Rethinking anticholinergic burden in older adults: innovative approaches to detection and management

Area of Science:

• Gerontology
• Clinical Pharmacy
• Health Informatics

Background:

• Anticholinergic burden (AChB) is a significant risk factor for adverse outcomes in older adults, including cognitive decline and falls.
• Existing AChB assessment tools lack a consensus gold standard and often use static drug rankings, limiting their adaptability.

Purpose of the Study:

• To review recent advancements in measuring AChB and facilitating medication deprescribing.
• To critically evaluate traditional AChB tools and emerging AI-driven models.
• To explore the role of digital health in risk stratification and intervention.

Main Methods:

• Narrative review of literature on AChB measurement and deprescribing.
• Evaluation of established tools (ACB scale, DBI) and novel machine learning models (ML-AB scale).
• Exploration of digital health innovations (clinical decision support, wearables).

Main Results:

• Traditional AChB tools have limitations in adaptability and workflow integration.
• AI and data-driven approaches offer enhanced predictive accuracy and scalability.
• Digital health tools can improve risk stratification and deprescribing interventions.

Conclusions:

• A paradigm shift towards hybrid systems combining mechanistic and empirical approaches is emerging for AChB management.
• Future deprescribing efforts require validated, patient-centered tools implemented in diverse settings.
• Integrating AI and digital health innovations is crucial for optimizing AChB assessment and intervention.