P3-088: The AD8 dementia screening test detects mild cognitive impairment

2012 ◽  
Vol 8 (4S_Part_13) ◽  
pp. P483-P483 ◽  
Author(s):  
James Galvin ◽  
Catherine Roe ◽  
John Morris
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Screening Test ◽  
Dementia Screening

scholarly journals Usefulness of the japanese version of rapid dementia screening test for mild cognitive impairment in older patients with cardiovascular disease

2021 ◽  
Vol 20 (Supplement_1) ◽  
Author(s):  
T Adachi ◽  
Y Tsunekawa ◽  
T Kameyama ◽  
K Kobayashi ◽  
A Matsuoka ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Cognitive Impairment ◽  
Clinical Practice ◽  
Mild Cognitive Impairment ◽  
Cognitive Decline ◽  
Screening Test ◽  
Older Patients ◽  
Roc Analysis ◽  
Japanese Version ◽  
Dementia Screening

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): JSPS KAKENHI Background Cognitive decline is common among older patients with cardiovascular disease and can decrease their self-management abilities. Therefore, early detection of cognitive decline is clinically important, as it can help guide effective home-based care measures, including education of family members and deployment of healthcare resources. However, the standard instruments for identifying mild cognitive impairment (MCI) are not always feasible in clinical practice. Purpose This study evaluated whether MCI could be detected using the Japanese version of the Rapid Dementia Screening Test (RDST-J), which is a simple screening tool for identifying cognitive decline. Methods This cross-sectional study included patients who were ≥ 65 years old and hospitalised because of cardiovascular disease. Patients with a pre-hospitalisation diagnosis of dementia were excluded. Each patient’s cognitive function had been measured at discharge using the RDST-J and the Japanese version of the Montreal Cognitive Assessment (MoCA-J), which is a standard tool for MCI screening. The RDST-J includes a number transcoding task and a supermarket task, and can be completed in 3 min (range: 0–12 points). The MoCA-J assesses nine domains of cognition and requires 10–15 min to complete (range: 0–30 points). The correlation between the two scores was evaluated using Spearman’s rank correlation coefficient. Receiver operating characteristic (ROC) analysis was also conducted to evaluate whether the RDST-J could identify MCI, which was defined as a MoCA-J score of ≤ 25 points. Results The study included 78 patients (the mean age: 77.2 ± 8.9 years, men: 56.4%). Based on a MoCA-J score of ≤ 25 points, MCI was identified in 73.1% (n = 57) of the patients. The RDST-J and MoCA-J scores were strongly correlated (r = 0.835, p <0.001). The ROC analysis revealed that an RDST-J score of ≤ 9 points provided 75.4% sensitivity and 95.2% specificity for identifying MCI, with an area under the curve of 0.899 (95% confidence interval: 0.835–0.964, Figure 1). The same cut-off value was identified when excluding patients with a high probability of dementia (RDST-J score of ≤ 4 points). Conclusions The RDST-J is a simple instrument and its score was highly correlated with the standard test for identifying MCI in older patients with cardiovascular disease. Our results suggest that the RDST-J may be useful for routine cognitive assessments in clinical practice. Longitudinal studies are needed to evaluate whether the RDST-J scores respond to changes in cognitive status, as well as whether this tool can be used to predict adverse health outcomes after discharge.

scholarly journals Psychometric Properties of the Persian Montreal Cognitive Assessment in Mild Cognitive Impairment and Alzheimer Disease

2021 ◽  
pp. 51-57
Author(s):  
Vahid Rashedi ◽  
Mahshid Foroughan ◽  
Negin Chehrehnegar
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Alzheimer Disease ◽  
Psychometric Properties ◽  
Cognitive Assessment ◽  
Screening Test ◽  
Cognitive Status ◽  
Montreal Cognitive Assessment ◽  
Cutoff Score ◽  
Persian Version

Introduction: The Montreal Cognitive Assessment (MoCA) is a cognitive screening test widely used in clinical practice and suited for the detection of Mild Cognitive Impairment (MCI). The aims were to evaluate the psychometric properties of the Persian MoCA as a screening test for mild cognitive dysfunction in Iranian older adults and to assess its accuracy as a screening test for MCI and mild Alzheimer disease (AD). Method: One hundred twenty elderly with a mean age of 73.52 ± 7.46 years participated in this study. Twenty-one subjects had mild AD (MMSE score ≤21), 40 had MCI, and 59 were cognitively healthy controls. All the participants were administered the Mini-Mental State Examination (MMSE) to evaluate their general cognitive status. Also, a battery of comprehensive neuropsychological assessments was administered. Results: The mean score on the Persian version of the MoCA and the MMSE were 19.32 and 25.62 for MCI and 13.71 and 22.14 for AD patients, respectively. Using an optimal cutoff score of 22 the MoCA test detected 86% of MCI subjects, whereas the MMSE with a cutoff score of 26 detected 72% of MCI subjects. In AD patients with a cutoff score of 20, the MoCA had a sensitivity of 94% whereas the MMSE detected 61%. The specificity of the MoCA was 70% and 90% for MCI and AD, respectively. Discussion: The results of this study show that the Persian version of the MoCA is a reliable screening tool for detection of MCI and early stage AD. The MoCA is more sensitive than the MMSE in screening for cognitive impairment, proving it to be superior to MMSE in detecting MCI and mild AD.

