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Introduction Increased post-marketing reports of interstitial lung disease in Japan have been recognized. An understanding of its regional groundings can be important for the global pharmacovigilance community. Objective The objective of this study was to explore the correlation between high rates of interstitial lung disease reporting and regulatory actions in Japan. Methods Post-marketing interstitial lung disease-related label changes and interstitial lung disease reports were classified by the anatomical therapeutic chemical classification groups of the suspected drugs. Regulatory actions for the top interstitial lung disease-reporting drugs were compared. The interstitial lung disease reporting patterns of protein kinase inhibitors were compared to those of methotrexate. Results Interstitial lung disease-related label changes predominantly occurred for drugs in the anatomical therapeutic chemical classification groups L, J, C, and herbal medicines. Interstitial lung disease was reported most frequently for L group, especially for the protein kinase inhibitors. The regulatory actions for those drugs with the highest number of interstitial lung disease reports (methotrexate, protease kinase inhibitors, gemcitabine, docetaxel) plus monoclonal antibodies were analyzed. The ratio of interstitial lung disease reports to all reports over time was initially high in the re-examination period, while it was constantly low after the period expired. The increase in interstitial lung disease reporting was observed for the drugs for which interstitial lung disease was designated as a priority item in the use-results survey. Methotrexate had more interstitial lung disease reports with multiple suspected drugs and fewer reports with high completeness than the protease kinase inhibitors. Conclusions The high rates of interstitial lung disease reporting derived from mainly the anatomical therapeutic chemical classification group L drugs. Interstitial lung disease is the targeted adverse drug reaction in the use-results survey mandated in the re-examination of those drugs. This system provides at least one explanation for the high reporting of interstitial lung disease in Japan. Electronic supplementary material The online version of this article (10.1007/s40264-020-00968-7) contains supplementary material, which is available to authorized users.

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. Electronic supplementary material The online version of this article (10.1007/s40264-020-00957-w) contains supplementary material, which is available to authorized users.

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. Electronic supplementary material The online version of this article (10.1007/s40264-019-00861-y) contains supplementary material, which is available to authorized users.

Over a period of 3 years, the European Union's Innovative Medicines Initiative WEB-RADR project has explored the value of social media (i.e., information exchanged through the internet, typically via online social networks) for identifying adverse events as well as for safety signal detection. Many patients and clinicians have taken to social media to discuss their positive and negative experiences of medications, creating a source of publicly available information that has the potential to provide insights into medicinal product safety concerns. The WEB-RADR project has developed a collaborative English language workspace for visualising and analysing social media data for a number of medicinal products. Further, novel text and data mining methods for social media analysis have been developed and evaluated. From this original research, several recommendations are presented with supporting rationale and consideration of the limitations. Recommendations for further research that extend beyond the scope of the current project are also presented.

Introduction Adverse drug reactions related to drug–drug interactions cause harm to patients. There is a body of research on signal detection for drug interactions in collections of individual case reports, but limited use in regular pharmacovigilance. Objective The aim of this study was to evaluate the feasibility of signal detection of drug–drug interactions in collections of individual case reports of suspected adverse drug reactions. Methods This study was conducted in VigiBase, the WHO global database of individual case safety reports. The data lock point was 31 August 2016, which provided 13.6 million reports for analysis after deduplication. Statistical signal detection was performed using a previously developed predictive model for possible drug interactions. The model accounts for an interaction disproportionality measure, expressed suspicion of an interaction by the reporter, potential for interaction through cytochrome P450 activity of drugs, and reported information indicative of unexpected therapeutic response or altered therapeutic effect. Triage filters focused the preliminary signal assessment on combinations relating to serious adverse events with case series of no more than 30 reports from at least two countries, with at least one report during the previous 2 years. Additional filters sought to eliminate already known drug interactions through text mining of standard literature sources. Preliminary signal assessment was performed by a multidisciplinary group of pharmacovigilance professionals from Uppsala Monitoring Centre and collaborating organizations, whereas in-depth signal assessment was performed by experienced pharmacovigilance assessors. Results We performed preliminary signal assessment for 407 unique drug pairs. Of these, 157 drug pairs were considered already known to interact, whereas 232 were closed after preliminary assessment for other reasons. Ten drug pairs were subjected to in-depth signal assessment and an additional eight were decided to be kept under review awaiting additional reports. The triage filters had a major impact in focusing our preliminary signal assessment on just 14% of the statistical signals generated by the predictive model for drug interactions. In-depth assessment led to three signals communicated with the broader pharmacovigilance community, six closed signals and one to be kept under review. Conclusion This study shows that signals of adverse drug interactions can be detected through broad statistical screening of individual case reports. It further shows that signal assessment related to possible drug interactions requires more detailed information on the temporal relationship between different drugs and the adverse event. Future research may consider whether interaction signal detection should be performed not for individual adverse event terms but for pairs of drugs across a spectrum of adverse events. Electronic supplementary material The online version of this article (10.1007/s40264-020-00939-y) contains supplementary material, which is available to authorized users.

