Drug-disease interaction in elderly patients in family practice

Int J Clin Pharmacol Ther. 2014 May;52(5):337-45. doi: 10.5414/CP202003.

Abstract

Objective: To determine the frequency of potential drug-disease interaction in elderly patients in family practice. To assess which drugs and diagnoses are associated with a high risk related to drug-disease interaction and whether there are gender- or age-related differences.

Methods: In routinely recorded electronic patient records, patients at least 65 years old with at least one diagnosis named in Beers list and one prescription were identified. Potential drug-disease interaction (PDDI) was presumed if within the same 3 months a "Beers" diagnosis and a potentially inappropriate prescription with respect to this diagnosis were documented for a patient. Multiple logistic regression analysis identified factors associated with a high risk of PDDI.

Results: Of 24,619 patients (63.4% women) corresponding to our inclusion criteria, 10.4% were exposed to at least one PDDI. Almost no (0.0%) PDDI was associated with the most common Beers disorder hypertension (prevalence 49.2%). However, 23.4% of men suffering from bladder outflow obstruction (prevalence 17.6% in males) were exposed to at least one PDDI. PDDI was quite common in some rarer conditions, for example, indications for anticoagulation (prevalence 2.6%, 31.5% PDDI). PDDI was not influenced by gender, but associated with taking more than 4 drugs (OR 1.91 (1.83 - 2.00)), suffering from more than one Beers disorder (OR 1.24 (1.16 - 1.31)), and advanced age (OR 1.10 (1.05 - 1.15)).

Conclusions: High risk patient groups could be identified. Some disorders as well as some drugs are particularly prone to risky constellations; these should be reflected in systems assisting prescribing with regard to patient safety.

MeSH terms

  • Age Factors
  • Aged
  • Aged, 80 and over
  • Chi-Square Distribution
  • Comorbidity
  • Drug Interactions*
  • Drug Prescriptions
  • Family Practice*
  • Female
  • Germany
  • Humans
  • Inappropriate Prescribing*
  • Logistic Models
  • Male
  • Multivariate Analysis
  • Odds Ratio
  • Patient Safety
  • Polypharmacy
  • Risk Factors
  • Sex Factors