Identification of Pregnancy Adverse Drug Reactions in Pharmacovigilance Reporting Systems: A Novel Algorithm Developed in EudraVigilance.

INTRODUCTION: There is a need to strengthen the evidence base regarding medication use during pregnancy and to facilitate the early detection of safety signals. EudraVigilance (EV) serves as the primary system for managing and analysing information concerning suspected adverse drug reactions (ADRs) within the European Economic Area. Despite its various functionalities, the current format for electronic submissions of safety reports lacks a specific data element indicating medicine exposure during pregnancy. OBJECTIVE: This paper aims to address the limitations of existing approaches by developing a rule-based algorithm in EV that more reliably identifies cases that are truly representative of an ADR during pregnancy. METHODS: The study utilised the standardised MedDRA query (SMQ) 'Pregnancy and neonatal topics' (PNT) as a benchmark for comparison. Recognising that the SMQ PNT also retrieves healthy pregnancy outcomes, contraceptive failure, failed abortifacients as well as ADRs not associated with pregnancy, a novel algorithm was tailored to improve the accuracy of identifying suspected ADRs occurring during pregnancy. RESULTS: Upon testing, the algorithm demonstrated superior performance, correctly predicting 90% of cases reporting an ADR during pregnancy, compared to 54% achieved by the SMQ PNT. The implementation of the algorithm in EV led to the retrieval of 202,426 cases. CONCLUSION: The development and successful testing of the novel algorithm represents a step forward in pregnancy-specific signal detection in EV. Because signals associated with pregnancy may be diluted in a large database such as EV, this study lays the groundwork for future research to evaluate the effectiveness of disproportionality methods on a more refined subset of pregnancy-related ADR reports.

Lessons Learned on Observed-to-Expected Analysis Using Spontaneous Reports During Mass Vaccination.

During the COVID-19 vaccination campaign, observed-to-expected analysis was used by the European Medicines Agency to contextualise data from spontaneous reports to generate real-time evidence on emerging safety concerns that may impact the benefit-risk profile of COVID-19 vaccines. Observed-to-expected analysis compares the number of cases spontaneously reported for an event of interest after vaccination ('observed') to the 'expected' number of cases anticipated to occur in the same number of individuals had they not been vaccinated. Observed-to-expected analysis is a robust methodology that relies on several assumptions that have been described in regulatory guidelines and scientific literature. The use of observed-to-expected analysis to support the safety monitoring of COVID-19 vaccines has provided valuable insights and lessons on its design and interpretability, which could prove to be beneficial in future analyses. When undertaking an observed-to-expected analysis within the context of safety monitoring, several aspects need attention. In particular, we emphasise the importance of stratified and harmonised data collection both for vaccine exposure and spontaneous reporting data, the need for alignment between coding dictionaries and the crucial role of accurate background incidence rates for adverse events of special interest. While these considerations and recommendations were determined in the context of the COVID-19 mass vaccination setting, they are generalisable in principle.

Predicting the submission frequency of periodic safety update reports: development and application of the EURD tool.

Introduction: Periodic Safety Update Reports (PSURs) are a key pharmacovigilance tool for the continuous evaluation of the benefit-risk balance of a medicinal product in the post-authorisation phase. The PSUR submission frequency for authorised active substances and combinations of active substances across the EU is individually determined. The objective of this research was the development and application of the EURD tool, a statistical method based on readily available safety data to predict PSUR frequencies and to ensure a consistent risk-based approach. Methods: First, variables considered relevant in determining the PSUR frequency were identified from data sources available at the European Medicines Agency. A subsequent first survey with National Competent Authorities in Europe lead to a prioritisation of identified variables, while a second survey was carried out to propose the PSUR frequencies for a set of substances. Finally, a regression model was built on the information collected, applied to a larger list of substances and its results tested via a third survey with the same experts. Results: The developed EURD tool was applied to the 1,032 EURD list entries with a PSUR assessment deferred to 2025 at the time of the creation of the list in 2012. As the number of procedures would have had a significant impact on the workload for the European Medicines Regulatory Network (EMRN), in a second step the workload impact was estimated after allocating the entries according to their proposed frequency. The analysis suggests that all entries could be reviewed by 2038 by increasing the median workload by 15% (from 868 to 1,000 substances/year). Conclusion: The EURD tool is the first data-driven application for supporting decision making of PSUR frequencies based on relevant active substance safety data. While we illustrated its potential for improving the assignment of PSUR submission frequencies for active substances authorised in the EU, other institutions requiring periodic assessment of safety data and balancing of the resulting workload could benefit from it.

