O1-02-07: The CAIDE dementia risk score: A practical tool to predict dementia risk in 20 years among middle aged persons

Abstract There are several shortcomings of the currently available risk prediction models for dementia. We developed a risk prediction model for dementia using machine-learning approach and compared its performance with traditional approaches. Data were from the Health, Aging, and Body Composition Study, comprising 3,075 older adults (at least 70 years). Dementia was defined as (1) use of a prescribed dementia medication, (2) adjudicated dementia diagnosis, or (3) a race-stratified cognitive decline>1.5 SDs from the baseline mean. We selected 275 predictors collected from questionnaires, imaging data, performance testing, and biospecimen. We used random survival forest (RSF) to build the full model and rank the importance of predictors. Subsequently, we built parsimonious models with top-20 predictors using RSF and Cox regression. A dementia risk score was developed using top-ranked variables. We used the C-statistic for performance evaluation. Over a median of 11.4 years of follow-up, 659 dementias (21.4%) occurred. The RSF model (both including all and top-20 variables) showed a higher C-statistic than the regression model. Digit symbol score, physical performance battery, finger tapping score, weight change since age 50, serum adiponectin, and APOE genotype were the top-6 variables. We created a dementia risk score (0-10) using the top-6 variables. A 1-unit increase in the risk score was associated with an 8% higher risk of dementia. The risk score demonstrated good discrimination (C-statistic=0.75). Machine learning methods offered improvement over traditional approaches in predicting dementia. The risk prediction score derived from a parsimonious model had good prediction performance.

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Cardiovascular Risk Assessed by Reynolds Risk Score in Relation to Waist Circumference in Apparently Healthy Middle-Aged Population in Montenegro

Acta Clinica Croatica ◽

10.20471/acc.2018.57.01.03 ◽

2018 ◽

Vol 57 (1) ◽

pp. 22-30 ◽

Cited By ~ 1

Author(s):

Aleksandra Klisić

Keyword(s):

Cardiovascular Risk ◽

Waist Circumference ◽

Risk Score ◽

Middle Aged ◽

Aged Population ◽

Reynolds Risk Score ◽

Apparently Healthy

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Development and Validation of a Novel Dementia Risk Score in the UK Biobank Cohort

10.31234/osf.io/5yvjr ◽

2021 ◽

Author(s):

Melis Anatürk ◽

Raihaan Patel ◽

Georgios Georgiopoulos ◽

Danielle Newby ◽

Anya Topiwala ◽

...

Keyword(s):

At Risk ◽

Cardiovascular Risk ◽

Risk Score ◽

Disease Risk ◽

Risk Index ◽

Risk Scores ◽

Uk Biobank ◽

Dementia Risk ◽

History Of ◽

The Uk

INTRODUCTION: Current prognostic models of dementia have had limited success in consistently identifying at-risk individuals. We aimed to develop and validate a novel dementia risk score (DRS) using the UK Biobank cohort.METHODS: After randomly dividing the sample into a training (n=166,487, 80%) and test set (n=41,621, 20%), logistic LASSO regression and standard logistic regression were used to develop the UKB-DRS.RESULTS: The score consisted of age, sex, education, apolipoprotein E4 genotype, a history of diabetes, stroke, and depression, and a family history of dementia. The UKB-DRS had good-to-strong discrimination accuracy in the UKB hold-out sample (AUC [95%CI]=0.79 [0.77, 0.82]) and in an external dataset (Whitehall II cohort, AUC [95%CI]=0.83 [0.79,0.87]). The UKB-DRS also significantly outperformed four published risk scores (i.e., Australian National University Alzheimer’s Disease Risk Index (ANU-ADRI), Cardiovascular Risk Factors, Aging, and Dementia score (CAIDE), Dementia Risk Score (DRS), and the Framingham Cardiovascular Risk Score (FRS) across both test sets.CONCLUSION: The UKB-DRS represents a novel easy-to-use tool that could be used for routine care or targeted selection of at-risk individuals into clinical trials.

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