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Related Experiment Videos

Can an algorithm for appropriate prescribing predict adverse drug events?

Kimberly J Rask1, Kristen J Wells, Gregg S Teitel

  • 1Department of Health Policy and Management, Rollins School of Public Health, Emory University School of Medicine, Atlanta, GA 30322, USA. krask@sph.emory.edu

The American Journal of Managed Care
|March 25, 2005
PubMed
Summary

A medication-appropriateness algorithm did not effectively identify older patients at risk for adverse drug events (ADEs). Most ADEs stemmed from commonly prescribed medications, not those flagged by the algorithm.

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Area of Science:

  • Geriatric Medicine
  • Pharmacovigilance
  • Health Informatics

Background:

  • Pharmacy claims data are increasingly used to monitor medication safety.
  • Identifying patients at risk for adverse drug events (ADEs) is crucial for improving geriatric care.
  • Existing algorithms may not accurately predict ADEs in community-dwelling older adults.

Purpose of the Study:

  • To determine if a medication-appropriateness algorithm using pharmacy claims data can identify ambulatory patients at risk for ADEs.
  • To assess the prevalence of self-reported ADEs in older Medicare managed care enrollees.
  • To identify the medications most frequently associated with ADEs in this population.

Main Methods:

  • A cohort study surveyed 211 community-dwelling Medicare managed care enrollees over 65 identified by pharmacy claims as taking potentially contraindicated medications (exposed).

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  • A control group of 195 enrollees not taking such medications (unexposed) was also surveyed.
  • The primary outcome was self-reported ADE prevalence over the previous six months.

    • Main Results:

    • Out of 406 respondents, 99 (24.4%) reported 134 ADEs in the prior six months.
    • Exposed enrollees had more chronic conditions and took more medications, but the difference in ADE prevalence was not statistically significant (OR=1.42; 95% CI=0.90-2.25).
    • Only 1.5% of ADEs were linked to potentially contraindicated medications; most ADEs involved common agents like cardiovascular, anti-inflammatory, and cholesterol-lowering drugs.

    Conclusions:

    • The medication-appropriateness algorithm failed to identify older adults at higher risk for ADEs from potentially contraindicated medications.
    • The majority of ADEs in this population were associated with commonly prescribed medications, highlighting a gap in current pharmacovigilance strategies.
    • Pharmacy claims data algorithms need refinement to better capture real-world ADEs in older populations.