Aims: To examine patient characteristics that predict adverse anticholinergic-type events in older people.

Methods: This retrospective population-level study included 2,248 hospitalised patients. Individual data on medicines that are commonly associated with anticholinergic events (delirium, constipation and urinary retention) were identified. Patient characteristics examined were medicines with anticholinergic effects (ACh burden), age, sex, non-anticholinergic medicines (non-ACM), Charlson comorbidity index scores and ethnicity. The Akaike information criterion was used for model selection. The data were analysed using logistic regression models for anticholinergic events using the software NONMEM.

Results: ACh burden was found to be a significant independent predictor for developing an anticholinergic event [adjusted odds ratio (aOR): 3.21, 95% CI: 1.23-5.81] for those taking an average of 5 anticholinergic medicines compared to those taking 1. Both non-ACM and age were also independent risk factors (aOR: 1.41, 95% CI: 1.31-1.51 and aOR: 1.08, 95% CI: 1.05-1.10, respectively).

Conclusion: To our knowledge, this is the first study that has examined population-level data in a nonlinear model framework to predict anticholinergic-type adverse events. This study evaluated the relationship between important patient characteristics and the occurrence of anticholinergic-type events. These findings reinforce the clinical significance of reviewing anticholinergic medicines in older people.