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This study developed an algorithm to reduce anticholinergic drug burden. The approach successfully reduced anticholinergic load in patients, improving memory and attention.

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Area of Science:

  • Geriatric Pharmacology
  • Clinical Pharmacy
  • Medication Safety

Background:

  • Adverse anticholinergic drug reactions are frequent but evidence for reducing exposure and measuring deprescribing is limited.
  • Anticholinergic activity contributes to various adverse drug reactions, impacting patient health and quality of life.

Purpose of the Study:

  • To propose and pilot-test an algorithm-based approach for evaluating and reducing anticholinergic drug load.
  • To assess the feasibility and effectiveness of a standardized deprescribing strategy for anticholinergic medications.

Main Methods:

  • Developed a list of 85 anticholinergic drugs and 21 algorithms for load reduction, recommending alternatives with lower risk.
  • Assembled a test battery including neuropsychological assessments, validated symptom measures (constipation, urinary, xerostomia), and blood biomarkers.
  • Pilot-tested the approach in a geriatric rehabilitation unit, with a pharmacist and clinical pharmacologist generating personalized recommendation letters.

Main Results:

  • Recommendations were generated for 22 drugs in nine patients; 78% of letters were deemed helpful.
  • Anticholinergic load was reduced in seven patients, with 36% of targeted drugs discontinued.
  • Patients with reduced anticholinergic load showed significant memory improvement after two weeks compared to those without drug changes.

Conclusions:

  • The algorithm-based approach was well-received by physicians in a pilot geriatric setting.
  • This method shows potential to support standardized and effective anticholinergic deprescribing.