Supporting Pharmacovigilance Signal Validation and Prioritization with Analyses of Routinely Collected Health Data: Lessons Learned from an EHDEN Network Study.

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.

Decision Support for Signal Assessment of Large Case Series in Pharmacovigilance.

In pharmacovigilance, signal assessment of a medicinal product and adverse event can involve reviewing prohibitively large numbers of case reports. A prototype of a decision support tool guided by a needs assessment was developed to help manual review of many reports. In a preliminary qualitative evaluation, users said the tool was easy to use, improved efficiency and provided new insights.

Signal Detection in EUROmediCAT: Identification and Evaluation of Medication-Congenital Anomaly Associations and Use of VigiBase as a Complementary Source of Reference.

INTRODUCTION: Knowledge on the safety of medication use during pregnancy is often sparse. Pregnant women are generally excluded from clinical trials, and there is a dependence on post-marketing surveillance to identify teratogenic medications. AIMS: This study aimed to identify signals of potentially teratogenic medications using EUROmediCAT registry data on medication exposure in pregnancies with a congenital anomaly, and to investigate the use of VigiBase reports of adverse events of medications in the evaluation of these signals. METHODS: Signals of medication-congenital anomaly associations were identified in EUROmediCAT (21,636 congenital anomaly cases with 32,619 medication exposures), then investigated in a subset of VigiBase (45,749 cases and 165,121 exposures), by reviewing statistical reporting patterns and VigiBase case reports. Evidence from the literature and quantitative and qualitative aspects of both datasets were considered before recommending signals as warranting further independent investigation. RESULTS: EUROmediCAT analysis identified 49 signals of medication-congenital anomaly associations. Incorporating investigation in VigiBase and the literature, these were categorised as follows: four non-specific medications; 11 likely due to maternal disease; 11 well-established teratogens; two reviewed in previous EUROmediCAT studies with limited additional evidence; and 13 with insufficient basis for recommending follow-up. Independent investigations are recommended for eight signals: pregnen (4) derivatives with limb reduction; nitrofuran derivatives with cleft palate and patent ductus arteriosus; salicylic acid and derivatives with atresia or stenosis of other parts of the small intestine and tetralogy of Fallot; carbamazepine with atrioventricular septal defect and severe congenital heart defect; and selective beta-2-adrenoreceptor agonists with posterior urethral valve and/or prune belly. CONCLUSION: EUROmediCAT data should continue to be used for signal detection, accompanied by information from VigiBase and review of the existing literature to prioritise signals for further independent evaluation.

Risk Factor Considerations in Statistical Signal Detection: Using Subgroup Disproportionality to Uncover Risk Groups for Adverse Drug Reactions in VigiBase.

INTRODUCTION: In the treatment of the individual patient, a vision is to achieve the best possible balance between benefit and harm. Such tailored therapy relies upon the identification and characterisation of risk factors for adverse drug reactions. Information relevant to risk factor considerations can be captured in adverse event reports and could be utilised in statistical signal detection. OBJECTIVE: The aim of this study was to explore whether statistical screening of a broad range of risk factors within a global database of adverse event reports could uncover signals of risk groups for adverse drug reactions. METHODS: Subgroup disproportionality analysis was applied to 15.4 million reports entered in VigiBase, the World Health Organization (WHO) global database of individual case safety reports, up to August 2017. Disproportionality analyses for drug-adverse event pairs were performed (1) in the full database and (2) across a range of subgroups defined by the following covariates: patient age, sex, body mass index, pregnancy, underlying condition, reporting country, and geographical region. Drug-adverse event pairs disproportionately over-reported in such subgroups, but not in the full database, and with a substantial difference between the two observed-to-expected ratios, were highlighted as statistical signals. These were further prioritised, through filtering and sorting, for clinical assessment, whereafter clinically relevant signals were communicated to the pharmacovigilance community and the public. RESULTS: Assessments were performed for 354 prioritised statistical signals, resulting in seven communicated signals describing previously unrecognised potential risk groups related to age (elderly), sex (male and female), body mass index (underweight and obese), and geographical region (Asia), all except one for already established adverse drug reactions. Important aspects considered in the assessments included an evaluation of the disproportionate over-reporting in the subgroup by reviewing alternative explanations and reporting patterns for similar drugs/adverse events/subgroups, and a search for plausible mechanisms to support the risk hypothesis. CONCLUSIONS: This study reveals that it is possible to uncover signals of risk groups for adverse drug reactions through incorporation of broad risk factor screening into statistical signal detection in a global database of adverse event reports. Our findings suggest the potential to use such statistical methodologies for risk characterisation in subpopulations of concern.

