Comparing Drug-Drug Interaction Severity Ratings between Bedside Clinicians and Proprietary Databases

Purpose. The purpose of this project was to compare DDI severity for clinician opinion in the context of the patient’s clinical status to the severity of proprietary databases. Methods. This was a single-center, prospective evaluation of DDIs at a large, tertiary care academic medical center in a 10-bed cardiac intensive care unit (CCU). A pharmacist identified DDIs using two proprietary databases. The physicians and pharmacists caring for the patients evaluated the DDIs for severity while incorporating their clinical knowledge of the patient. Results. A total of 61 patients were included in the evaluation and experienced 769 DDIs. The most common DDIs included: aspirin/clopidogrel, aspirin/insulin, and aspirin/furosemide. Pharmacists ranked the DDIs identically 73.8% of the time, compared to the physicians who agreed 42.2% of the time. Pharmacists agreed with the more severe proprietary database scores for 14.8% of DDIs versus physicians at 7.3%. Overall, clinicians agreed with the proprietary database 20.6% of the time while clinicians ranked the DDIs lower than the database 77.3% of the time. Conclusions. Proprietary DDI databases generally label DDIs with a higher severity rating than bedside clinicians. Developing a DDI knowledgebase for CDSS requires consideration of the severity information source and should include the clinician. 1. Introduction Adverse drug events (ADEs) may occur due to medication errors (MEs), pharmacokinetic alterations, drug-drug interactions (DDIs) and drug-disease interactions, with research revealing that both the incidence and severity of ADEs are heightened in intensive care unit (ICU) patients [1, 2]. An ADE is defined as an undesirable clinical manifestation that is consequent to and caused by the administration of medications, as well as events due to error [3]. Drug-drug interactions contribute to ADEs when the efficacy or toxicity of a medication is altered by the administration of another substance and causes a reduction in the intended therapeutic effect or increase in the expected toxicity profile [4]. Automated clinical decision support systems (CDSSs) within most computerized prescriber order entry (CPOE) programs have contributed to error reduction by prospectively identifying potential medication allergies, interactions, or overdoses and may reduce the incidence of DDIs by 50% [5, 6]. Notably, only 1 out of 15 interactions in a cardiac ICU is considered major or contraindicated by proprietary DDI databases and excessive DDI alerting may cause “alert fatigue” [7]. Alert fatigue is defined as a