Abstract
BACKGROUND: Models for forecasting individual clinical progression trajectories in early Alzheimer's disease (AD) are needed for optimizing clinical studies and patient monitoring. METHODS: Prediction models were constructed using a clinical trial training cohort (TC; n = 934) via a gradient boosting algorithm and then evaluated in two validation cohorts (VC 1, n = 235; VC 2, n = 421). Model inputs included baseline clinical features (cognitive function assessments, APOE ε4 status, and demographics) and brain magnetic resonance imaging (MRI) measures. RESULTS: The model using clinical features achieved R2 of 0.21 and 0.31 for predicting 2-year cognitive decline in VC 1 and VC 2, respectively. Adding MRI features improved the R2 to 0.29 in VC 1, which employed the same preprocessing pipeline as the TC. Utilizing these model-based predictions for clinical trial enrichment reduced the required sample size by 20% to 49%. DISCUSSION: Our validated prediction models enable baseline prediction of clinical progression trajectories in early AD, benefiting clinical trial enrichment and various applications.
Original language | English (US) |
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Pages (from-to) | 1725-1738 |
Number of pages | 14 |
Journal | Alzheimer's and Dementia |
Volume | 20 |
Issue number | 3 |
DOIs | |
State | Published - Mar 2024 |
Keywords
- clinical trial enrichment
- disease progression
- machine learning
- mild cognitive impairment
- prognosis
ASJC Scopus subject areas
- Epidemiology
- Health Policy
- Developmental Neuroscience
- Clinical Neurology
- Geriatrics and Gerontology
- Cellular and Molecular Neuroscience
- Psychiatry and Mental health