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

Jet M J Vonk,Mirjam I Geerlings,Jacoba P Greving,Lenore J Launer,Vilmundur Gudnason

doi:10.1007/s10654-021-00785-x

Abstract

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.

Highlights

The global increase in prevalence and incidence of dementia—mainly due to prolonged life expectancy—carries substantial individual, societal, and economic burden [1]
Development of new models for dementia prediction has flourished across the past decades [9,10,11,12]
This study aimed to evaluate the external performance of prediction models for all-cause dementia or Alzheimer’s disease (AD) in older adults that were developed for prediction horizons of 5–10 years

Summary

Introduction

The global increase in prevalence and incidence of dementia—mainly due to prolonged life expectancy—carries substantial individual, societal, and economic burden [1]. Multiple risk factors for all-cause dementia and Alzheimer’s disease (AD) have been identified, ranging from non-modifiable factors such as genetics (e.g., APOE e4) to modifiable medical (e.g., cardiovascular health) and environmental influences (e.g., level of education) [3,4,5]. These risk factors can be used to estimate someone’s individual probability of developing all-cause dementia or AD over a specified time through multivariable prognostic modeling. External validation of a model’s ability to differentiate who does and does not develop dementia (i.e., discrimination) and the model’s agreement between predicted and observed risks (i.e., calibration) in an independent dataset can quantify optimism

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