Does the Clock Drawing Test Predict Dementia? Results of the Leipzig Longitudinal Study of the Aged (LEILA 75+)

Ehreke, Lena; Luppa, Melanie; König, Hans‐Helmut; Villringer, Arno; Riedel‐Heller, Steffi G.

doi:10.1159/000323317

Cited by 15 publications

(13 citation statements)

References 77 publications

(62 reference statements)

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“…13 new study populations[ 7 – 20 ] have been used to create risk models compared to the first review (all literature till 2009)[ 1 ]. There was however some overlap including use of data from the Cardiovascular Health Cognition Study[ 20 , 21 ], Leipzig Longitudinal Study of the Aged (LEILA 75+)[ 22 ], Einstein Aging Study[ 23 ], Vienna TransDanube Aging[ 24 ], Canadian Study of Health and Aging[ 25 ] and the Kungsholmen Project[ 4 ]. Sample sizes ranged from 194[ 11 ] to 29,961[ 13 ].…”

Section: Resultsmentioning

confidence: 99%

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Current Developments in Dementia Risk Prediction Modelling: An Updated Systematic Review

et al. 2015

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BackgroundAccurate identification of individuals at high risk of dementia influences clinical care, inclusion criteria for clinical trials and development of preventative strategies. Numerous models have been developed for predicting dementia. To evaluate these models we undertook a systematic review in 2010 and updated this in 2014 due to the increase in research published in this area. Here we include a critique of the variables selected for inclusion and an assessment of model prognostic performance.MethodsOur previous systematic review was updated with a search from January 2009 to March 2014 in electronic databases (MEDLINE, Embase, Scopus, Web of Science). Articles examining risk of dementia in non-demented individuals and including measures of sensitivity, specificity or the area under the curve (AUC) or c-statistic were included.FindingsIn total, 1,234 articles were identified from the search; 21 articles met inclusion criteria. New developments in dementia risk prediction include the testing of non-APOE genes, use of non-traditional dementia risk factors, incorporation of diet, physical function and ethnicity, and model development in specific subgroups of the population including individuals with diabetes and those with different educational levels. Four models have been externally validated. Three studies considered time or cost implications of computing the model.InterpretationThere is no one model that is recommended for dementia risk prediction in population-based settings. Further, it is unlikely that one model will fit all. Consideration of the optimal features of new models should focus on methodology (setting/sample, model development and testing in a replication cohort) and the acceptability and cost of attaining the risk variables included in the prediction score. Further work is required to validate existing models or develop new ones in different populations as well as determine the ethical implications of dementia risk prediction, before applying the particular models in population or clinical settings.

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Section: Resultsmentioning

confidence: 99%

“…Sample sizes ranged from 194[ 11 ] to 29,961[ 13 ]. Follow-up duration ranged from 1.4[ 22 ] to 20 years[ 7 ]. Follow-up rate ranged from 66%[ 12 ] to 86%[ 19 ], with 10 studies[ 4 , 7 , 11 , 14 , 15 , 17 , 20 , 21 , 23 , 25 ] not reporting attrition or dropout.…”

Section: Resultsmentioning

confidence: 99%

Current Developments in Dementia Risk Prediction Modelling: An Updated Systematic Review

et al. 2015

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“…Our results were obtained in a clinic setting, so the results may not generalize to community-based populations. Recent work by others has sought to use information obtained from the MMSE or from the Clock Drawing Test to predict conversion to dementia in population-based samples, with modest success to date in terms of diagnostic accuracy (∼70%) [58,59]. In a community-based study of incident dementia cases, predictors of cognitive decline were found to differ from those identified in similarly-aged control cases [60].…”

Section: Discussionmentioning

confidence: 99%

Predictors of Future Cognitive Decline in Persons with Mild Cognitive Impairment

Xie

Mayo

Koski

2011

Dement Geriatr Cogn Disord

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This study sought first to identify individual items of the Mini-Mental State Examination (MMSE) and demographic variables at baseline that predicted the trajectories of cognitive change among patients with mild cognitive impairment (MCI), and second to quantify the risk of cognitive decline in such patients based on their pattern of failure of MMSE items. 187 MCI patients were evaluated serially with the MMSE for up to 3.5 years. Patients who followed a declining cognitive trajectory differed from the stable reference group in their baseline profile of MMSE test performance. Patient age and performance on delayed recall, constructional praxis, attention, and orientation to time and floor predicted future cognitive decline with good accuracy (79.9%) and specificity (86.4%), and moderate sensitivity (67.2%). These results are presented in the form of a simple clinical tool for quantifying risk of future cognitive decline in MCI.

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“…Ehreke et al [24] claimed that the CDT does not exhibit sufficient quality to screen for MCI. Nevertheless, in a later publication, these authors recommended it as a possible predictive test for dementia [25]. A validation study of Cognistat concluded that this tool has moderate validity in the detection of MCI and mild dementia but that, on its own, it is not sufficient for diagnosing MCI or mild dementia [26].…”

Section: Discussionmentioning

confidence: 99%

Brief Tests such as the Clock Drawing Test or Cognistat Can Be Useful Predictors of Conversion from MCI to Dementia in the Clinical Assessment of Outpatients

Nesset

Kersten

Ulstein

2014

Dement Geriatr Cogn Disord Extra

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Background: The identification of patients with mild cognitive impairment (MCI) who are at high risk of conversion to dementia is a challenging clinical task. Aims: To investigate whether simple cognitive screening tests can predict the conversion from MCI to dementia and to study the impact of different patient characteristics on the progression rate. Methods: A retrospective, longitudinal study of 90 outpatients diagnosed with MCI at a psychogeriatric clinic in Norway was conducted. Baseline scores on the Mini-Mental State Examination (MMSE), Clock Drawing Test (CDT), and Neurobehavioral Cognitive Status Examination (Cognistat) were related to ICD-10 diagnosis during 46 months. The influence of demographic, life situational, and clinical data were analyzed. Results: Sixty-four patients were diagnosed with dementia, significantly more females (82%) than males (50%) (p < 0.01). Low scores on the CDT [adjusted hazard ratio (HR) = 0.85; 95% CI 0.73-0.97; p = 0.020] and Cognistat (adjusted HR = 0.78; 95% CI 0.65-0.93; p = 0.007) significantly predicted the conversion from MCI to dementia, whereas the MMSE score did not. Conclusions: A high proportion of patients converted from MCI to dementia within 46 months, and females seem to be at higher risk. CDT and Cognistat significantly predicted the conversion from MCI to dementia and are therefore considered appropriate tests in clinical practice.

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Does the Clock Drawing Test Predict Dementia? Results of the Leipzig Longitudinal Study of the Aged (LEILA 75+)

Cited by 15 publications

References 77 publications

Current Developments in Dementia Risk Prediction Modelling: An Updated Systematic Review

Current Developments in Dementia Risk Prediction Modelling: An Updated Systematic Review

Predictors of Future Cognitive Decline in Persons with Mild Cognitive Impairment

Brief Tests such as the Clock Drawing Test or Cognistat Can Be Useful Predictors of Conversion from MCI to Dementia in the Clinical Assessment of Outpatients

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