Dementia risk in the general population: large-scale external validation of prediction models in the AGES-Reykjavik study

We aimed to evaluate the external performance of prediction models for all-cause dementia or AD in the general population, which can aid selection of high-risk individuals for clinical trials and prevention. We identified 17 out of 36 eligible published prognostic models for external validation in the population-based AGES-Reykjavik Study. Predictive performance was assessed with c statistics and calibration plots. All five models with a c statistic > .75 (.76-.81) contained cognitive testing as a predictor, while all models with lower c statistics (.67-.75) did not. Calibration ranged from... Mehr ...

Verfasser: Vonk, Jet MJ
Greving, Jacoba P
Gudnason, Vilmundur
Launer, Lenore J
Geerlings, Mirjam I
Dokumenttyp: Artikel
Erscheinungsdatum: 2021
Reihe/Periodikum: European Journal of Epidemiology, vol 36, iss 10
Verlag/Hrsg.: eScholarship
University of California
Schlagwörter: Health Services and Systems / Health Sciences / Dementia / Brain Disorders / Aging / Alzheimer's Disease / Prevention / Acquired Cognitive Impairment / Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD) / Neurodegenerative / Neurological / Female / Humans / Male / Netherlands / Population Surveillance / Predictive Value of Tests / Reproducibility of Results / Risk Assessment / Risk Factors / Prognosis / Validation / Alzheimer’s disease / Public Health and Health Services / Epidemiology
Sprache: unknown
Permalink: https://search.fid-benelux.de/Record/base-27180717
Datenquelle: BASE; Originalkatalog
Powered By: BASE
Link(s) : https://escholarship.org/uc/item/5ks4w41q

We aimed to evaluate the external performance of prediction models for all-cause dementia or AD in the general population, which can aid selection of high-risk individuals for clinical trials and prevention. We identified 17 out of 36 eligible published prognostic models for external validation in the population-based AGES-Reykjavik Study. Predictive performance was assessed with c statistics and calibration plots. All five models with a c statistic > .75 (.76-.81) contained cognitive testing as a predictor, while all models with lower c statistics (.67-.75) did not. Calibration ranged from good to poor across all models, including systematic risk overestimation or overestimation for particularly the highest risk group. Models that overestimate risk may be acceptable for exclusion purposes, but lack the ability to accurately identify individuals at higher dementia risk. Both updating existing models or developing new models aimed at identifying high-risk individuals, as well as more external validation studies of dementia prediction models are warranted.