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Potential Adverse Drug Events Identified with Decision Support Algorithms from Janusmed Risk Profile-A Retrospective

Tora Hammar1,2, Emma Jonsén1, Olof Björneld1,2,3

  • 1The eHealth Institute, Department of Medicine and Optometry, Linnaeus University, S-391 82 Kalmar, Sweden.

Pharmacy (Basel, Switzerland)
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Summary

Clinical decision support systems (CDSSs) like Janusmed Risk Profile help identify patients at risk for adverse drug events (ADEs). Over 20% of patients faced risks from medication combinations, highlighting common ADE vulnerabilities.

Keywords:
adverse drug eventsclinical decision support systemdrug-related problemsdrug–drug interactionspharmacoepidemiologyside effects

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Area of Science:

  • Pharmacovigilance and Drug Safety
  • Health Informatics
  • Clinical Pharmacology

Background:

  • Adverse drug events (ADEs) are frequent, leading to significant morbidity and mortality.
  • Harmful drug combinations are a major cause of preventable ADEs.
  • Clinical decision support systems (CDSSs) offer a strategy for ADE prevention.

Purpose of the Study:

  • To determine the prevalence of potential ADEs identified by the Janusmed Risk Profile CDSS.
  • To analyze ADE risk prevalence across nine common or serious ADE categories.
  • To investigate demographic factors associated with increased ADE risk.

Main Methods:

  • Retrospective, cross-sectional study of a Swedish region's population (n=246,010 in 2020).
  • Utilized data on all dispensed and administered medications.
  • Employed Janusmed Risk Profile CDSS algorithms and cluster analysis.

Main Results:

  • Over 20% of patients exhibited an increased risk for bleeding, constipation, orthostatism, or renal toxicity.
  • The proportion of patients with increased risk ranged from 3.5% to nearly 30% across ADE categories.
  • Higher age and gender were associated with increased risk; cluster analysis revealed patient groups with multiple ADE risks.

Conclusions:

  • Medication combinations posing a risk for ADEs are prevalent in the studied population.
  • Age and gender influence the risk of potential ADEs.
  • Further research is needed to correlate identified risks with actual observed ADEs.