Objective To describe and evaluate vigiGroup - a consensus clustering algorithm which can identify groups of individual case reports referring to similar suspected adverse drug reactions and describe associated adverse event profiles, accounting for co-reported adverse event terms. Materials and methods Consensus clustering is achieved by grouping pairs of reports that are repeatedly placed together in the same clusters across a set of mixture model-based cluster analyses. The latter use empirical Bayes statistical shrinkage for improved performance. As baseline comparison, we considered a regular mixture model-based cluster analysis. Three randomly selected drugs in VigiBase, the World Health Organization’s global database of Individual Case Safety Reports were analyzed: sumatriptan, ambroxol and tacrolimus. Clustering stability was assessed using the adjusted Rand index, ranging between -1 and +1, and clinical coherence was assessed through an intruder detection analysis. Results For the three drugs considered, vigiGroup achieved stable and coherent results with adjusted Rand indices between +0.80 and +0.92, and intruder detection rates between 86% and 94%. Consensus clustering improved both stability and clinical coherence compared to mixture model-based clustering alone. Statistical shrinkage improved the stability of clusters compared to the baseline mixture model, as well as the cross-validated log-likelihood. Conclusions The proposed algorithm can achieve adequate stability and clinical coherence in clustering individual case reports, thereby enabling better identification of case series and associated adverse event profiles in pharmacovigilance. The use of empirical Bayes shrinkage and consensus clustering each led to meaningful improvements in performance.

Purpose To develop a method for data‐driven exploration in pharmacovigilance and illustrate its use by identifying the key features of individual case safety reports related to medication errors. Methods We propose vigiPoint, a method that contrasts the relative frequency of covariate values in a data subset of interest to those within one or more comparators, utilizing odds ratios with adaptive statistical shrinkage. Nested analyses identify higher order patterns, and permutation analysis is employed to protect against chance findings. For illustration, a total of 164 000 adverse event reports related to medication errors were characterized and contrasted to the other 7 833 000 reports in VigiBase, the WHO global database of individual case safety reports, as of May 2013. The initial scope included 2000 features, such as patient age groups, reporter qualifications, and countries of origin. Results vigiPoint highlighted 109 key features of medication error reports. The most prominent were that the vast majority of medication error reports were from the United States (89% compared with 49% for other reports in VigiBase); that the majority of reports were sent by consumers (53% vs 17% for other reports); that pharmacists (12% vs 5.3%) and lawyers (2.9% vs 1.5%) were overrepresented; and that there were more medication error reports than expected for patients aged 2‐11 years (10% vs 5.7%), particularly in Germany (16%). Conclusions vigiPoint effectively identified key features of medication error reports in VigiBase. More generally, it reduces lead times for analysis and ensures reproducibility and transparency. An important next step is to evaluate its use in other data.

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

Over a period of 5 years, the Innovative Medicines Initiative PROTECT (Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium) project has addressed key research questions relevant to the science of safety signal detection. The results of studies conducted into quantitative signal detection in spontaneous reporting, clinical trial and electronic health records databases are summarised and 39 recommendations have been formulated, many based on comparative analyses across a range of databases (e.g. regulatory, pharmaceutical company). The recommendations point to pragmatic steps that those working in the pharmacovigilance community can take to improve signal detection practices, whether in a national or international agency or in a pharmaceutical company setting. PROTECT has also pointed to areas of potentially fruitful future research and some areas where further effort is likely to yield less.

