Applying Methods for Signal Detection in Spontaneous Reports to Electronic Patient Records

Currently, pharmacovigilance relies mainly on disproportionality analysis of spontaneous reports. However, the analysis of spontaneous reports is concerned with several problems, such as reliability, under-reporting and insufficient patient information. Longitudinal healthcare data, such as Electronic Patient Records (EPRs) in which comprehensive information of each patient is covered, is a complementary source of information to detect Adverse Drug Events (ADEs). A wide set of disproportionality methods has been developed for analyzing spontaneous reports to assess the risk of reported events being ADEs. This study aims to investigate the use of such methods for detecting ADEs when analyzing EPRs. The data used in this study was extracted from Stockholm EPR Corpus. Four disproportionality methods (proportional reporting rate, reporting odds ratio, Bayesian confidence propagation neural network, and Gamma-Poisson shrinker) were applied in two different ways to analyze EPRs: creating pseudo spontaneous reports based on all observed drug-event pairs (event-level analysis) or analyzing distinct patients who experienced a drug-event pair (patient-level analysis). The methods were evaluated in a case study on safety surveillance of Celecoxib. The results showed that, among the top 200 signals, more ADEs were detected by the event-level analysis than by the patient-level analysis. Moreover, the event-level analysis also resulted in a higher mean average precision. The main conclusion of this study is that the way in which the disproportionality analysis is applied, the event-level or patient-level analysis, ∗Corresponding author. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. KDD-DMH’13, August 11, 2013, Chicago, Illinois, USA Copyright 2013 ACM 978-1-4503-2174-7/13/08 ...$15.00. can have a much higher impact on the performance than which disproportionality method is employed.