset, with patients matched by gender and age. Linear regression analysis shows that fusion of acoustic features, age, sex and years of education provides better results (mean absolute error, MAE = 4.97, and R² = 0.261) than acoustic features alone (MAE = 5.66 and R² =0.125) and age, gender and education level alone (MAE of 5.36 and R² =0.17). This suggests that the acoustic features of spontaneous speech are an important part of an automatic screening tool for cognitive impairment detection.Clinical relevance- We hereby present a method for automatic screening of cognitive health. It is based on acoustic information of speech, a ubiquitous source of data, therefore being cost-efficient, non-invasive and with little infrastructure required.