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

Funding Information: The Age, Gene/Environment Susceptibility-Reykjavik Study has been funded by National Institute on Aging contract N01-AG-12100 with contributions from the National Eye Institute; National Institute on Deafness and Other Communication Disorders; National Heart, Lung and Blood Institute; National Institute on Aging Intramural Research Program; Hjartavernd (the Icelandic Heart Association, HHSN271201200022C); and the Althingi (Icelandic Parliament). The authors thank all the study participants and the IHA staff. This work was supported by Alzheimer Nederland Fellowship WE.15-2018-05 (PI: J.M.J. Vonk), Alzheimer Nederland grant WE.03-2017-06 (PI: M.I. Geerlings), National Institute on Aging K99/R00 award K99AG066934 (PI: J.M.J. Vonk), and NWO/ZonMw Veni Grant project number 09150161810017 (PI: J.M.J. Vonk). None of the funders had any role in the design and conduct of the study, the collection, management, analysis, and interpretation of the data, the preparation and writing of the manuscript, or the decision to submit the manuscript for publication. Publisher Copyright: © 2021, The Author(s).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.Peer reviewe