Introduction and Objective Social media has been proposed as a possibly useful data source for pharmacovigilance signal detection. This study primarily aimed to evaluate the performance of established statistical signal detection algorithms in Twitter/Facebook for a broad range of drugs and adverse events. Methods Performance was assessed using a reference set by Harpaz et al., consisting of 62 US Food and Drug Administration labelling changes, and an internal WEB-RADR reference set consisting of 200 validated safety signals. In total, 75 drugs were studied. Twitter/Facebook posts were retrieved for the period March 2012 to March 2015, and drugs/events were extracted from the posts. We retrieved 4.3 million and 2.0 million posts for the WEB-RADR and Harpaz drugs, respectively. Individual case reports were extracted from VigiBase for the same period. Disproportionality algorithms based on the Information Component or the Proportional Reporting Ratio and crude post/report counting were applied in Twitter/Facebook and VigiBase. Receiver operating characteristic curves were generated, and the relative timing of alerting was analysed. Results Across all algorithms, the area under the receiver operating characteristic curve for Twitter/Facebook varied between 0.47 and 0.53 for the WEB-RADR reference set and between 0.48 and 0.53 for the Harpaz reference set. For VigiBase, the ranges were 0.64–0.69 and 0.55–0.67, respectively. In Twitter/Facebook, at best, 31 (16%) and four (6%) positive controls were detected prior to their index dates in the WEB-RADR and Harpaz references, respectively. In VigiBase, the corresponding numbers were 66 (33%) and 17 (27%). Conclusions Our results clearly suggest that broad-ranging statistical signal detection in Twitter and Facebook, using currently available methods for adverse event recognition, performs poorly and cannot be recommended at the expense of other pharmacovigilance activities. Electronic supplementary material The online version of this article (10.1007/s40264-018-0699-2) contains supplementary material, which is available to authorized users.

Disproportionality analyses are used in many organisations to identify adverse drug reactions (ADRs) from spontaneous report data. Reporting patterns vary over time, with patient demographics, and between different geographical regions, and therefore subgroup analyses or adjustment by stratification may be beneficial. The objective of this study was to evaluate the performance of subgroup and stratified disproportionality analyses for a number of key covariates within spontaneous report databases of differing sizes and characteristics. Using a reference set of established ADRs, signal detection performance (sensitivity and precision) was compared for stratified, subgroup and crude (unadjusted) analyses within five spontaneous report databases (two company, one national and two international databases). Analyses were repeated for a range of covariates: age, sex, country/region of origin, calendar time period, event seriousness, vaccine/non-vaccine, reporter qualification and report source. Subgroup analyses consistently performed better than stratified analyses in all databases. Subgroup analyses also showed benefits in both sensitivity and precision over crude analyses for the larger international databases, whilst for the smaller databases a gain in precision tended to result in some loss of sensitivity. Additionally, stratified analyses did not increase sensitivity or precision beyond that associated with analytical artefacts of the analysis. The most promising subgroup covariates were age and region/country of origin, although this varied between databases. Subgroup analyses perform better than stratified analyses and should be considered over the latter in routine first-pass signal detection. Subgroup analyses are also clearly beneficial over crude analyses for larger databases, but further validation is required for smaller databases.

Individual case reports of suspected harm from medicines are fundamental for signal detection in postmarketing surveillance. Their effective analysis requires reliable data and one challenge is report duplication. These are multiple unlinked records describing the same suspected adverse drug reaction (ADR) in a particular patient. They distort statistical screening and can mislead clinical assessment. Many organisations rely on rule-based detection, but probabilistic record matching is an alternative. The aim of this study was to evaluate probabilistic record matching for duplicate detection, and to characterise the main sources of duplicate reports within each data set. vigiMatch™, a published probabilistic record matching algorithm, was applied to the WHO global individual case safety reports database, VigiBase®, for reports submitted between 2000 and 2010. Reported drugs, ADRs, patient age, sex, country of origin, and date of onset were considered in the matching. Suspected duplicates for the UK, Denmark, and Spain were reviewed and classified by the respective national centre. This included evaluation to determine whether confirmed duplicates had already been identified by in-house, rule-based screening. Furthermore, each confirmed duplicate was classified with respect to the likely source of duplication. For each country, the proportions of suspected duplicates classified as confirmed duplicates, likely duplicates, otherwise related, and unrelated were obtained. The proportions of confirmed or likely duplicates that were not previously known by the national organisation were determined, and variations in the rates of suspected duplicates across subsets of reports were characterised. Overall, 2.5 % of the reports with sufficient information to be evaluated by vigiMatch were classified as suspected duplicates. The rates for the three countries considered in this study were 1.4 % (UK), 1.0 % (Denmark), and 0.7 % (Spain). Higher rates of suspected duplicates were observed for literature reports (11 %) and reports with fatal outcome (5 %), whereas a lower rate was observed for reports from consumers and non-health professionals (0.5 %). The predictive value for confirmed or likely duplicates among reports flagged as suspected duplicates by vigiMatch ranged from 86 % for the UK, to 64 % for Denmark and 33 % for Spain. The proportions of confirmed duplicates that were previously unknown to national centres ranged from 89 % for Spain, to 60 % for the UK and 38 % for Denmark, despite in-house duplicate detection processes in routine use. The proportion of unrelated cases among suspected duplicates were below 10 % for each national centre in the study. Probabilistic record matching, as implemented in vigiMatch, achieved good predictive value for confirmed or likely duplicates in each data source. Most of the false positives corresponded to otherwise related reports; less than 10 % were altogether unrelated. A substantial proportion of the correctly identified duplicates had not previously been detected by national centre activity. On one hand, vigiMatch highlighted duplicates that had been missed by rule-based methods, and on the other hand its lower total number of suspected duplicates to review improved the accuracy of manual review.