Safety Monitoring of COVID-19 Vaccines: Perspective from the European Medicines Agency.

Prior to deployment of coronavirus disease 2019 (COVID-19) vaccines in the European Union in 2021, a high vaccine uptake leading to an unprecedented volume of safety data from spontaneous reports and real-world evidence, was anticipated. The European Medicines Agency (EMA) implemented specific activities to ensure enhanced monitoring of emerging vaccine safety information, including intensive monitoring of reports of adverse events of special interest and the use of observed-to-expected analyses. The EMA also commissioned several independent observational studies using a large network of electronic healthcare databases and primary data collection via mobile and web-based applications. This preparedness was key for two high-profile safety signals: thrombosis with thrombocytopenia syndrome (TTS), a new clinical entity associated with adenovirus-vectored vaccines, and myocarditis/pericarditis with messenger RNA vaccines. With no existing case definition nor background rates, the signal of TTS posed particular challenges. Nevertheless, it was rapidly identified, evaluated, contextualized and the risk minimized thanks to close surveillance and an efficient use of available evidence, clinical expertise and flexible regulatory tools. The two signals illustrated the complementarity between spontaneous and real-world data, the former enabling rapid risk identification and communication, the latter enabling further characterization. The COVID-19 pandemic has tremendously enhanced the development of tools and methods to harness the unprecedented volume of safety data generated for the vaccines. Areas for further improvement include the need for better and harmonized data collection across Member States (e.g., stratified vaccine exposure) to support signal evaluation in all population groups, risk contextualization, and safety communication.

The Impact of Mandatory Reporting of Non-Serious Safety Reports to EudraVigilance on the Detection of Adverse Reactions.

INTRODUCTION AND OBJECTIVE: European Union legislation has mandated the submission of European Economic Area non-serious reports to the EudraVigilance database since November 2017. As spontaneous reports of suspected adverse reactions to medicines represent a key source of safety signals, the European Medicines Agency has undertaken this work to assess the effects of this requirement on the characteristics of the reports submitted to EudraVigilance and on the detection of adverse drug reactions through routine analyses of the database. METHODS: Changes in the numbers of serious and non-serious reports transmitted to EudraVigilance were examined over the period during which the legislation was implemented. The numbers and nature of potential safety signals emerging from established statistical algorithms used at the European Medicines Agency applied either to only the serious reports or to all reports in EudraVigilance were compared. RESULTS: Up to November 2017, less than 25% of European Economic Area reports in EudraVigilance were classified as non-serious, since than this figure was slightly above 60%. This change accompanied an increase in the total number of reports received. Addition of non-serious reports to the signal detection process resulted in a small overall increase in signals of disproportionate reporting with some new signals of disproportionate reporting appearing and some existing signals of disproportionate reporting disappearing; the sensitivity of the signal detection system was slightly increased and the proportion of signals of disproportionate reporting that corresponded to known adverse drug reactions (a measure of efficiency) was unchanged. CONCLUSIONS: The change in legislation has led to a small increase in sensitivity, without affecting the efficiency of the routine statistical measures used. The number of non-serious reports as a proportion of reports in EudraVigilance is likely to increase over time and further monitoring of the impact on signal detection is required. Further work is also required on the qualitative impact of non-serious reports on the nature of signals detected and on their evaluation.