Machine-learning prediction and risk stratification of 12-month cognitive decline in Alzheimer's disease using routine clinical and MRI data.

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Abstract

Early identification of patients with Alzheimer's disease (AD) who will experience near-term cognitive decline can support trial enrichment and risk-stratified follow-up. Using the Alzheimer's Disease Neuroimaging Initiative (ADNI), we developed two prognostic models for 12-month Mini-Mental State Examination (MMSE) decrease (≥ 3 points): (i) a clinical logistic-regression model and (ii) a random-forest model combining clinical variables with MRI-derived volumetric measures. In 306 participants with baseline AD and complete 12-month MMSE (mean age 74.8 years; baseline MMSE 23.1), 131 (42.8%) declined. Five-fold stratified cross-validation with within-fold preprocessing and imputation was used for internal validation. The clinical model achieved an area under the ROC curve (AUC) of 0.755, while the random-forest model achieved an AUC of 0.773 and provided higher net benefit across threshold probabilities of 0.20-0.80 in decision-curve analysis. Risk stratification using pre-specified cut-offs (< 0.25, 0.25-0.50, ≥ 0.50) yielded monotonic observed decline rates (13.2%, 35.3%, 67.2%). These findings suggest that a transparent two-model framework based on ADNI data provides moderate prognostic accuracy and clinically interpretable three-tier risk stratification; however, external validation and local recalibration are required before clinical implementation.

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