title: Can an algorithm for appropriate prescribing predict adverse drug events?
abstract: OBJECTIVE
  To evaluate whether a medication-appropriateness algorithm applied to pharmacy claims data can identify ambulatory patients at risk for experiencing adverse drug events (ADEs) from those medications.
  
  
  METHODS
  Cohort study.
  
  
  METHODS
  We surveyed a random sample of 211 community-dwelling Medicare managed care enrollees over age 65 years who were identified by pharmacy claims as taking a potentially contraindicated medication (exposed enrollees) and a random sample of 195 enrollees who were identified as not taking such a medication (unexposed enrollees). The primary outcome of interest was the prevalence of self-reported events in previous 6 months.
  
  
  RESULTS
  Ninety-nine (24.4% of total sample) respondents reported a total of 134 ADEs during the previous 6 months. Exposed enrollees had a significantly higher number of chronic conditions and were taking more prescription and nonprescription medications. However, the higher rate of self-reported ADEs among exposed enrollees was not statistically significant from that of unexposed enrollees (prevalence odds ratio = 1.42; 95% confidence interval [CI] = 0.90, 2.25). Only 1.5% (2/134) of the self-reported ADEs were attributed to a medication from the potentially contraindicated list. Instead, most ADEs were attributed to medications that are commonly used in older patients, including cardiovascular agents (21.6%), anti-inflammatory agents (12.2%), and cholesterol-lowering agents (7.9%).
  
  
  CONCLUSIONS
  A medication-appropriateness algorithm using pharmacy claims data was not able to identify a subgroup of enrollees at higher risk of experiencing an ADE from those medications. The vast majority of ADEs were attributable to commonly prescribed medications.
authors.affiliation: Department of Health Policy and Management, Rollins School of Public Health, Emory University School of Medicine, Atlanta, GA 30322, USA. krask@sph.emory.edu
authors.forename: Kimberly J
authors.initials: KJ
authors.lastname: Rask
authors.forename: Kristen J
authors.initials: KJ
authors.lastname: Wells
authors.forename: Gregg S
authors.initials: GS
authors.lastname: Teitel
authors.forename: Jonathan N
authors.initials: JN
authors.lastname: Hawley
authors.forename: Calita
authors.initials: C
authors.lastname: Richards
authors.forename: Julie A
authors.initials: JA
authors.lastname: Gazmararian
mesh_terms: D000368:Aged; D000465:Algorithms; D015331:Cohort Studies; D064420:Drug-Related Side Effects and Adverse Reactions; D005260:Female; D006801:Humans; D007345:Insurance Claim Review; D008297:Male; D006278:Medicare; D010414:Pennsylvania; D018570:Risk Assessment