Background: Although individualized models using demographic, MRI, and biological markers have recently been applied in mild cognitive impairment (MCI), a similar study is lacking for patients with early Alzheimer's disease (AD) with biomarker evidence of abnormal amyloid in the brain.
Objective: We aimed to develop prognostic models for individualized prediction of cognitive change in early AD.
Methods: A total of 421 individuals with early AD (MCI or mild dementia due to AD) having biomarker evidence of abnormal amyloid in the brain were included in the current study. The primary cognitive outcome was the slope of change in Alzheimer's Disease Assessment Scale-cognitive subscale-13 (ADAS-Cog-13) over a period of up to 5 years.
Results: A model combining demographics, baseline cognition, neurodegenerative markers, and CSF AD biomarkers provided the best predictive performance, achieving an overfitting-corrected R of 0.59 (bootstrapping validation). A nomogram was created to enable clinicians or trialists to easily and visually estimate the individualized magnitude of cognitive change in the context of patient characteristics. Simulated clinical trials suggested that the inclusion of our nomogram into the enrichment strategy would lead to a substantial reduction of sample size in a trial of early AD.
Conclusions: Our findings may be of great clinical relevance to identify individuals with early AD who are likely to experience fast cognitive deterioration in clinical practice and in clinical trials.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11491935 | PMC |
| http://dx.doi.org/10.3233/ADR-240049 | DOI Listing |
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