Validity of a novel computerized screening test system for mild cognitive impairment

2018 ◽  
Vol 30 (10) ◽  
pp. 1455-1463 ◽  
Author(s):  
Jin-Hyuck Park ◽  
Minye Jung ◽  
Jongbae Kim ◽  
Hae Yean Park ◽  
Jung-Ran Kim ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Elderly People ◽  
Predictive Value ◽  
Screening Test ◽  
Concurrent Validity ◽  
Test System ◽  
Screening Tests ◽  
Validity And Reliability ◽  
Healthy Elderly

ABSTRACTBackground:The mobile screening test system for screening mild cognitive impairment (mSTS-MCI) was developed for clinical use. However, the clinical usefulness of mSTS-MCI to detect elderly with MCI from those who are cognitively healthy has yet to be validated. Moreover, the comparability between this system and traditional screening tests for MCI has not been evaluated.Objective:The purpose of this study was to examine the validity and reliability of the mSTS-MCI and confirm the cut-off scores to detect MCI.Method:The data were collected from 107 healthy elderly people and 74 elderly people with MCI. Concurrent validity was examined using the Korean version of Montreal Cognitive Assessment (MoCA-K) as a gold standard test, and test–retest reliability was investigated using 30 of the study participants at four-week intervals. The sensitivity, specificity, positive predictive value, and negative predictive value (NPV) were confirmed through Receiver Operating Characteristic (ROC) analysis, and the cut-off scores for elderly people with MCI were identified.Results:Concurrent validity showed statistically significant correlations between the mSTS-MCI and MoCA-K and test–rests reliability indicated high correlation. As a result of screening predictability, the mSTS-MCI had a higher NPV than the MoCA-K.Conclusions:The mSTS-MCI was identified as a system with a high degree of validity and reliability. In addition, the mSTS-MCI showed high screening predictability, indicating it can be used in the clinical field as a screening test system for mild cognitive impairment.

Comparison of the Mini-Mental State Examination and Computerized Brief Cognitive Screening Test as Cognitive Screening Tools in Patients with Mild Cognitive Impairment

2021 ◽  
Vol 18 ◽  
Author(s):  
Che-Sheng Chu ◽  
I-Chen Lee ◽  
Chuan-Cheng Hung ◽  
I-Ching Lee ◽  
Chi-Fa Hung ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Normal Control ◽  
Internal Consistency ◽  
Mental State ◽  
Screening Test ◽  
Mini Mental State Examination ◽  
Cognitive Screening ◽  
Validity And Reliability ◽  
State Examination

Background: The aim of this study was to establish the validity and reliability of the Computerized Brief Cognitive Screening Test (CBCog) for early detection of cognitive impairment. Method: One hundred and sixty participants, including community-dwelling and out-patient volunteers (both men and women) aged ≥ 65 years, were enrolled in the study. All participants were screened using the CBCog and Mini-Mental State Examination (MMSE). The internal consistency of the CBCog was analyzed using Cronbach’s α test. Areas under the curves (AUCs) of receiver operating characteristic analyses were used to test the predictive accuracy of the CBCog in detecting mild cognitive impairment (MCI) in order to set an appropriate cutoff point. Results: The CBCog scores were positively correlated with the MMSE scores of patients with MCI-related dementia (r = 0.678, P < .001). The internal consistency of the CBCog (Cronbach’s α) was 0.706. It was found that the CBCog with a cutoff point of 19/20 had a sensitivity of 97.5% and a specificity of 53.7% for the diagnosis of MCI with education level ≥ 6 years. The AUC of the CBCog for discriminating the normal control elderly from patients with MCI (AUC = 0.827, P < 0.001) was larger than that of the MMSE for discriminating the normal control elderly from patients with MCI (AUC= 0.819, P < .001). Conclusion: The CBCog demonstrated to have sufficient validity and reliability to evaluate mild cognitive impairment, especially in highly educated elderly people.

scholarly journals Mild cognitive impairment, dementia, and cognitive dysfunction screening using machine learning

2020 ◽  
Vol 48 (7) ◽  
pp. 030006052093688
Author(s):  
Daehyuk Yim ◽  
Tae Young Yeo ◽  
Moon Ho Park
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Dysfunction ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Kappa Statistics ◽  
Machine Learning Algorithm ◽  
Neuropsychological Screening ◽  
Dementia Screening

Objective To develop a machine learning algorithm to identify cognitive dysfunction based on neuropsychological screening test results. Methods This retrospective study included 955 participants: 341 participants with dementia (dementia), 333 participants with mild cognitive impairment (MCI), and 341 participants who were cognitively healthy. All participants underwent evaluations including the Mini-Mental State Examination and the Montreal Cognitive Assessment. Each participant’s caregiver or informant was surveyed using the Korean Dementia Screening Questionnaire at the same visit. Different machine learning algorithms were applied, and their overall accuracies, Cohen’s kappa, receiver operating characteristic curves, and areas under the curve (AUCs) were calculated. Results The overall screening accuracies for MCI, dementia, and cognitive dysfunction (MCI or dementia) using a machine learning algorithm were approximately 67.8% to 93.5%, 96.8% to 99.9%, and 75.8% to 99.9%, respectively. Their kappa statistics ranged from 0.351 to 1.000. The AUCs of the machine learning models were statistically superior to those of the competing screening model. Conclusion This study suggests that a machine learning algorithm can be used as a supportive tool in the screening of MCI, dementia, and cognitive dysfunction.

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