P4-078: DemTect B: A parallel version of a cognitive screening tool to detect mild cognitive impairment and dementia

2009 ◽  
Vol 5 (4S_Part_15) ◽  
pp. P454-P454
Author(s):  
Elke Kalbe ◽  
Pasquale Calabrese ◽  
Josef Kessler
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Screening Tool ◽  
Cognitive Screening ◽  
Parallel Version

Is the Clock Drawing Test a screening tool for the diagnosis of mild cognitive impairment? A systematic review

2009 ◽  
Vol 22 (1) ◽  
pp. 56-63 ◽  
Author(s):  
Lena Ehreke ◽  
Melanie Luppa ◽  
Hans-Helmut König ◽  
Steffi G. Riedel-Heller
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Literature Search ◽  
Screening Tool ◽  
Cognitive Screening ◽  
Clock Drawing Test ◽  
Clock Drawing ◽  
Drawing Test ◽  
Diagnosis Of Dementia ◽  
Mean Differences

ABSTRACTBackground:The clock drawing test (CDT) is a common and widely used cognitive screening instrument for the diagnosis of dementia. However, it has remained unclear whether it is a suitable method to identify mild cognitive impairment (MCI). The aim of this paper is to review systematically the studies concerning the utility of the CDT in diagnosing MCI.Method:A systematic literature search was conducted. All studies dealing with utility of CDT in diagnosing MCI regardless of the applied CDT scoring system and MCI concept were selected.Results:Nine relevant studies were identified. The majority of the studies compared average CDT scores of cognitively healthy and mildly impaired subjects, and four of them identified significant mean differences. If reported, sensitivity and specificity have been mostly unsatisfactory.Conclusion:CDT should not be used for MCI-screening.

scholarly journals Ten‐minute cognitive screening tool for mild cognitive impairment and prediction of pathological ß‐amyloid

2021 ◽  
Vol 17 (S6) ◽  
Author(s):  
P. Monroe Butler ◽  
Jason Osik ◽  
Joel Schwartz ◽  
Victoria Irzhevsky ◽  
Oskar Hansson ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Screening Tool ◽  
Cognitive Screening

scholarly journals 544 - Validation of a new cognitive screening tool, the Brain Health Test-7, for identification of mild cognitive impairment and early dementia in 3 differentkinds of hospital settings

2021 ◽  
Vol 33 (S1) ◽  
pp. 86-87
Author(s):  
Meng-Shiuan Shie ◽  
Mei Xian Loi ◽  
His-Chung Chen ◽  
Ming-Hsien Hsieh ◽  
Yi-Ting Lin ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Outpatient Clinic ◽  
Screening Tool ◽  
Testing Time ◽  
Cognitive Screening ◽  
Brain Health ◽  
Early Dementia ◽  
Health Test ◽  
The Brain

BackgroundThe Brain Health Test-7 (BHT-7) is a revised tool from the original BHT, containing more tests about frontal lobe function. It was developed with theaim of identifying patients with mild cognitive impairment (MCI) and early dementia.Research objectiveHere we report the validity of the BHT-7 versus the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) in differentpsychiatry or neurology clinics.MethodsPatients with memory complaints were recruited in this study from the outpatient clinic of psychiatry or neurology in 3 different kinds of hospitals. Allpatients underwent the evaluation of the BHT-7, MMSE, MoCA, and clinical dementia rating (CDR). The clinical diagnosis (normal, MCI, dementia) was made by consensus meeting, taking into account all available data.Demographic data and the scores of the MMSE, MoCA, and BHT-7 between groups were compared. Logistic regression was adopted for analysis of optimal cutoff values, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), receiver operating characteristic (ROC) curve,and the area under the ROC curve (AUC).ResultsWe enrolled a total of 1090 subjects (normal 402, MCI 317, dementia 371); of them, 705 (64.7%) were female. There was a statistically significant differencein age, years of education, and 3 cognitive test scores among the 3 groups.Compared with the MMSE and MoCA, the BHT-7 performed slightly betterthan MMSE and MoCA in differentiating MCI or dementia from the normalcontrols (Table 1). For BHT- 7, the cutoff point was 17 between normal andMCI, and 14 between normal and dementia. These cutoff points for BHT-7were consistent through 3 different clinical settings, but inconsistent for MMSE and MoCA. The testing time for the BHT-7 was about 5-7 minutes, shorter than that of the MMSE and MoCA.ConclusionCompared with MMSE and MoCA, the BHT-7 showed slightly better performance in differentiating normal from MCI or dementia subjects. The testing time for the BHT-7 was shorter, and its cutoff points were consistent through different outpatient clinic settings. The results support that BHT-7 is auseful cognitive screening tool for MCI or early dementia in various hospital settings.Table 1Comparisons of the performance of BHT-7, MMSE, MoCAAUCcutoffSENSPEPPVNPVNormal vs. MCIBHT-70.8532≦170.81700.74130.71350.8371MMSE0.8061≦270.79500.68830.66840.8091MoCA0.8316≦250.82020.67910.66840.8273Normal vs. DementiaBHT-70.9848≦140.94340.96020.95630.9484MMSE0.9693≦240.88950.96260.95650.9040MoCA0.9768≦210.92450.94280.93720.9312Normal vs. MCI + DementiaBHT-70.9241≦160.83720.84580.90280.7522MMSE0.8941≦250.72820.91520.93650.6625MoCA0.9099≦230.80810.85320.90410.7221

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 Utility of MemTrax and Machine Learning Modeling in Classification of Mild Cognitive Impairment

2020 ◽  
Vol 77 (4) ◽  
pp. 1545-1558
Author(s):  
Michael F. Bergeron ◽  
Sara Landset ◽  
Xianbo Zhou ◽  
Tao Ding ◽  
Taghi M. Khoshgoftaar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Performance Metrics ◽  
Early Stage ◽  
Characteristic Curve ◽  
Classification Performance ◽  
Cognitive Screening ◽  
Cross Sectional ◽  
Machine Learning Classification

Background: The widespread incidence and prevalence of Alzheimer’s disease and mild cognitive impairment (MCI) has prompted an urgent call for research to validate early detection cognitive screening and assessment. Objective: Our primary research aim was to determine if selected MemTrax performance metrics and relevant demographics and health profile characteristics can be effectively utilized in predictive models developed with machine learning to classify cognitive health (normal versus MCI), as would be indicated by the Montreal Cognitive Assessment (MoCA). Methods: We conducted a cross-sectional study on 259 neurology, memory clinic, and internal medicine adult patients recruited from two hospitals in China. Each patient was given the Chinese-language MoCA and self-administered the continuous recognition MemTrax online episodic memory test on the same day. Predictive classification models were built using machine learning with 10-fold cross validation, and model performance was measured using Area Under the Receiver Operating Characteristic Curve (AUC). Models were built using two MemTrax performance metrics (percent correct, response time), along with the eight common demographic and personal history features. Results: Comparing the learners across selected combinations of MoCA scores and thresholds, Naïve Bayes was generally the top-performing learner with an overall classification performance of 0.9093. Further, among the top three learners, MemTrax-based classification performance overall was superior using just the top-ranked four features (0.9119) compared to using all 10 common features (0.8999). Conclusion: MemTrax performance can be effectively utilized in a machine learning classification predictive model screening application for detecting early stage cognitive impairment.

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