TY - JOUR
T1 - Dementia risk in the general population
T2 - large-scale external validation of prediction models in the AGES-Reykjavik study
AU - Vonk, Jet M. J.
AU - Greving, Jacoba P.
AU - Gudnason, Vilmundur
AU - Launer, Lenore J.
AU - Geerlings, Mirjam I.
PY - 2021/10/1
Y1 - 2021/10/1
N2 - 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.
AB - 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.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85111097186&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/34308533
U2 - https://doi.org/10.1007/s10654-021-00785-x
DO - https://doi.org/10.1007/s10654-021-00785-x
M3 - Article
C2 - 34308533
SN - 0393-2990
VL - 36
SP - 1025
EP - 1041
JO - European Journal of Epidemiology
JF - European Journal of Epidemiology
IS - 10
ER -