Dataset Information

Comparison of the quick mild cognitive impairment (Qmci) screen and the SMMSE in screening for mild cognitive impairment.

ABSTRACT:

Introduction

differentiating mild cognitive impairment (MCI) from normal cognition (NC) is difficult. The AB Cognitive Screen (ABCS) 135, sensitive in differentiating MCI from dementia, was modified to improve sensitivity and specificity, producing the quick mild cognitive impairment (Qmci) screen.

Objective

this study compared the sensitivity and specificity of the Qmci with the Standardised MMSE and ABCS 135, to differentiate NC, MCI and dementia.

Methods

weightings and subtests of the ABCS 135 were changed and a new section 'logical memory' added, creating the Qmci. From four memory clinics in Ontario, Canada, 335 subjects (154 with MCI, 181 with dementia) were recruited and underwent comprehensive assessment. Caregivers, attending with the subjects, without cognitive symptoms, were recruited as controls (n = 630).

Results

the Qmci was more sensitive than the SMMSE and ABCS 135, in differentiating MCI from NC, with an area under the curve (AUC) of 0.86 compared with 0.67 and 0.83, respectively, and in differentiating MCI from mild dementia, AUC of 0.92 versus 0.91 and 0.91. The ability of the Qmci to identify MCI was better for those over 75 years.

Conclusion

the Qmci is more sensitive than the SMMSE in differentiating MCI and NC, making it a useful test, for MCI in clinical practice, especially for older adults.

SUBMITTER: O'Caoimh R

PROVIDER: S-EPMC3424052 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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Publications

Comparison of the quick mild cognitive impairment (Qmci) screen and the SMMSE in screening for mild cognitive impairment.

O'Caoimh Rónán R Gao Yang Y McGlade Ciara C Healy Liam L Gallagher Paul P Timmons Suzanne S Molloy D William DW

Age and ageing 20120518 5

<h4>Introduction</h4>differentiating mild cognitive impairment (MCI) from normal cognition (NC) is difficult. The AB Cognitive Screen (ABCS) 135, sensitive in differentiating MCI from dementia, was modified to improve sensitivity and specificity, producing the quick mild cognitive impairment (Qmci) screen.<h4>Objective</h4>this study compared the sensitivity and specificity of the Qmci with the Standardised MMSE and ABCS 135, to differentiate NC, MCI and dementia.<h4>Methods</h4>weightings and s ...[more]

PMID: 22610464

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Project description:BackgroundEarly detection of cognitive impairment is among the top research priorities aimed at reducing the global burden of dementia. Currently used screening tools have high sensitivity but lack specificity at their original cut-off, while decreasing the cut-off was repeatedly shown to improve specificity, but at the cost of lower sensitivity. In 2012, a new screening tool was introduced that aims to overcome these limitations - the Quick mild cognitive impairment screen (Qmci). The original English Qmci has been rigorously validated and demonstrated high diagnostic accuracy with both good sensitivity and specificity. We aimed to determine the optimal cut-off value for the German Qmci, and evaluate its diagnostic accuracy, reliability (internal consistency) and construct validity.MethodsWe retrospectively analyzed data from healthy older adults (HOA; n = 43) and individuals who have a clinical diagnosis of 'mild neurocognitive disorder' (mNCD; n = 37) with a biomarker supported characterization of the etiology of mNCD of three studies of the 'Brain-IT' project. Using Youden's Index, we calculated the optimal cut-off score to distinguish between HOA and mNCD. Receiver operating characteristic (ROC) curve analysis was performed to evaluate diagnostic accuracy based on the area under the curve (AUC). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Reliability (internal consistency) was analyzed by calculating Cronbach's α. Construct validity was assessed by analyzing convergent validity between Qmci-G subdomain scores and reference assessments measuring the same neurocognitive domain.ResultsThe optimal cut-off score for the Qmci-G was ≤ 67 (AUC = 0.96). This provided a sensitivity of 91.9% and a specificity of 90.7%. The PPV and NPV were 89.5% and 92.9%, respectively. Cronbach's α of the Qmci-G was 0.71 (CI95% [0.65 to 0.78]). The Qmci-G demonstrated good construct validity for subtests measuring learning and memory. Subtests that measure executive functioning and/or visuo-spatial skills showed mixed findings and/or did not correlate as strongly as expected with reference assessments.ConclusionOur findings corroborate the existing evidence of the Qmci's good diagnostic accuracy, reliability, and construct validity. Additionally, the Qmci shows potential in resolving the limitations of commonly used screening tools, such as the Montreal Cognitive Assessment. To verify these findings for the Qmci-G, testing in clinical environments and/or primary health care and direct comparisons with standard screening tools utilized in these settings are warranted.

