Development and Pilot Testing of an Algorithm-Based Approach to Anticholinergic Deprescribing in Older Patients

Adverse anticholinergic drug reactions are common, yet evidence on how to reduce exposure to anticholinergic activity and reliably measure successful deprescribing is still scant. This study proposes an algorithm-based approach to evaluate and reduce anticholinergic load, and reports the results of its pilot testing.

Based on published evidence and expert opinion, a list of 85 anticholinergic drugs and 21 algorithms for reducing anticholinergic load, e.g., by recommending alternative drugs with lower risk, were developed. An accompanying test battery was assembled by focusing on instruments that sensitively reflect anticholinergic load and may be sensitive to depict changes (Neuropsychological Assessment Battery to measure memory and attention, validated assessments for constipation, urinary symptoms, and xerostomia, as well as blood biomarkers). The approach was pilot-tested in a geriatric rehabilitation unit, with clinician feedback as the primary outcome and characterization of anticholinergic symptoms as the secondary outcome. The intervention was delivered by a pharmacist and a clinical pharmacologist who used the algorithms to generate personalized recommendation letters.

We included a total of 20 patients, 13 with anticholinergic drugs and 7 without. Recommendations were made for 22 drugs in nine patients from the intervention group, of which seven letters (78%) were considered helpful and 8/22 (36%) anticholinergic drugs were discontinued, reducing anticholinergic load in seven patients. In contrast to patients without drug change, memory assessment in patients with reduced anticholinergic load improved significantly after 2 weeks (6 ± 3 vs. -1 ± 6 points).

The approach was well received by the participating physicians and might support standardized anticholinergic deprescribing.