Individual case safety reports of suspected harm from medicines are fundamental to post-marketing surveillance. Their value is directly proportional to the amount of clinically relevant information they include. To improve the quality of the data, communication between stakeholders is essential and can be facilitated by a simple score and visualisation of the results.The objective of this study was to propose a measure of completeness and identify predictors of well-documented reports, globally.The Uppsala Monitoring Centre has developed the vigiGrade completeness score to measure the amount of clinically relevant information in structured format, without reflecting whether the information establishes causality between the drug and adverse event. The vigiGrade completeness score (C) starts at 1 for reports with information on time-to-onset, age, sex, indication, outcome, report type, dose, country, primary reporter and comments. For each missing dimension, a penalty is detracted which varies with clinical relevance. We classified reports with C0.8 as well-documented and identified all such reports in the WHO global individual case safety report database, VigiBase, from 2007 to January 2012. We utilised odds ratios with statistical shrinkage to identify subgroups with unexpectedly high proportions of well-documented reports.Altogether, 430,000 (13 %) of the studied reports achieved C0.8 in VigiBase. For VigiBase as a whole, the median completeness was 0.41 with an interquartile range of 0.26-0.63. Two out of three well-documented reports come from Europe, and two out of three from physicians. Among the countries with more than 1,000 reports in total, the highest rate of well-documented reports is 65 % in Italy. Tunisia, Spain, Portugal, Croatia and Denmark each have rates above 50 %, and another 20 countries have rates above 30 %. On the whole, 24 % of the reports from physicians are well-documented compared with only 4 % for consumers/non-health professionals. Notably, Denmark and Norway have more than 50 % well-documented reports from consumers/non-health professionals and higher rates than for physicians. The rate of well-documented reports for the E2B format is 11 % compared with 22 % for the older INTDIS (International Drug Information System) format. However, for E2B reports entered via the WHO programme's e-reporting system VigiFlow, the rate is 29 %.Overall, only one report in eight provides the desired level of information, but much higher proportions are observed for individual countries. Physicians and e-reporting tools also generate greater proportions of well-documented reports overall. Reports from consumers/non-health professionals in specific regions have excellent quality, which illustrates their potential for the future. vigiGrade has already provided valuable information by highlighting data quality issues both in Italy and the USA.

