Disease-Specific Risk Models for Predicting Dementia: An Umbrella Review

Abstract

Dementia is a leading cause of disability and death globally. Individuals with diseases such as cardiovascular, cardiometabolic and cerebrovascular disease are often at increased dementia risk. However, while numerous models have been developed to predict dementia, they are often not tailored to disease-specific groups. Yet, different disease groups may have unique risk factor profiles and tailored models that account for these differences may have enhanced predictive accuracy. In this review, we synthesise findings from three previous systematic reviews on dementia risk model development and testing to present an overview of the literature on dementia risk prediction modelling in people with a history of disease. Nine studies met the inclusion criteria. Currently, disease-specific models have only been developed in people with a history of diabetes where demographic, disease-specific and comorbidity data were used. Some existing risk models, including CHA₂DS₂-VASc and CHADS₂, have been externally validated for dementia outcomes in those with atrial fibrillation and heart failure. One study developed a dementia risk model for their whole population, which had similar predictive accuracy when applied in a sub-sample with stroke. This emphasises the importance of considering disease status in identifying key predictors for dementia and generating accurate prediction models for dementia.

Keywords: comorbidity; dementia; risk factors.