Development of a Screening Strategy for Community-based Adverse Drug Related Events in the Emergency Department

Adverse Drug Related Events

Adverse drug event, harm, patient safety, clinical decision support, Adverse drug related event

Adverse Drug Related Events (ADREs) are a leading cause of Emergency Department (ED) visits in Canada. However emergency physicians recognize only half of all ADREs in patients presenting to the ED, missing opportunities to intervene. The objective of this study is to develop a screening strategy that identifies patients with ADREs. Our hypothesis is that the development of a user-friendly, reliable screening strategy for ADREs in patients presenting to the ED is feasible. We believe that this will lead to improved patient care.

Background Between 9250 and 23,750 Canadians die each year because of preventable adverse events related to medical care. ADREs account for 50% of all preventable adverse events and cause up to 12% of ED visits. Unfortunately, emergency physicians recognize only half of all ADREs, missing crucial opportunities to intervene. Objective The objective of this study is to derive a clinical decision rule that accurately stratifies patients presenting to the ED into risk categories for drug-related morbidity. This instrument will allow early identification of clinically significant ADREs allowing rational referral for medication optimization, treatment of ADREs and prevention of future events. Hypothesis The development of a rapid, user-friendly reliable clinical decision rule for ADREs in patients presenting to the ED is feasible. Methodology This prospective observational study will evaluate predictor variables (from the history, physical examination and laboratory tests) for ADREs in a representative sample of ED patients using standardized assessments by emergency nurses and physicians. Inter-rater agreement of predictor variables will be measured, and clinical pharmacists, blinded to the nursing and physician assessments, will use a standardized, validated algorithm to identify ADREs. Recursive partitioning and/or logistic regression analysis will be used to determine the optimal combination of predictor variables to detect the presence of an ADRE. We will report diagnostic test characteristics of the derived clinical decision rule. Validation of the clinical decision rule is planned in a future study.

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