Application of the STOPP/START criteria to a medical record database

Pharmacoepidemiol Drug Saf. 2017 Oct;26(10):1242-1247. doi: 10.1002/pds.4283. Epub 2017 Aug 11.

Abstract

Purpose: The STOPP/START criteria are increasingly used to assess prescribing quality in elderly patients at practice level. Our aim was to test computerized algorithms for applying these criteria to a medical record database.

Methods: STOPP/START criteria-based computerized algorithms were defined using Anatomical-Therapeutic-Chemical (ATC) codes for medication and International Classification of Primary Care (ICPC) codes for diagnoses. The algorithms were applied to a Dutch primary care database, including patients aged ≥65 years using ≥5 chronic drugs. We tested for associations with patient characteristics that have previously shown a relationship with the original STOPP/START criteria, using multivariate logistic regression models.

Results: Included were 1187 patients with a median age of 75 years. In total, 39 of the 62 STOPP and 18 of the 26 START criteria could be converted to a computerized algorithm. The main reasons for inapplicability were lack of information on the severity of a condition and insufficient covering of ICPC-codes. We confirmed a positive association between the occurrence of both the STOPP and the START criteria and the number of chronic drugs (adjusted OR ranging from 1.37, 95% CI 1.04-1.82 to 3.19, 95% CI 2.33-4.36) as well as the patient's age (adjusted OR for STOPP 1.30, 95% CI 1.01-1.67; for START 1.73, 95% CI 1.35-2.21), and also between female gender and the occurrence of STOPP criteria (adjusted OR 1.41, 95% CI 1.09-1.82).

Conclusion: Sixty-five percent of the STOPP/START criteria could be applied with computerized algorithms to a medical record database with ATC-coded medication and ICPC-coded diagnoses.

Keywords: aged; algorithms; inappropriate prescribing; pharmacoepidemiology; polypharmacy; potentially inappropriate medication list.

MeSH terms

  • Aged
  • Aged, 80 and over
  • Algorithms
  • Databases, Factual / standards*
  • Databases, Factual / statistics & numerical data
  • Drug Prescriptions / standards*
  • Drug Prescriptions / statistics & numerical data
  • Electronic Health Records / standards*
  • Electronic Health Records / statistics & numerical data
  • Feasibility Studies
  • Female
  • Humans
  • Inappropriate Prescribing / prevention & control
  • Logistic Models
  • Male
  • Netherlands
  • Potentially Inappropriate Medication List / standards*
  • Potentially Inappropriate Medication List / statistics & numerical data
  • Primary Health Care / methods*
  • Primary Health Care / standards
  • Primary Health Care / statistics & numerical data