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PURPOSE: To provide estimates of the number and types of drugs that can be monitored for safety surveillance using electronic healthcare databases. METHODS: Using data from eight European databases (administrative claims, medical records) and in the context of a cohort study, we determined the amount of drug exposure required for signal detection across varying magnitudes of relative risk (RR). We provide estimates of the number and types of drugs that can be monitored as a function of actual use, minimal detectable RR, and empirically derived incidence rates for the following adverse events: (i) acute myocardial infarction; (ii) acute renal failure; (iii) anaphylactic shock; (iv) bullous eruptions; (v) rhabdomyolysis; and (vi) upper gastrointestinal bleeding. We performed data simulation to see how expansion of database size would influence the capabilities of such system. RESULTS: Data from 1,947,452 individuals (59,594,132 person-years follow-up) who used 2,289 drugs in the EU-ADR network 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. For a rare event such as rhabdomyolysis, there are 19 drugs (1%) for which an association of same magnitude can be investigated. CONCLUSION: Active surveillance using healthcare data-based networks for signal detection is feasible, although the leverage to do so may be low for infrequently used drugs and for rare outcomes. Extending database network size to include data from heterogeneous populations and increasing follow-up time are warranted to maximize leverage of these surveillance systems.

The EU-ADR project aims to exploit different European electronic healthcare records (EHR) databases for drug safety signal detection. In this paper we report the preliminary results concerning the comparison of signal detection between EU-ADR network and two spontaneous reporting databases, the Food and Drug Administration and World Health Organization databases. EU-ADR data sources consist of eight databases in four countries (Denmark, Italy, Netherlands, and United Kingdom) that are virtually linked through distributed data network. A custom-built software (Jerboa©) elaborates harmonized input data that are produced locally and generates aggregated data which are then stored in a central repository. Those data are subsequently analyzed through different statistics (i.e. Longitudinal Gamma Poisson Shrinker). As potential signals, all the drugs that are associated to six events of interest (bullous eruptions - BE, acute renal failure - ARF, acute myocardial infarction - AMI, anaphylactic shock - AS, rhabdomyolysis - RHABD, and upper gastrointestinal bleeding - UGIB) have been detected via different data mining techniques in the two systems. Subsequently a comparison concerning the number of drugs that could be investigated and the potential signals detected for each event in the spontaneous reporting systems (SRSs) and EU-ADR network was made. SRSs could explore, as potential signals, a larger number of drugs for the six events, in comparison to EU-ADR (range: 630-3,393 vs. 87-856), particularly for those events commonly thought to be potentially drug-induced (i.e. BE: 3,393 vs. 228). The highest proportion of signals detected in SRSs was found for BE, ARF and AS, while for ARF, and UGIB in EU-ADR. In conclusion, it seems that EU-ADR longitudinal database network may complement traditional spontaneous reporting system for signal detection, especially for those adverse events that are frequent in general population and are not commonly thought to be drug-induced. The methodology for signal detection in EU-ADR is still under development and testing phase.

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.

INTRODUCTION Detection of clinical events from diverse electronic sources of information such as hospital discharge claims (HOSP), death registries (DEATH), laboratory values (LAB), and general practice databases (GP) may be useful for various epidemiological purposes. In particular, the EU-ADR project aims to detect adverse events deemed to be important in pharmacovigilance. Eight databases (DBs) from four countries, combining different sources of information, participate in the project. A common conceptual framework was lacking to describe harmoniously the algorithm by which each DB detected each event. OBJECTIVES Describe the algorithms that 8 different DBs used to identify 6 events: Acute myocardial infarction (AMI), Acute Renal Failure (ARF), Anaphylactic Shock (AS), Bullous Eruption (BE), Rhabdomyolysis (RHABD), Upper Gastrointestinal Bleeding (UGIB). Benchmark corresponding incidence rates (IRs). METHODS A list of medical concepts corresponding to each event of interest was provided and projected to the DSs different coding systems (ICD9, ICD10, READ, ICPC) and natural languages through the Unified Medical Language System (UMLS). Specific sources of information contained in each DB were classified in a common framework and DBs with similar structures were asked to search for concepts within the same sources. RESULTS Concepts were mainly searched for in GP diagnoses and primary diagnoses of HOSP, but for some events DEATH (AMI, ARF, AS) and LAB (RHABD) were independently used by DBs having them. Resulting age-adjusted IRs vary as follows across DBs: 1-2/1,000PY (AMI), 3-7/10,000PY (UGIB), 2-12/100,000PY (AS), 2-17/100,000PY (BE), 1-8/100,000PY (RHABD), 3-49/100,000PY (ARF). CONCLUSIONS It is possible to describe event extractions from heterogeneous DBs in a common conceptual framework. Residual differences in IRs may be due either to differences in the underlying populations or to differences in the characteristics (structure, coding system) of the DBs.