Project description:Dementia and mild cognitive impairment (MCI) are underrecognized in community settings. This may be due in part to the lack of brief dementia screening tools available to clinicians. We compared 2 brief, informant-based screening tests: the AD8 and the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) in a community-based neurology practice in the midwestern United States.We examined 186 consecutive patients (44 controls, 13 with MCI, and 129 with dementia). Receiver operator characteristic curves were used to examine the ability of AD8 and IQCODE to discriminate between controls and MCI or dementia. Sensitivity, specificity, predictive values, and likelihood ratios were reported.AD8 differentiated healthy controls from MCI (P<0.001) or dementia (P<0.001), and MCI from dementia (P=0.008). The IQCODE differentiated controls and MCI from dementia (both P<0.001), and between controls and MCI (P=0.002). Both AD8 (AUC=0.953; 95% confidence interval, 0.92-0.99) and IQCODE (AUC=0.930, 95% confidence interval, 0.88-0.97) provided discrimination between controls and patients with dementia; however, the AD8 had superior sensitivity detecting dementia (99.2%) and MCI (100%) compared with the IQCODE (79.1% for dementia, 46.1% for MCI) with nonoverlapping confidence intervals. Using published cut-offs (AD8?2, IQCODE?3.4), only 1 case of dementia was missed with the AD8, whereas the IQCODE failed to detect dementia in 27 individuals. The AD8 detected MCI in all 13 individuals, whereas the IQCODE misclassified 7 individuals.Both the AD8 and IQCODE were able to detect dementia in a community setting. The AD8, however, was more successful than IQCODE in detecting MCI. If simple and efficient screening for early cognitive impairment is the goal, particularly in the early stages (e.g., for prevention trials or public screening), the combination of an informant interview (the AD8) and a brief performance measure could be considered as they meet the basic requirements of the Personalized Prevention Plan for Medicare beneficiaries.

Project description:BackgroundA digital cognitive test can be a useful and quick tool for the screening of cognitive impairment. Previous studies have shown that the diagnostic performance of digital cognitive tests is comparable with that of conventional paper-and-pencil tests. However, the use of commercially available digital cognitive tests is not common in Hong Kong, which may be due to the high cost of the tests and the language barrier. Thus, we developed a brief and user-friendly digital cognitive test called the Electronic Cognitive Screen (EC-Screen) for the detection of mild cognitive impairment (MCI) and dementia of older adults.ObjectiveThe aim of this study was to evaluate the performance of the EC-Screen for the detection of MCI and dementia in older adults.MethodsThe EC-Screen is a brief digital cognitive test that has been adapted from the Rapid Cognitive Screen test. The EC-Screen uses a cloud-based platform and runs on a tablet. Participants with MCI, dementia, and cognitively healthy controls were recruited from research clinics and the community. The outcomes were the performance of the EC-Screen in distinguishing participants with MCI and dementia from controls, and in distinguishing participants with dementia from those with MCI and controls. The cohort was randomly split into derivation and validation cohorts based on the participants' disease group. In the derivation cohort, the regression-derived score of the EC-Screen was calculated using binomial logistic regression. Two predictive models were produced. The first model was used to distinguish participants with MCI and dementia from controls, and the second model was used to distinguish participants with dementia from those with MCI and controls. Receiver operating characteristic curves were constructed and the areas under the curves (AUCs) were calculated. The performances of the two predictive models were tested using the validation cohorts. The relationship between the EC-Screen and paper-and-pencil Montreal Cognitive Assessment-Hong Kong version (HK-MoCA) was evaluated by the Pearson correlation coefficient.ResultsA total of 126 controls, 54 participants with MCI, and 63 participants with dementia were included in the study. In differentiating participants with MCI and dementia from controls, the AUC of the EC-Screen in the derivation and validation cohorts was 0.87 and 0.84, respectively. The optimal sensitivity and specificity in the derivation cohorts were 0.81 and 0.80, respectively. In differentiating participants with dementia from those with MCI and controls, the AUC of the derivation and validation cohorts was 0.90 and 0.88, respectively. The optimal sensitivity and specificity in the derivation cohort were 0.83 and 0.83, respectively. There was a significant correlation between the EC-Screen and HK-MoCA (r=-0.67, P<.001).ConclusionsThe EC-Screen is suggested to be a promising tool for the detection of MCI and dementia. This test can be self-administered or assisted by a nonprofessional staff or family member. Therefore, the EC-Screen can be a useful tool for case finding in primary health care and community settings.

Dataset Information

Comparison of the quick mild cognitive impairment (Qmci) screen and the SMMSE in screening for mild cognitive impairment.

Introduction

Objective

Methods

Results

Conclusion

Publications

Comparison of the quick mild cognitive impairment (Qmci) screen and the SMMSE in screening for mild cognitive impairment.

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