Electronic Healthcare Record (EHR) Databases for Drug Safety Signal Detection: What Can We Expect?

Background: There is growing interest in the use of electronic healthcare records (EHR) databases for drug safety signal detection, but an important concern is whether these databases have the data and adequate power to monitor all types of drugs and all types of adverse events. Objectives: To put into perspective expectations about use of EHR data-based systems as new approach to signal detection and active drug safety surveillance. Methods: Within the setting of EU-ADR network comprising eight European EHR databases (administrative claims and general practice medical records) and in the context of a cohort study, we determined the amount of drug exposure required to enable detection of safety signals of varying magnitudes of risk. We provide estimates of the number and types of drugs (WHO ATC Classification, 5th level) that can be monitored as a function of actual use, minimal detectable relative risk (RR), and empirically-derived Background incidence rates of the following adverse events deemed to be important in pharmacovigilance: (1) acute myocardial infarction; (2) acute renal failure; (3) anaphylactic shock; (4) bullous eruptions; (5) rhabdomyolysis; and (6) upper gastrointestinal bleeding.[1] We further perform data simulation in an attempt to see if, and how, expansion of database size would make a difference in the capabilities of the system. Results: Data from 19 647 452 individuals (59 594 132 person-years follow-up) who used 2289 drugs show that for a frequent event such as acute myocardial infarction there are 531 drugs (23% of total) for which an association with RR= 2, if present, can be investigated and 939 drugs (41%) for which an association with RR= 4 can be investigated. For a rare event such as rhabdomyolysis there are 19 drugs (1%) for which an association with RR= 2, if present, can be investigated and 170 drugs (7%) for which an association with RR= 4 can be investigated. With expansion of the database network to 10 times its current size (»200 million subjects) the percentage of drugs that can be monitored for acute myocardial infarction may increase to 44% (at RR= 2) and for rhabdomyolysis to 23% (at RR= 4). Conclusions: Drug safety signal detection using linked healthcare data-based networks for signal detection is feasible, although the leverage to do so may be low for drugs that are infrequently used and for very rare events. Extending the database network size to include data from more heterogeneous populations and increasing followup time are warranted to maximize the leverage of these surveillance systems.