Clock Drawing Test: A Simple Scoring System for the Accurate Screening of Cognitive Impairment in Patients with Mild Cognitive Impairment and Dementia

Rakuša, Martin; Jensterle, J; Mlakar, Janez

doi:10.1159/000490381

Cited by 31 publications

(30 citation statements)

References 26 publications

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“…Brief screening tests are useful instruments for early and simple identification of cognitive impairment in neurodegenerative disorders, especially in Alzheimer disease (AD) [1-6]. The Montreal Cognitive Assessment (MoCA) was developed to detect mild cognitive impairment (MCI) [7].…”

Section: Introductionmentioning

confidence: 99%

Validation of the Czech Montreal Cognitive Assessment for Mild Cognitive Impairment due to Alzheimer Disease and Czech Norms in 1,552 Elderly Persons

Bartoš

Fayette

2018

Dement Geriatr Cogn Disord

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Background: The Czech version of the Montreal Cognitive Assessment (MoCA-CZ) and delayed recall of 5 words have not been validated in patients with mild cognitive impairment (MCI) due to Alzheimer disease (AD) and compared to norms of a large population. Method: The MoCA-CZ was administered to 1,600 elderly individuals in 2 groups consisting of 48 patients with MCI due to AD (AD-MCI) and 1,552 normal elderly adults. Results: MoCA-CZ scores were significantly lower in the AD-MCI patients than in the normal elderly (21 ± 4 vs. 26 ± 3 points; p = 0.03). Under the recommended cutoff score of ≤25, the MoCA-CZ demonstrated an excellent sensitivity of 94% but a low specificity of 62%. When the score was reduced to ≤24, the MoCA-CZ showed an optimal sensitivity of 87% for AD-MCI and a specificity of 72%. Normal elderly persons should recall at least 2 words after delay (sensitivity 80%, specificity 74%). Several cutoff points were derived from normative data stratified by age and education. Conclusions: The cutoff for AD-MCI and stratified norms are available for the MoCA total score and delayed recall of the Czech version. The cut-off scores of the MoCA-CZ, sensitivity, and specificity are lower than in the original study.

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Section: Introductionmentioning

confidence: 99%

Validation of the Czech Montreal Cognitive Assessment for Mild Cognitive Impairment due to Alzheimer Disease and Czech Norms in 1,552 Elderly Persons

Bartoš

Fayette

2018

Dement Geriatr Cogn Disord

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“…These cutoff values have been reported to ensure good sensitivity and specificity in identifying normal cognition versus mild cognitive impairment and mild cognitive impairment versus dementia in the elderly Chinese individuals (Li, Jia, & Yang, 2016). Although the discriminant validity of the CDT total scores has been shown in previous studies (Babins et al, 2008;Grande et al, 2013;Rakusa, Jensterle, & Mlakar, 2018) (Cohen, 1988). Spearman's correlation analysis was used to assess the relationship between the CDT and MMSE scores.…”

Section: 000mentioning

confidence: 94%

Clock‐drawing test: Normative data of three quantitative scoring methods for Chinese‐speaking adults in Shijiazhuang City and clinical utility in patients with acute ischemic stroke

et al. 2020

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“…An alternative approach is to use the Clock Drawing Test (CDT) [4], which provides a simple scoring system for the rapid screening for cognitive impairment in patients with mild cognitive impairment [5].…”

Section: Introductionmentioning

confidence: 99%

“…Figures 3,4,5,and 6 depict the contribution of the top 10 features for predicting the class for the respective problems. The right part of the figure represents the probabilities of the different classes and the names of the top 10 relevant features and their values.…”

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confidence: 99%

Identifying the Presence and Severity of Dementia by Applying Interpretable Machine Learning Techniques on Structured Clinical Records

Vyas

Aisopos

Vidal

et al. 2021

Preprint

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Background: Dementia occurs through declining of cognitive abilities, and its early detection stands essential for effective preventive measures. However, mainstream diagnostic tests and screening tools, such as Cambridge Cognition Examination (CAMCOG) and Mini-Mental State Examination (MMSE), often fail to detect dementia accurately. Various graph-based or feature-dependent prediction and progression models have been proposed. Whenever these models exploit information in the patients’ electronic medical records, they represent promising options to identify the presence and severity of dementia more precisely. Methods: The methods presented in this paper aim to address two problems related to dementia: (a) Basic diagnosis: identifying the presence of dementia in individuals, and (b) Severity diagnosis: predicting the presence of dementia, as well as the severity of the disease. We formulate these two tasks as classification problems and address them using machine learning models based on random forests, analysing structured clinical data from an elderly population cohort. We perform a hybrid data curation method where a dementia expert is in the loop to ensure meaningful curation decisions. We then employ the machine learning algorithms that classify individual episodes into a specific dementia class. Decision trees are also used for enhancing the explainability of decisions made by prediction models, allowing medical experts to identify the most crucial patient features and their threshold values for the detection of dementia. Results: Our experiment results prove that clinical record features (such as age or comorbidities), along with baseline arithmetic or memory tests, can predict dementia and its severity with high accuracy. In specific, our prediction models have reached an average f1-score of 0.93 and 0.85 for problems (a) and (b), respectively. Moreover, the decision trees produced for the two issues empower the interpretability of the prediction models. Conclusions: This study proves that there can be an accurate estimation of the existence and severity of dementia disease by analysing various clinical features and cognitive tests from the past episodes of the elderly population. Moreover, a set of rules represent building blocks in the efficiently patient classification. Relevant clinical and screening test features (e.g., simple arithmetic or animal fluency task) represent precise predictors without calculating the scores of mainstream cognitive tests such as MMSE and CAMCOG. They identify not only meaningful features but also justifications of classifications. As a result, the predictive power of machine learning models over curated clinical data is demonstrated, paving the way for accurate diagnosis of dementia.

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Clock Drawing Test: A Simple Scoring System for the Accurate Screening of Cognitive Impairment in Patients with Mild Cognitive Impairment and Dementia

Cited by 31 publications

References 26 publications

Validation of the Czech Montreal Cognitive Assessment for Mild Cognitive Impairment due to Alzheimer Disease and Czech Norms in 1,552 Elderly Persons

Validation of the Czech Montreal Cognitive Assessment for Mild Cognitive Impairment due to Alzheimer Disease and Czech Norms in 1,552 Elderly Persons

Clock‐drawing test: Normative data of three quantitative scoring methods for Chinese‐speaking adults in Shijiazhuang City and clinical utility in patients with acute ischemic stroke

Identifying the Presence and Severity of Dementia by Applying Interpretable Machine Learning Techniques on Structured Clinical Records

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