Detecting Adverse Drug Reactions Using Inpatient Medication Orders and Laboratory Tests Data

Introduction: Medication safety requires monitoring throughout a drug\´s market life. Early detection of adverse drug reactions (ADRs) can lead to alerts that prevent patient harm. Recently, electronic medical records (EMRs) have emerged as a valuable resource for pharmacovigilance. This study examines the use of retrospective medication orders and inpatient laboratory results in the EMR to identify ADRs. Methods: Using 12 years of EMR data, we designed a study to correlate abnormal laboratory results with specific drug orders by comparing outcomes of a drug-exposed group and a matched unexposed group. We assessed the relative merits of six pharmacovigilance methods used in spontaneous reporting systems (SRS), including proportional reporting ratio (PRR), reporting odds ratio (ROR), Yule\´s Q, the Chi-square test, Bayesian confidence propagation neural networks (BCPNN) and a gamma Poisson shrinker (GPS). The time of admission was set as "day zero" and all drug orders and laboratory results timings were represented as days elapsed since that time until discharge. Each patient in the exposed group was randomly matched to four unexposed patients by age group, gender, race, and major diagnoses based on ICD9 codes.