Objective. To validate A Quick Test of Cognitive Speed (AQT) as an instrument in diagnostic dementia evaluations against final clinical diagnosis and compare AQT with the Mini-Mental State Examination (MMSE) and Clock Drawing Test (CDT) in primary care. Design. Primary health care cohort survey. Setting. Four primary health care centres and a geriatric memory clinic in Sweden. Patients. 81 patients (age 65 and above) were included: 52 with cognitive symptoms and 29 presumed cognitively healthy. None of the patients had a previous documented dementia diagnosis. All patients performed MMSE, CDT, and AQT at the primary health care clinic and were referred for extensive neuropsychological testing at a memory clinic. AQT was validated against final clinical diagnosis determined by a geriatric specialist and a neuropsychologist. Main outcome measures. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), likelihood ratios, correlation data, and receiver operating characteristic (ROC). Results. For MMSE, sensitivity and specificity was 0.587 and 0.909; CDT 0.261 and 0.879; and AQT 0.783 and 0.667, respectively. For the combination of MMSE and CDT, sensitivity and specificity was 0.696 and 0.788, for MMSE and AQT 0.913 and 0.636. The ROC curve for AQT showed an area under curve (AUC) of 0.773. Conclusion. Our results suggest AQT is a usable test for dementia assessments in primary care. Sensitivity for AQT is superior to CDT, equivalent to MMSE, and comparable to the combination MMSE and CDT. MMSE in combination with AQT improves sensitivity. Because AQT is user-friendly and quickly administered, it could be applicable for primary care settings.

Background. Diagnostic evaluations of dementia are often performed in primary health care (PHC). Cognitive evaluation requires validated instruments.

Objective. To investigate the diagnostic accuracy and clinical utility of Cognistat in a primary care population.

Methods. Participants were recruited from 4 PHC centres; 52 had cognitive symptoms and 29 were presumed cognitively healthy. Participants were tested using the Mini-Mental State Examination (MMSE), the Clock Drawing Test (CDT), and Cognistat. Clinical diagnoses, based on independent neuropsychological examination and a medical consensus discussion in secondary care, were used as criteria for diagnostic accuracy analyses.

Results. The sensitivity, specificity, positive predictive value, and negative predictive value were 0.85, 0.79, 0.85, and 0.79, respectively, for Cognistat; 0.59, 0.91, 0.90, and 0.61 for MMSE; 0.26, 0.88, 0.75, and 0.46 for CDT; 0.70, 0.79, 0.82, and 0.65 for MMSE and CDT combined. The area under the receiver operating characteristic curve was 0.82 for Cognistat, 0.75 for MMSE, 0.57 for CDT, and 0.74 for MMSE and CDT combined.

Conclusions. The diagnostic accuracy and clinical utility of Cognistat was better than the other tests alone or combined. Cognistat is well adapted for cognitive evaluations in PHC and can help the general practitioner to decide which patients should be referred to secondary care.

BACKGROUND/OBJECTIVES: Normative Mini-mental state examination (MMSE) reference values in elderly are scarce. Therefore, the aim is to present normative MMSE values for 85-93 year olds.

DESIGN: A longitudinal age cohort study.

SETTING: A population study of the residents in the municipality of Linköping, Sweden.

PARTICIPANTS: Residents (n = 650) born in 1922 during the course of 2007. In total, 374 individuals participated and were tested with MMSE at age 85, 280 of these were willing and able to also participate at age 86, 107 at age 90 and 51 at age 93.

MEASUREMENTS: MMSE, from 0-30, with lower scores denoting more impaired cognition.

RESULTS: Median MMSE values for the total population over the ages 85, 86, 90 and 93 years was 28 for all ages investigated. The 25th percentile values were 26, 26, 26 and 27, respectively. For a "brain healthy" sub-group median values were 28, 29, 28, and 28. The 25th percentile values were 27, 28, 26 and 27, respectively. Comparisons for age-effects showed no differences when all individuals for each age group were compared. When only the individuals reaching 93 years of age (n = 50) were analyzed, there was a significant lowering of MMSE in that age group.

CONCLUSION: The literature is variable and in clinical practice a low (24) MMSE cut off is often used for possible cognitive impairment in old age. The present data indicate that MMSE 26 is a reasonable cut off for possible cognitive decline in older persons up to the age of 93. J Am Geriatr Soc 67:534-538, 2019.

Background: There are several cognitive assessment tools used in primary care, e.g., the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment. The Cognitive Assessment Battery (CAB) was introduced as a sensitive tool to detect cognitive decline in primary care. However, primary care validation is lacking. Therefore, we investigated the accuracy of the CAB in a primary care population. 

Objective: To investigate the accuracy of the CAB in a primary care population. 

Methods: Data from 46 individuals with cognitive impairment and 33 individuals who visited the primary care with somatic noncognitive symptoms were analyzed. They were investigated with the MMSE, the CAB, and a battery of neuropsychological tests; they also underwent consultation with a geriatric specialist. The accuracy of the CAB was assessed using c-statistics and the area under the receiver operating characteristic curve (AUC) was used to quantify the binary outcomes (“no cognitive impairment” or “cognitive impairment”).

Results: The “cognitive impairment” group was significantly different from the unimpaired group for all the subtests of the CAB. When accuracy was based on binary significant reduction or not in one or several domains of the CAB, the AUC varied between 0.685 and 0.772. However, when a summation or logistic regression of several subcategories was performed, using the numerical values for each subcategory, the AUC was >0.9. For comparison, the AUC for the MMSE was 0.849.

Conclusions: The accuracy of the CAB in a primary care population is poor to good when using binary cutoffs. Accuracy can be improved to high when using a summation or logistic regression of the numerical data of the subcategories. Considering CAB time, lack of adequate age norms, and a good accuracy for the MMSE, implementation of the CAB in primary care is not recommended at present based on the results of this study.