Background: There is huge potential for mining electronic healthcare records (EHR) databases to augment current systems in pharmacovigilance.[1-3] Like any signal detection system, there is a need to establish ‘rules’ how to trigger an alert, when to consider a signal likely enough to be true to warrant follow-up or even to require immediate health policy intervention.[4,5] Objectives: To describe the process of prioritisation of drug-adverse event associations derived from signal detection using EHR databases in the EU-ADR Project. Methods: Association measures between drug use and acute myocardial infarction (AMI) were generated by first applying various statistical methods on healthcare data from seven databases of the EUADR network.[6] Association estimates were ranked based on the best performing method (Longitudinal Gamma Poisson Shrinker). Matched case-control and self-controlled case series methods were additionallyconducted to deal with temporality and confounding effect, while the LEOPARD method was applied to specifically detect protopathic bias. Consistency of the association among drugs of the same class and the number of excess cases attributable to the drug exposure were further assessed to prioritize the list of potential signals. Finally, signal filtering and signal substantiation were done using different bioinformatics workflows to determine the novelty and plausibility of the identified signals. Results: Demographic, clinical and prescription/dispensing data in three European Countries were obtained from 21 171 291 individuals with 154 474 063 person-years of follow-up within the period 1995–2011. Overall, 163 potential signals forAMI were identified based on statistical association. Of these, 72 signals were flagged by LEOPARDas likely due to protopathic bias. Further signal refinement to reduce possible confounding decreased the number of signals to 39. Nine signals remained after applying the criteria for novelty and plausibility. Conclusion: We propose a prioritisation strategy for drug safety signal detection using EHR by taking into account, in addition to statistical association, also public health relevance, novelty, and plausibility. This strategy needs to be further tested using other EHR data sources and other adverse events.

Background: Drug-induced acute liver injury (ALI) is one of the leading causes of drug withdrawal from the market. There is, however, lack of data on ALI in the pediatric population. Combining multi-country electronic healthcare databases offers the opportunity to explore the risk of ALI in children and adolescents and facilitate detection of potential drug safety signals. Objectives: To identify drugs potentially associated with ALI in the pediatric population using the EU-ADR database network and to estimate the power of such a network for signal detection concerning ALI as a function of actual drug use, minimal detectable relative risk (RR) and Background incidence rate (IR) of ALI in children and adolescents. Methods: We extracted data on all potential cases of ALI and data concerning prescribed/dispensed drugs among individuals 0–15 years registered within eight European, population-based, electronicmedical record and claims databases of the EU-ADR network during the period 1995–2010. We estimated the Background IR of ALI in the pediatric population and assessed drug use according to number of person-years (PYs) of exposure by Anatomical Therapeutic and Chemical (ATC) classification, 5th level. Based on IR of ALI derived within EU-ADR, power = 80% and alpha = 5%, we estimated how much drug exposure would be necessary to allow for detection of a signal concerning ALI. Finally, among drugs with enough exposure, all potential signals were identified by measuring age- and sex-adjusted relative risks of ALI, with all other drugs as comparator. Results: Children 0–15 years of age contributed 20 088 726 PYs of follow-up to the EU-ADR database network. The incidence rate for ALI was estimated to be 4.3 per 100 000 PYs in this pediatric population. The total amount of drug exposure that would be required to detect a ‘weak’ association (RR= 2) with ALI, if present, was 186 490 PYs and 28 554 PYs to detect a ‘moderate’ association (RR = 4). The following drugs were identified to be potentially associated with ALI: amoxicillin (RR 4.3, 95% CI 2.6, 7.0); clarithromycin (3.7, 1.8, 7.5); cetirizine (3.1, 1.5, 6.5); flunisolide (2.5, 1.0, 6.8); budesonide (2.5, 1.1, 5.5); and amoxicillin plus clavulanic acid (2.3, 1.3, 4.1). Conclusions: Combining multiple electronic healthcare databases may facilitate identification of potentially drug-related cases of ALI and increase the power for drug safety signal detection in the pediatric population. Except for the anti-asthmatic medications, the signals detected are already known in adults.