Data-Driven Identification of Adverse Event Reporting Patterns for Japan in VigiBase, the WHO Global Database of Individual Case Safety Reports.

INTRODUCTION: Adverse event reporting patterns vary between countries, reflecting differences in reporting culture, clinical practice and underlying patient populations. Japan collects about 60,000 domestic adverse event reports yearly and shares serious reports with the World Health Organization (WHO) Programme for International Drug Monitoring in VigiBase, the WHO global database of individual case safety reports. Understanding these reports in the global context can be helpful for regulators worldwide and can aid hypothesis-generation for Japanese-specific vulnerabilities to adverse drug reactions. OBJECTIVE: The objective of this study was to explore differences in the reporting of adverse events between Japan and other countries. METHODS: vigiPoint is a method for data-driven exploration in pharmacovigilance. It outlines data subsets, pinpoints key features and facilitates expert review, using odds ratios subjected to statistical shrinkage to distinguish one data subset from another. Here, we compared 260,000 Japanese reports in E2B format classified as serious and received in VigiBase between 2013 and 2018 with 2.5 million reports from the rest of the world (of which 51% are from the USA). Reporting patterns for which the 99% credibility interval of the shrunk log-odds ratios were above 0.5 or below - 0.5 were flagged as key features. The shrinkage was set to the vigiPoint default corresponding to 1% of the size of the Japanese data subset. As a sensitivity analysis, additional vigiPoint comparisons were performed between Japan and, in turn, Africa, the Americas, the Americas except the USA and Canada, Asia and Europe. RESULTS: There were higher reporting rates in Japan from physicians (83% vs. 39%) and pharmacists (17% vs. 10%). It was also more common to see reports with more than five drugs per report (22% vs. 14%) and with a single adverse event (72% vs. 45%). More than half of the Japanese reports had a vigiGrade completeness score above 0.8 compared with about one in five from the rest of the world. There were more reports than expected for patients aged 70-89 years and fewer reports for adults aged 20-59 years. Adverse events reported more often in Japan included interstitial lung disease, abnormal hepatic function, decreased platelet count, decreased neutrophil count and drug eruption. Adverse events reported less often included death, fatigue, dyspnoea, pain and headache. Drugs reported more often in Japan included prednisolone, methotrexate and peginterferon alfa-2b. Drugs reported less often included rosiglitazone and adalimumab as well as blood substitutes and perfusion solutions. The findings were generally robust to the sensitivity analysis except for the less often reported drugs, many of which were rarely reported in most countries, except in the USA. CONCLUSION: Analysis of Japanese adverse event reporting patterns in a global context has revealed key features that may reflect possible pharmaco-ethnic vulnerabilities in the Japanese, as well as differences in adverse event reporting and clinical practice. This knowledge is essential in the global collaboration of signal detection afforded by the WHO Programme for International Drug Monitoring.

Paediatric safety signals identified in VigiBase: Methods and results from Uppsala Monitoring Centre.

