Abstract Background The global population of People Living with Dementia (PLWD) is expected to grow rapidly in the coming decades, increasing the need for personalised, generalisable, and scalable prognosis and care planning support. However, current prognostic guidance does not adequately capture the heterogeneity in dementia trajectories, and existing predictive models of dementia progression rely on costly and inaccessible data, limiting their scalability in resource-constrained settings. Methods Using clinical assessments, demographic, and medical history data from 153 12-month clinical trajectories collected over three years, two machine learning algorithms were developed to predict 12-month cognitive and functional decline in Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI). Models were externally validated on 741 trajectories from the ADNI cohort. Cognitive and functional decline were estimated using the Mini-Mental State Exam (MMSE) and Bristol Activities of Daily Living (BADL). Results The MMSE model achieves a mean absolute error (MAE) of 1.84 (95% CI: 1.64-2.04) internally and 2.19 in external validation. The BADL model achieves an MAE of 3.88 (95% CI: 3.46-4.30). Baseline scores on ideational praxis, orientation, and word recall are among the strongest predictors of cognitive decline, while independence in food preparation, finances, and dressing are among the top predictors of functional decline. Conclusions Our models use only routinely collected and easily accessible data, offering high translational potential. If implemented, our scalable, data-driven prognostic support tool could streamline clinical workflows, support personalised care planning, and provide PLWD and their families with greater clarity and reassurance.
Fogel et al. (Thu,) studied this question.