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Introduction In late 2019, a new coronavirus—severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)—was discovered in Wuhan, China, and the World Health Organization later declared coronavirus disease 2019 (COVID-19) a pandemic. Numerous drugs have been repurposed and investigated for therapeutic effectiveness in the disease, including those from “Solidarity,” an international clinical trial (azithromycin, chloroquine, hydroxychloroquine, the fixed combination lopinavir/ritonavir, and remdesivir). Objective Our objective was to evaluate adverse drug reaction (ADR) reporting for drugs when used in the treatment of COVID-19 compared with use for other indications, specifically focussing on sex differences. Method We extracted reports on COVID-19-specific treatments from the global ADR database, VigiBase, using an algorithm developed to identify reports that listed COVID-19 as the indication. The Solidarity trial drugs were included, as were any drugs reported ≥ 100 times. We performed a descriptive comparison of reports for the same drugs used in non-COVID-19 indications. The data lock point date was 7 June 2020. Results In total, 2573 reports were identified for drugs used in the treatment of COVID-19. In order of frequency, the most reported ADRs were electrocardiogram QT-prolonged, diarrhoea, nausea, hepatitis, and vomiting in males and diarrhoea, electrocardiogram QT-prolonged, nausea, vomiting, and upper abdominal pain in females. Other hepatic and kidney-related events were included in the top ten ADRs in males, whereas no hepatic or renal terms were reported for females. COVID-19-related reporting patterns differed from non-pandemic reporting for these drugs. Conclusion Review of a global database of suspected ADR reports revealed sex differences in the reporting patterns for drugs used in the treatment of COVID-19. Patterns of ADR sex differences need further elucidation.

Aim/Objective: To explore how and why pharmacovigilance assessors interact with the case narrative fields in spontaneous reports to better understand their challenges and needs, paving the way for future developments and support. Methods: We conducted semi-structured interviews with five PV specialists, three pharmacists and two medical doctors, who had an average of 14 years of hands-on experience working with signal detection and assessment. The participants were all purposively selected from four national regulatory pharmacovigilance centers, covering four geographically and societally different countries. Interviews were recorded, transcribed, and analyzed using thematic content analysis. Results: We identified four main themes and their respective subthemes (Table 1): narratives' content and value; processes utilizing the narratives; challenges assessors face and their needs when interacting with the narratives. All interviewees viewed the narrative as a source of useful clinical information used in multiple steps of signal management. The narrative provides a \"full clinical picture\", a chronological storyline that is difficult to obtain from the structured fields. However, assessors often face challenges with uninformative, repetitive, and incomplete narratives despite established guidelines and recommendations on how narratives could be written. This quality issue requires assessors to complement the narrative's information either using discharge letters or through multiple follow-ups with the initial reporter to better understand the full clinical picture. Furthermore, assessors struggle with the identification of informative narratives and with the extraction of the relevant clinical information. Assessors' needs are determined by the main challenges they face when interacting with the narratives or with ways to improve their current manual processes. Conclusion: Despite challenges in the interaction with case narratives, PV-assessors consider them the hallmark of suspected adverse event reports. Our study stresses the need for a clear implementation of what should be reported in the narratives. The study's insights highlight the importance of considering assessors' experiences with the narratives in the development of innovative tools.

Introduction Manual identification of case narratives with specific relevant information can be challenging when working with large numbers of adverse event reports (case series). The process can be supported with a search engine, but building search queries often remains a manual task. Suggesting terms to add to the search query could support assessors in the identification of case narratives within a case series. Objective The aim of this study is to explore the feasibility of identifying case narratives containing specific characteristics with a narrative search engine supported by artificial intelligence (AI) query suggestions. Methods The narrative search engine uses Best Match 25 (BM25) and suggests additional query terms from two word embedding models providing English and biomedical words to a human in the loop. We calculated the percentage of relevant narratives retrieved by the system (recall) and the percentage of retrieved narratives relevant to the search (precision) on an evaluation dataset including narratives from VigiBase, the World Health Organization global database of adverse event reports for medicines and vaccines. Exact-match search and BM25 search with the Relevance Model (RM3), an alternative way to expand queries, were used as comparators. Results The gold standard included 55/750 narratives labelled as relevant. Our narrative search engine retrieved on average 56.4% of the relevant narratives (recall), which is higher when compared with exact-match search (21.8%), without a significant drop in precision  (54.5% to 43.1%). The recall is also higher as compared with RM3 (34.4%). Conclusions Our study demonstrates that a narrative search engine supported by AI query suggestions can be a viable alternative to an exact-match search and BM25 search with RM3, since it can facilitate the retrieval of additional relevant narratives during signal assessments.