Introduction A number of safety signals—complex regional pain syndrome (CRPS), postural orthostatic tachycardia syndrome (POTS), and chronic fatigue syndrome (CFS)—have emerged with human papillomavirus (HPV) vaccines, which share a similar pattern of symptomatology. Previous signal evaluations and epidemiological studies have largely relied on traditional methodologies and signals have been considered individually. Objective The aim of this study was to explore global reporting patterns for HPV vaccine for subgroups of reports with similar adverse event (AE) profiles. Methods All individual case safety reports (reports) for HPV vaccines in VigiBase® until 1 January 2015 were identified. A statistical cluster analysis algorithm was used to identify natural groupings based on AE profiles in a data-driven exploratory analysis. Clinical assessment of the clusters was performed to identify clusters relevant to current safety concerns. Results Overall, 54 clusters containing at least five reports were identified. The four largest clusters included 71 % of the analysed HPV reports and described AEs included in the product label. Four smaller clusters were identified to include case reports relevant to ongoing safety concerns (total of 694 cases). In all four of these clusters, the most commonly reported AE terms were headache and dizziness and fatigue or syncope; three of these four AE terms were reported in >50 % of the reports included in the clusters. These clusters had a higher proportion of serious cases compared with HPV reports overall (44–89 % in the clusters compared with 24 %). Furthermore, only a minority of reports included in these clusters included AE terms of diagnoses to explain these symptoms. Using proportional reporting ratios, the combination of headache and dizziness with either fatigue or syncope was found to be more commonly reported in HPV vaccine reports compared with non-HPV vaccine reports for females aged 9–25 years. This disproportionality remained when results were stratified by age and when those countries reporting the signals of CRPS (Japan) and POTS (Denmark) were excluded. Conclusions Cluster analysis reveals additional reports of AEs following HPV vaccination that are serious in nature and describe symptoms that overlap those reported in cases from the recent safety signals (POTS, CRPS, and CFS), but which do not report explicit diagnoses. While the causal association between HPV vaccination and these AEs remains uncertain, more extensive analyses of spontaneous reports can better identify the relevant case series for thorough signal evaluation. Electronic supplementary material The online version of this article (doi:10.1007/s40264-016-0456-3) contains supplementary material, which is available to authorized users.

A large proportion of potential drug interactions are known from pre-authorization studies, but adverse drug reactions (ADRs) due to interactions (adverse drug interactions) are often first detected through astute observation in clinical practice. Individual case safety reports (ICSRs) are collected from broad patient populations and allow for the identification of groups of similar reports. Systematic screening for adverse drug interactions in ICSRs will require an understanding of which information on these reports can be suggestive of adverse drug interactions.The aim of the study was to identify what reported information may support the identification of drug interaction safety signals in collections of ICSRs.Three previously published safety signals of suspected adverse drug interactions were re-evaluated. To this end, 137 reports related to these signals were retrieved from the WHO Global ICSR Database, VigiBase™, and corresponding original reports were obtained from national pharmacovigilance centres. Criteria from an operational score for causality analysis of drug interactions of clinical cases, the Drug Interaction Probability Scale (DIPS), were applied to each of these reports with the aim of identifying what supportive information tends to be available in ICSRs. For three DIPS elements (plausible time course, resolution of the ADR after terminating the drug inducing the interaction without changes in affected drug therapy (positive dechallenge) and alternative causes of the reaction) we also compared the amount of information in VigiBase™ and in original reports, and in free text and structured data.Commonly fulfilled DIPS elements on reports supporting an adverse drug interaction signal were plausible time course (50 reports; 36%) and positive dechallenge (8 reports; 6%). Alternative causes for the observed adverse reaction were observed in 72 (53%) reports. We found limited differences between VigiBase™ and original reports for the structured data, although a substantial amount of additional information was available in free text in original reports.Information on plausible time courses and resolution of the adverse reaction upon withdrawal of the drug suspected to have induced the interaction may be a useful element in identifying suspected adverse drug interactions from ICSRs. Of these, plausible time course is by far the most commonly reported element in the three signals studied here. Our analysis also demonstrated the importance of sharing and analysing information available in free text where relevant clinical details are often available, such as those mentioned above, along with severity and dosage changes.

Adverse drug interaction surveillance in collections of Individual Case Safety Reports (ICSRs) remains underdeveloped. Most efforts to date have focused on disproportionality analysis, but the empirical support for its value is based on isolated examples. Additionally, too little attention has been given to the potential value of the detailed content of ICSRs for improved adverse drug interaction surveillance.The aim of the study was to identify reporting patterns indicative of suspected adverse drug interactions before the drug interactions are generally established.A reference set of known adverse drug interactions and drug pairs not known to interact was constructed from information added to Stockley's Drug Interactions Alerts between the first quarter of 2007 and the third quarter of 2009. The reference set was used to systematically study differences in reporting patterns between adverse drug interactions before they are generally established and adverse drug reactions (ADRs) to drug pairs that are not known to interact, in the WHO Global ICSR Database, VigiBase. The scope of the study included pharmacological properties such as common cytochrome P450 metabolism, explicit suspicions of drug interactions as noted by the reporter, clinical details such as dose and treatment overlap, and the lower limit of the 95% credibility interval of a three-way measure of disproportionality, Omega(025) (Ω(025)), based on the total number of reports on two drugs and one ADR together. Analyses were carried out including and excluding concomitant medicines.Five reporting patterns were highlighted as particularly strong indicators of adverse drug interactions before they are known: suspicion of interactions as noted by the reporter in a case narrative, the assignment of the two drugs as interacting or through an ADR term; co-reporting of effect increased with the drug pair; and, finally, an excess total number of reports on the ADR together with the two drugs, as measured by Ω(025). Overall, the inclusion of concomitant medicines led to a larger number of true adverse drug interactions being highlighted, but at a substantial decrease in the strength of most indicators. Notably, the inclusion of concomitant medicines completely eliminated the value of Ω(025) as an indicator of adverse drug interactions, in this systematic evaluation.Reported suspicion of interactions as noted by the reporter in a case narrative, the assignment of the two drugs as interacting or through an ADR term; co-reporting of effect increased with the drug pair and by the Ω(025) each provide unique information to highlight adverse drug interactions before they become known in the literature. To our knowledge, this is the first systematic analysis demonstrating the value of disproportionality analysis for adverse drug interactions using a comprehensive reference set, and the first study to consider a broader basis including clinical information for systematic drug interaction surveillance.

