Agreement between drugs-to-avoid criteria and expert assessments of problematic prescribing

Abstract

Background: Drugs-to-avoid criteria are commonly used to evaluate prescribing quality in elderly persons. However, few studies have evaluated the concordance between these criteria and individualized patient assessments as measures of problem prescribing.

Methods: We used data on 256 outpatients from the Iowa City VA Medical Center who were 65 years or older and taking 5 or more medications. After a comprehensive patient interview, a study team composed of a physician and a pharmacist recommended that certain drugs be discontinued, substituted, or reduced in dose. We evaluated the degree to which drugs considered potentially inappropriate by the drugs-to-avoid criteria of Beers et al and Zhan et al (hereinafter, Beers criteria and Zhan criteria) were also considered problematic by the study team, and vice versa.

Results: In the study cohort, 256 patients were using 3678 medications. The physician-pharmacist team identified 563 drugs (15%) as problematic, while 214 drugs (6%) were flagged as potentially inappropriate by the Beers criteria and 91 drugs (2.5%) were flagged as potentially inappropriate using the Zhan criteria. The kappa statistics for concordance between drugs-to-avoid criteria and expert assessments were 0.10 to 0.14, indicating slight agreement between these measures. Sixty-one percent of drugs identified as potentially inappropriate by the Beers criteria and 49% of drugs flagged by the Zhan criteria were not judged to be problematic by the expert reviewers. Correspondence between drugs-to-avoid criteria and expert assessment varied widely across different types of drugs.

Conclusions: Drugs-to-avoid criteria have limited power to differentiate between drugs and patients with and without prescribing problems identified on individualized expert review. Although these criteria are useful as guides for initial prescribing decisions, they are insufficiently accurate to use as stand-alone measures of prescribing quality.

Figure 1

Concordance of drugs-to-avoid criteria with individualized expert review: drug level data

Figure 2

Concordance of drugs-to-avoid criteria with individualized expert review: patient level data