Introduction Patient reporting in pharmacovigilance is important and contributes to signal detection. However, descriptions of methodologies for using patient reports in signal detection are scarce, and published experiences of how patient reports are used in pharmacovigilance are limited to a few individual countries. Objective Our objective was to explore the contribution of patient reports to global signal detection in VigiBase. Methods Data were retrieved from VigiBase in September 2016. Drug–event-combination series were restricted to those with >50% patient reports, defined as reporter type “Consumer/non-health professional” per E2B reporting standard. vigiRank was applied to patient reports to prioritize combinations for assessment. Product information for healthcare professionals (HCPs) as well as patient information leaflets (PILs) were used as reference for information on adverse drug reactions (ADRs). Staff from the Uppsala Monitoring Centre and the Netherlands Pharmacovigilance Centre Lareb categorized the combinations. Potential signals proceeded to a more in-depth clinical review to determine whether the safety concern should be communicated as a “signal.” Results Of the 212 combinations assessed, 20 (9%) resulted in eight signals communicated within the World Health Organization (WHO) programme for international drug monitoring. Review of PILs revealed insufficient ADR descriptions for patients and examples of poor consistency with product information for HCPs. Patient narratives provided details regarding the experience and impact of ADRs and evidence that patients make causality and personal risk assessments. Conclusions Safety concerns described in patient reports can be identified in a global database including previously unknown ADRs as well as new aspects of known ADRs. Patient reports provide unique information valuable in signal assessment and should be included in signal detection. Novel approaches to highlighting patient reports in statistical signal detection can further improve the contribution of patient reports to pharmacovigilance.

Introduction Individual case reports are the main asset in pharmacovigilance signal management. Signal validation is the first stage after signal detection and aims to determine if there is sufficient evidence to justify further assessment. Throughout signal management, a prioritization of signals is continually made. Routinely collected health data can provide relevant contextual information but are primarily used at a later stage in pharmacoepidemiological studies to assess communicated signals. Objective The aim of this study was to examine the feasibility and utility of analysing routine health data from a multinational distributed network to support signal validation and prioritization and to reflect on key user requirements for these analyses to become an integral part of this process. Methods Statistical signal detection was performed in VigiBase, the WHO global database of individual case safety reports, targeting generic manufacturer drugs and 16 prespecified adverse events. During a 5-day study-a-thon, signal validation and prioritization were performed using information from VigiBase, regulatory documents and the scientific literature alongside descriptive analyses of routine health data from 10 partners of the European Health Data and Evidence Network (EHDEN). Databases included in the study were from the UK, Spain, Norway, the Netherlands and Serbia, capturing records from primary care and/or hospitals. Results Ninety-five statistical signals were subjected to signal validation, of which eight were considered for descriptive analyses in the routine health data. Design, execution and interpretation of results from these analyses took up to a few hours for each signal (of which 15–60 minutes were for execution) and informed decisions for five out of eight signals. The impact of insights from the routine health data varied and included possible alternative explanations, potential public health and clinical impact and feasibility of follow-up pharmacoepidemiological studies. Three signals were selected for signal assessment, two of these decisions were supported by insights from the routine health data. Standardization of analytical code, availability of adverse event phenotypes including bridges between different source vocabularies, and governance around the access and use of routine health data were identified as important aspects for future development. Conclusions Analyses of routine health data from a distributed network to support signal validation and prioritization are feasible in the given time limits and can inform decision making. The cost–benefit of integrating these analyses at this stage of signal management requires further research.