Alzheimer’s disease (AD) has a long disease duration and a progressive course. To stop or slow down cognitive decline as early as possible, intervention studies are increasingly focusing on the earliest stage of the disease. To evaluate the effectiveness of these interventions, one ideally would want to track patients from the earliest preclinical stage, where amyloid pathology exists but cognition is still intact, to the prodromal stage, where cognitive functioning is impaired, to later and increasingly severe stages of dementia. An alternative strategy is to re-use and combine data that were previously collected. Combining different data sources can improve generalizability of findings, efficiency of future clinical trials, and identification of persons best suited for treatment at different disease stages. The aim of this thesis was to examine relevant outcomes and endpoints related to amyloid pathology in pre-dementia stages, and to examine the disease trajectory and care duration after a dementia diagnosis. In this thesis, we used different data sources and data types ranging from biomarker data to registry data to examine relevant outcomes and endpoints in AD. The relevant outcomes and endpoints in this thesis are important for the monitoring of treatment effects and for personalized predictions of whether and how a patient might advance on the AD disease spectrum. Part I focuses on preclinical and prodromal stages of AD, and Part II focuses on the disease trajectory and duration of different types of care after a dementia diagnosis.
|Award date||17 Jun 2022|
|Place of Publication||Maastricht|
|Publication status||Published - 2022|
- Alzheimer’s disease
- care use