Predicting risk of 2-year incident dementia using the CAMCOG total and subscale scores

Restaino, Marialuisa; Matthews, Fiona E.; Minett, Thaís; Albanese, Emiliano; Brayne, Carol; Stephan, Blossom C M

doi:10.1093/ageing/aft082

Cited by 13 publications

(15 citation statements)

References 18 publications

(22 reference statements)

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“…The AUCs ranged from 0.81 to 0.88 in 3 years’ follow-up. The predictive performances of the Cognitive Abilities Screening Instrument,18 Clock Drawing Test,16 Cambridge Cognitive Examination24 and the total score of the Consortium to Establish a Registry for Alzheimer’s Disease neuropsychological assessment battery20 were also tested, and these models showed moderate accuracy (AUC ranged from 0.74 to 0.89). But none of them have been validated externally.…”

Section: Resultsmentioning

confidence: 99%

Models for predicting risk of dementia: a systematic review

Hou

Feng

Zhang

et al. 2018

J Neurol Neurosurg Psychiatry

132

147

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Section: Resultsmentioning

confidence: 99%

Models for predicting risk of dementia: a systematic review

Hou

Feng

Zhang

et al. 2018

J Neurol Neurosurg Psychiatry

132

147

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show abstract

“…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. In ten studies[ 7 , 10 , 12 – 14 , 16 , 20 – 22 , 26 ] the outcome tested was all-cause dementia, in nine studies[ 8 , 9 , 11 , 15 , 17 – 19 , 23 , 24 ] the outcome was AD and two studies had separate models for both AD and all-cause dementia [ 4 , 25 ]. The number of predictors ranged from one[ 22 ] to 19[ 25 ].…”

Section: Resultsmentioning

confidence: 99%

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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“…In contrast to our findings, a previous population study which included informant report and clinical judgment found high sensitivity and low specificity. 22 This suggests the testable hypothesis that both informant reports and clinical judgment enhance sensitivity, perhaps at the expense of specificity.…”

Section: Discussionmentioning

confidence: 99%

How Well Do MCI Criteria Predict Progression to Severe Cognitive Impairment and Dementia?

Ganguli

Lee²,

Snitz

et al. 2014

Alzheimer Disease &Amp; Associated Disorders

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Background The International Working Group (IWG) criteria for mild cognitive impairment (MCI) have variable utility for predicting progression to dementia, partly depending on setting. We explored an empiric approach to optimize the criteria and cutpoints in a population study. Methods In a cohort of adults aged 65+, we identified 1129 individuals with normal or only mildly impaired cognition by cognitive classification, and 1146 individuals without dementia (Clinical Dementia Rating <1). Operationally defining the IWG criterion set, we examined its sensitivity and specificity for the development of severe cognitive impairment and dementia (CDR>=1) over four years. We then disaggregated the criteria and used Classification and Regression Tree (CART) analyses to identify the optimal predictive model. Results The operational IWG criteria had 49% sensitivity and 86% specificity for the outcome of severe cognitive impairment, and 40% sensitivity and 84% specificity for the outcome of dementia. CART modeling improved sensitivity to 82% for the cognitive outcome and 76% for the dementia outcome; specificity remained high. Memory scores were the most important predictors for both outcomes. The optimal cutpoints were around 1.0 SD below the age-education mean. The best fit was observed when prediction was modeled separately for each age-education group. Conclusions Objective cognitive measurements contributed more to the prediction of dementia than subjective and functional measures. Those with less education only required memory testing, while those with more education required assessment of several cognitive domains. Where only overall norms are available, the appropriate threshold will vary according to the individual's age and education.

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Predicting risk of 2-year incident dementia using the CAMCOG total and subscale scores

Cited by 13 publications

References 18 publications

Models for predicting risk of dementia: a systematic review

Models for predicting risk of dementia: a systematic review

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

How Well Do MCI Criteria Predict Progression to Severe Cognitive Impairment and Dementia?

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