Comparison of the quick mild cognitive impairment (Qmci) screen and the SMMSE in screening for mild cognitive impairment.
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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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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.
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Project description:There is a need for a screening tool with capacities of accurate detection of early mild cognitive impairment (MCI) and dementia and is suitable for use in a range of languages and cultural contexts. This research aims to evaluate the psychometric and diagnostic properties of the Taiwan version of Qmci (Qmci-TW) screen and to explore the discriminating ability of the Qmci-TW in differentiating among normal controls (NCs), MCI and dementia. Thirty-one participants with dementia and 36 with MCI and 35 NCs were recruited from a neurology department of regional hospital in Taiwan. Their results on the Qmci-TW, Taiwanese version of the Montreal Cognitive Assessment (MoCA), and Traditional Chinese version of the Mini-Mental State Examination (MMSE) were compared. For analysis, we used Cronbach's ?, intraclass correlation coefficient, Spearman's ?, Kruskal-Wallis test, receiver operating characteristic curve analysis, and multivariate analysis, as appropriate. The Qmci-TW exhibited satisfactory test-retest reliability, internal consistency, and interrater reliability as well as a strong positive correlation with results from the MoCA and MMSE. The optimal cut-off score on the Qmci-TW for differentiating MCI from NC was ? 51.5/100 and dementia from MCI was ? 31/100. The MoCA exhibited the highest accuracy in differentiating MCI from NC, followed by the Qmci-TW and then MMSE; whereas, the Qmci-TW and MMSE exhibited the same accuracy in differentiating dementia from MCI, followed by the MoCA. The Qmci-TW may be a useful clinical screening tool for a spectrum of cognitive impairments.
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Project description:BackgroundAlzheimer's disease (AD) is a neurodegenerative disorder characterised by cognitive decline, behavioural and psychological symptoms of dementia (BPSD) and impairment of activities of daily living (ADL). Early differentiation of AD from mild cognitive impairment (MCI) is necessary.MethodsA total of 458 patients newly diagnosed with AD and MCI were included. Eleven batteries were used to evaluate ADL, BPSD and cognitive function (ABC). Machine learning approaches including XGboost, classification and regression tree, Bayes, support vector machines and logical regression were used to build and verify the new tool.ResultsThe Alzheimer's Disease Assessment Scale (ADAS-cog) word recognition task showed the best importance in judging AD and MCI, followed by correct numbers of auditory verbal learning test delay recall and ADAS-cog orientation. We also provided a selected ABC-Scale that covered ADL, BPSD and cognitive function with an estimated completion time of 18 min. The sensitivity was improved in the four models.ConclusionThe quick screen ABC-Scale covers three dimensions of ADL, BPSD and cognitive function with good efficiency in differentiating AD from MCI.
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Project description:As populations age and the prevalence of cognitive impairment increases, healthcare professionals and researchers require short, validated cognitive screening instruments (CSIs). As part the EIP-on-AHA Twinning Support Scheme (2016), four reference sites developed the RAPid COmmunity COGnitive screening Programme (RAPCOG) twinning project to validate translated versions of the Quick Mild Cognitive Impairment (Qmci) screen that could be adapted quickly for use with future eHealth screening and assessment programmes. Here we present the cultural adaption and translation of the Qmci-Portuguese (Qmci-P) screen as part of RAPCOG and explore its subsequent validation against two commonly-used CSIs (MMSE-P and MoCA-P) with 93 participants aged ≥65, attending ten day care centres or resident in two long-term care institutions; median age 74 (+/-15), 66% female. The Qmci-P's internal consistency was high (Cronbach's Alpha 0.82), compared with the MoCA (0.79) and SMMSE (0.54). Qmci-P screen scores moderately correlated with the SMMSE (r=0.61, 95% CI:0.45-0.72, p<0.001) and MoCA (r=0.63, 95% CI:0.36-0.80, p<0.001). The Qmci-P screen demonstrates high internal consistency and concurrent validity against more established CSIs and given its brevity (3-5mins), may be preferable for use in community settings. This project shows the potential of the EIP-on-AHA Twinning initiative to promote the scaling-up of innovative good practices.
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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.
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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.
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Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2015-08-05 | E-GEOD-63063 | biostudies-arrayexpress
Project description:Questionnaires or computer-based tests for assessing activities of daily living are well-known approaches to screen for mild cognitive impairment (MCI). However, questionnaires are subjective and computerized tests only collect simple performance data with conventional input devices such as a mouse and keyboard. This study explored the validity and discriminative power of a virtual daily living test as a new diagnostic approach to assess MCI. Twenty-two healthy controls and 20 patients with MCI were recruited. The virtual daily living test presents two complex daily living tasks in an immersive virtual reality environment. The tasks were conducted based on subject body movements and detailed behavioral data (i.e., kinematic measures) were collected. Performance in both the proposed virtual daily living test and conventional neuropsychological tests for patients with MCI was compared to healthy controls. Kinematic measures considered in this study, such as body movement trajectory, time to completion, and speed, classified patients with MCI from healthy controls, F(8, 33) = 5.648, p < 0.001, η2 = 0.578. When both hand and head speed were employed in conjunction with the immediate free-recall test, a conventional neuropsychological test, the discrimination power for screening MCI was significantly improved to 90% sensitivity and 95.5% specificity (cf. the immediate free-recall test alone has 80% sensitivity and 77.3% specificity). Inclusion of the kinematic measures in screening for MCI significantly improved the classification of patients with MCI compared to the healthy control group, Wilks' Lambda = 0.451, p < 0.001.
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