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Background: Phase 3 randomized clinical trials within Alzheimer's Disease (AD) typically last over 18 months. Post-baseline participants can use additional treatment for Alzheimer's disease, potentially impacting the cognitive ability as evaluated by the primary endpoint. Consequently, this could overestimate or underestimate the treatment effect, depending on the distribution of usage between treatment arms.
View Article and Find Full Text PDFBackground: The advent of disease-modifying therapies in Alzheimer's disease (AD) necessitates a nuanced understanding of how therapies impact disease processes. Over the past decades, AD clinical trials have primarily relied on classical statistical analysis methodology such as the mixed model for repeated measures (MMRM) to estimate treatment effects. These conventional treatment effect quantifications are given as group differences in clinical outcome measures at a single visit.
View Article and Find Full Text PDFAlzheimer's disease (AD) is a common copathology in Lewy body dementia (LBD). The presence of AD is associated with a distinct clinical presentation, faster progression, and shorter survival in LBD. However, the relationship between-alpha synuclein and AD remains incompletely understood.
View Article and Find Full Text PDFDementia Care Research and Psychosocial Factors.
Alzheimers Dement
December 2024
University of Kentucky, Lexington, KY, USA.
Background: Emerging research suggests that complementary and supportive care programs, such as music therapy, show positive short-term impacts (e.g., purposeful engagement, positive emotions) on persons with dementia who live in care facilities.
View Article and Find Full Text PDFDementia Care Research and Psychosocial Factors.
Alzheimers Dement
December 2024
Infovital, Envigado, Colombia.
Background: Currently, the diagnosis of Alzheimer's disease dementia (ADD) is determined based on clinical criteria, as well as specific imaging and cerebrospinal fluid (CSF) biomarker profiles. However, healthcare professionals face a variety of challenges that hinder their application, such as the interpretation and integration or large amounts of data derived from neuropsychological assessment, the importance attributed to each source of information and the impact of unknown variables, among others. Therefore, this research focuses on the development of a computerized diagnostic tool based on Artificial Intelligence (AI), to strengthen the capacity of healthcare professionals in the identification and diagnosis of ADD.
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