Useful Interplay Between Spontaneous ADR Reports and Electronic Healthcare Records in Signal Detection

Abstract

Background and objective: Spontaneous reporting systems (SRSs) remain the cornerstone of post-marketing drug safety surveillance despite their well-known limitations. Judicious use of other available data sources is essential to enable better detection, strengthening and validation of signals. In this study, we investigated the potential of electronic healthcare records (EHRs) to be used alongside an SRS as an independent system, with the aim of improving signal detection.

Methods: A signal detection strategy, focused on a limited set of adverse events deemed important in pharmacovigilance, was performed retrospectively in two data sources-(1) the Exploring and Understanding Adverse Drug Reactions (EU-ADR) database network and (2) the EudraVigilance database-using data between 2000 and 2010. Five events were considered for analysis: (1) acute myocardial infarction (AMI); (2) bullous eruption; (3) hip fracture; (4) acute pancreatitis; and (5) upper gastrointestinal bleeding (UGIB). Potential signals identified in each system were verified using the current published literature. The complementarity of the two systems to detect signals was expressed as the percentage of the unilaterally identified signals out of the total number of confirmed signals. As a proxy for the associated costs, the number of signals that needed to be reviewed to detect one true signal (number needed to detect [NND]) was calculated. The relationship between the background frequency of the events and the capability of each system to detect signals was also investigated.

Results: The contribution of each system to signal detection appeared to be correlated with the background incidence of the events, being directly proportional to the incidence in EU-ADR and inversely proportional in EudraVigilance. EudraVigilance was particularly valuable in identifying bullous eruption and acute pancreatitis (71 and 42 % of signals were correctly identified from the total pool of known associations, respectively), while EU-ADR was most useful in identifying hip fractures (60 %). Both systems contributed reasonably well to identification of signals related to UGIB (45 % in EudraVigilance, 40 % in EU-ADR) but only fairly for signals related to AMI (25 % in EU-ADR, 20 % in EudraVigilance). The costs associated with detection of signals were variable across events; however, it was often more costly to detect safety signals in EU-ADR than in EudraVigilance (median NNDs: 7 versus 5).

Conclusion: An EHR-based system may have additional value for signal detection, alongside already established systems, especially in the presence of adverse events with a high background incidence. While the SRS appeared to be more cost effective overall, for some events the costs associated with signal detection in the EHR might be justifiable.

Fig. 1

Workflow of verification of signals. *A 5 % random sample was manually checked to see if the classification as non–adverse drug reactions (non-ADRs) was accurate

Fig. 2

Distribution of potential signals in the EudraVigilance and (EU-ADR) databases, grouped according to the anatomical therapeutic chemical (ATC) classification therapeutic subgroup (note: only classes with >30 potential signals are shown)

Fig. 3

Contribution of each system to signal detection (i.e. percentage of ‘positive’ associations detected out of the total adverse drug reaction [ADR] pool in the literature). ‘N’ indicates the total number of true associations in the dataset, and ‘found in neither’ indicates that the association was not highlighted as a signal in any of the databases during the signal detection process. AMI acute myocardial infarction, BE bullous eruption, EU-ADR exploring and understanding adverse drug reactions, FRACT fracture, PANC pancreatitis, UGIB upper gastrointestinal bleeding

Fig. 4

Correlation between the background incidence of events and the contribution of each system to signal detection. The background incidences of the events (per 100,000 person-years), estimated from exploring and understanding adverse drug reactions (EU-ADR) data, pooled across all databases, were bullous eruption = 4.2, pancreatitis = 21.4, upper gastrointestinal bleeding = 82.2, hip fracture = 117.7 and acute myocardial infarction = 153.7. ‘Identified signals’ refer to signals proven to be known adverse drug reactions

Fig. 5

Numbers needed to detect a true association. Note: this represents the ‘cost’ per signal, e.g. in the case of detection of acute myocardial infarction (AMI) in EudraVigilance, 5 signals needed to be investigated in order to detect 1 true association. BE bullous eruption, EU-ADR exploring and understanding adverse drug reactions, FRACT fracture, PANC pancreatitis, UGIB upper gastrointestinal bleeding