PURPOSE: The purpose of this study is to uncover previously unrecognised risks of medicines in paediatric pharmacovigilance reports and thereby advance a safer use of medicines in paediatrics. METHODS: Individual case safety reports (ICSRs) with ages less than 18 years were retrieved from VigiBase, the World Health Organization (WHO) global database of ICSRs, in September 2014. The reports were grouped according to the following age spans: 0 to 27 days; 28 days to 23 months; 2 to 11 years; and 12 to 17 years. vigiRank, a data-driven predictive model for emerging safety signals, was used to prioritise the list of drug events by age groups. The list was manually assessed, and potential signals were identified to undergo in-depth assessment to determine whether a signal should be communicated. RESULTS: A total of 472 drug-event pairs by paediatric age groups were the subject of an initial manual assessment. Twenty-seven drug events from the two older age groups were classified as potential signals. An in-depth assessment resulted in eight signals, of which one concerned harm in connection with off-label use of dextromethorphan and another with accidental overdose of olanzapine by young children, and the remaining signals referred to potentially new causal associations for atomoxetine (two signals), temozolamide, deferasirox, levetiracetam, and desloratadine that could be relevant also for adults. CONCLUSIONS: Clinically relevant signals were uncovered in VigiBase by using vigiRank applied to paediatric age groups. Further refinement of the methodology is needed to identify signals in reports with ages under 2 years and to capture signals specific to the paediatric population as a risk group.

vigiRank for statistical signal detection in pharmacovigilance: First results from prospective real-world use.

PURPOSE: vigiRank is a data-driven predictive model for emerging safety signals. In addition to disproportionate reporting patterns, it also accounts for the completeness, recency, and geographic spread of individual case reporting, as well as the availability of case narratives. Previous retrospective analysis suggested that vigiRank performed better than disproportionality analysis alone. The purpose of the present analysis was to evaluate its prospective performance. METHODS: The evaluation of vigiRank was based on real-world signal detection in VigiBase. In May 2014, vigiRank scores were computed for pairs of new drugs and WHO Adverse Reaction Terminology critical terms with at most 30 reports from at least 2 countries. Initial manual assessments were performed in order of descending score, selecting a subset of drug-adverse drug reaction pairs for in-depth expert assessment. The primary performance metric was the proportion of initial assessments that were decided signals during in-depth assessment. As comparator, the historical performance for disproportionality- guided signal detection in VigiBase was computed from a corresponding cohort of drug-adverse drug reaction pairs assessed between 2009 and 2013. During this period, the requirement for initial manual assessment was a positive lower endpoint of the 95% credibility interval of the Information Component measure of disproportionality, observed for the first time. RESULTS: 194 initial assessments suggested by vigiRank's ordering eventually resulted in 6 (3.1%) signals. Disproportionality analysis yielded 19 signals from 1592 initial assessments (1.2%; P < .05). CONCLUSIONS: Combining multiple strength-of-evidence aspects as in vigiRank significantly outperformed disproportionality analysis alone in real-world pharmacovigilance signal detection, for VigiBase.

Longitudinal medical records as a complement to routine drug safety signal analysis.

PURPOSE: To explore whether and how longitudinal medical records could be used as a source of reference in the early phases of signal detection and analysis of novel adverse drug reactions (ADRs) in a global pharmacovigilance database. METHODS: Drug and ADR combinations from the routine signal detection process of VigiBase® in 2011 were matched to combinations in The Health Improvement Network (THIN). The number and type of drugs and ADRs from the data sets were investigated. For unlabelled combinations, graphical display of longitudinal event patterns (chronographs) in THIN was inspected to determine if the pattern supported the VigiBase combination. RESULTS: Of 458 combinations in the VigiBase data set, 190 matched to corresponding combinations in THIN (after excluding drugs with less than 100 prescriptions in THIN). Eighteen percent of the VigiBase and 9% of the matched THIN combinations referred to new drugs reported with serious reactions. Of the 112 unlabelled combinations matched to THIN, 52 chronographs were inconclusive mainly because of lack of data; 34 lacked any outstanding pattern around the time of prescription; 24 had an elevation of events in the pre-prescription period, hence weakened the suspicion of a drug relationship; two had an elevated pattern of events exclusively in the post-prescription period that, after review of individual patient histories, did not support an association. CONCLUSIONS: Longitudinal medical records were useful in understanding the clinical context around a drug and suspected ADR combination and the probability of a causal relationship. A drawback was the paucity of data for newly marketed drugs with serious reactions.