Identifying high-risk medication: a systematic literature review

Eur J Clin Pharmacol. 2014 Jun;70(6):637-45. doi: 10.1007/s00228-014-1668-z. Epub 2014 Mar 27.

Abstract

Purpose: A medication error (ME) is an error that causes damage or poses a threat of harm to a patient. Several studies have shown that only a minority of MEs actually causes harm, and this might explain why medication reviews at hospital admission reduce the number of MEs without showing an effect on length of hospital stay, readmissions, or death. The purpose of this study was to define drugs that actually cause serious MEs. We conducted a literature search of medication reviews and other preventive efforts.

Methods: A systematic search in PubMed, Embase, Cochrane Reviews, Psycinfo, and SweMed+ was performed. Danish databases containing published patient complaints, patient compensation, and reported medication errors were also searched. Articles and case reports were included if they contained information of an ME causing a serious adverse reaction (AR) in a patient. Information concerning AR seriousness, causality, and preventability was required for inclusion.

Results: This systematic literature review revealed that 47 % of all serious MEs were caused by seven drugs or drug classes: methotrexate, warfarin, nonsteroidal anti-inflammatory drugs (NSAIDS), digoxin, opioids, acetylic salicylic acid, and beta-blockers; 30 drugs or drug classes caused 82 % of all serious MEs. The top ten drugs involved in fatal events accounted for 73 % of all drugs identified.

Conclusion: Increasing focus on seven drugs/drug classes can potentially reduce hospitalizations, extended hospitalizations, disability, life-threatening conditions, and death by almost 50 %.

Publication types

  • Review
  • Systematic Review

MeSH terms

  • Adverse Drug Reaction Reporting Systems / statistics & numerical data
  • Drug-Related Side Effects and Adverse Reactions* / epidemiology
  • Drug-Related Side Effects and Adverse Reactions* / etiology
  • Hospitalization / statistics & numerical data
  • Humans
  • Medication Errors / classification
  • Medication Errors / statistics & numerical data*
  • Pharmaceutical Preparations* / classification

Substances

  • Pharmaceutical Preparations