ArticleEuropean journal of hospital pharmacy : science and practice2023
Analysis of medication-related triggers to determine adverse drug events.
Article in European journal of hospital pharmacy : science and practice, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
5 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeVoluntary event reporting systems continue to be the most common method used to identify adverse events in most US hospitals; however, this method fails to capture more than 90% of adverse drug events (ADEs). The purpose of this study is to examine which medication-related triggers have the highest positive predictive values (PPV) for detecting ADEs at a large academic medical centre.
methodsA 1-year, single-centre, retrospective quality improvement study was conducted to assess the PPV of four medication-related triggers: flumazenil, naloxone, glucose <70 mg/dL or dextrose 50%. Retrospective chart review was conducted on a random sample of eligible patients to establish if an ADE occurred and determine its preventability. Assessed triggers were also compared against the hospital's voluntary event reporting system to determine whether the events were previously reported.
resultsA total of 161 triggers were reviewed. PPV values for detection of ADEs were 0.55, 0.58, 0.76 and 0.68 for flumazenil, naloxone, glucose <70 mg/dL and dextrose 50%, respectively. PPV values for detection of preventable ADEs were 0.09, 0.16, 0.32 and 0.34 for flumazenil, naloxone, glucose <70 mg/dL and dextrose 50%, respectively. Of the 107 ADEs identified, three events were reported through the hospital's voluntary event reporting system (2.8%).
conclusionsTrigger tools successfully detected both preventable and non-preventable ADEs. Events detected using trigger tools are unlikely to be reported through voluntary event reporting systems; therefore, trigger tools can serve as a useful adjunct for adverse event detection.
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