Discriminative performance of externally validated dementia risk prediction models: a systematic review and...

Data on the external validation of current dementia risk prediction models has not yet been systematically synthesised. This systematic review and meta-analysis collated results from three previous reviews to evaluate the predictive discriminative performance of dementia risk models when validated in population-based settings.

Embase (via Ovid), Medline (via Ovid), Scopus, and Web of Science were searched from inception to June 2022 with an updated search conducted up to November 2024. Included studies (1) had a population-based cohort design; (2) assessed incident late-life (i.e. ≥ 60 years) dementia; and (3) reported predictive performance of at least one dementia risk prediction model in an independent validation sample. Information on study characteristics, dementia outcomes, prediction models (including whether they were fully validated [all original variables available and mapped] or partially validated [one or more variables missing or substituted]), and their discriminative performance were extracted in duplicate. Discrimination, quantified by the area under the receiver operating characteristic curve (AUC) or c-statistic, was pooled across studies using a random-effects model. Models were stratified by validation type: fully versus partially validated.

Thirty-six studies were included. Seventeen studies undertook full validation (14 unique prediction models) and were included in the meta-analysis. Predictor count ranged from one to 57. For all-cause dementia, RADaR showed the highest performance (c-statistic = 0.83, 95%CI: 0.80-0.86; n = 2 validations), followed by eRADAR (c-statistic = 0.81, 95%CI: 0.75-0.85; n = 2 validations). The BDSI model had the most validations (all-cause dementia c-statistic = 0.72, 95%CI: 0.69-0.75; n = 13 validations; and Alzheimer's disease c-statistic = 0.74, 95%CI: 0.61-0.87; n = 2 validations) and performed similarly across high- and middle-income counties. Most validations (76%) were conducted in high-income countries, with 24% in upper-middle income countries. Considerable variation in heterogeneity was observed across models (I² values ranging from 0 to 99%).

Several dementia risk prediction models demonstrate moderate to high external validity. The BDSI model, tested across multiple settings and dementia outcomes, showed promising generalisability. However, the limited number of fully validated models and scarcity of studies in low-income country settings highlight the need for further research on feasibility, resource requirements, and cost-effectiveness before clinical adoption.