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

Anticholinergic burden (AChB), the cumulative impact of medications with anticholinergic properties, is a modifiable risk factor linked to cognitive impairment, falls, and functional decline in older adults. Yet despite the availability of multiple AChB assessment tools, no consensus gold standard exists, and commonly used scales often rely on static, expert-derived drug rankings.

This narrative review synthesizes recent advances in AChB measurement and deprescribing. It critically evaluates established tools like the Anticholinergic Cognitive Burden (ACB) scale and Drug Burden Index (DBI), alongside emerging machine learning - based models such as the ML-AB scale. The review also explores the role of digital health innovations such as clinical decision support systems and wearable technologies in enhancing risk stratification and deprescribing interventions.

While traditional tools remain useful, they suffer from limitations in adaptability and integration into routine workflows. Newer AI and data-driven approaches show promise in improving predictive accuracy and scalability. A paradigm shift is emerging, with future deprescribing efforts likely to depend on hybrid systems that combine mechanistic understanding with empirical robustness. For optimal impact, these tools must be validated, implemented thoughtfully, and aligned with patient-centered outcomes in diverse care settings.