Medicines improve health and the chances of survival in a wide variety of conditions. At the same time, no substance with pharmacological effects is without hazard. Adverse drug reactions (ADRs) can be associated with the intended pharmacological effect of the medicine (eg bleeding from warfarin)

Interaction between drug substances may yield excessive risk of adverse drug reactions (ADRs) when two drugs are taken in combination. Collections of individual case safety reports (ICSRs) related to suspected ADR incidents in clinical practice have proven to be very useful in post-marketing surveillance for pairwise drug--ADR associations, but have yet to reach their full potential for drug-drug interaction surveillance. In this paper, we implement and evaluate a shrinkage observed-to-expected ratio for exploratory analysis of suspected drug-drug interaction in ICSR data, based on comparison with an additive risk model. We argue that the limited success of previously proposed methods for drug-drug interaction detection based on ICSR data may be due to an underlying assumption that the absence of interaction is equivalent to having multiplicative risk factors. We provide empirical examples of established drug-drug interaction highlighted with our proposed approach that go undetected with logistic regression. A database wide screen for suspected drug-drug interaction in the entire WHO database is carried out to demonstrate the feasibility of the proposed approach. As always in the analysis of ICSRs, the clinical validity of hypotheses raised with the proposed method must be further reviewed and evaluated by subject matter experts.

Background Detection of unknown risks with marketed medicines is key to securing the optimal care of individual patients and to reducing the societal burden from adverse drug reactions. Large collections of individual case reports remain the primary source of information and require effective analytics to guide clinical assessors towards likely drug safety signals. Disproportionality analysis is based solely on aggregate numbers of reports and naively disregards report quality and content. However, these latter features are the very fundament of the ensuing clinical assessment. Objective Our objective was to develop and evaluate a data-driven screening algorithm for emerging drug safety signals that accounts for report quality and content. Methods vigiRank is a predictive model for emerging safety signals, here implemented with shrinkage logistic regression to identify predictive variables and estimate their respective contributions. The variables considered for inclusion capture different aspects of strength of evidence, including quality and clinical content of individual reports, as well as trends in time and geographic spread. A reference set of 264 positive controls (historical safety signals from 2003 to 2007) and 5,280 negative controls (pairs of drugs and adverse events not listed in the Summary of Product Characteristics of that drug in 2012) was used for model fitting and evaluation; the latter used fivefold cross-validation to protect against over-fitting. All analyses were performed on a reconstructed version of VigiBase® as of 31 December 2004, at around which time most safety signals in our reference set were emerging. Results The following aspects of strength of evidence were selected for inclusion into vigiRank: the numbers of informative and recent reports, respectively; disproportional reporting; the number of reports with free-text descriptions of the case; and the geographic spread of reporting. vigiRank offered a statistically significant improvement in area under the receiver operating characteristics curve (AUC) over screening based on the Information Component (IC) and raw numbers of reports, respectively (0.775 vs. 0.736 and 0.707, cross-validated). Conclusions Accounting for multiple aspects of strength of evidence has clear conceptual and empirical advantages over disproportionality analysis. vigiRank is a first-of-its-kind predictive model to factor in report quality and content in first-pass screening to better meet tomorrow’s post-marketing drug safety surveillance needs.

This study aimed to develop an algorithm for uncovering associations masked by extreme reporting rates, characterize the occurrence of masking by influential outliers in two spontaneous reporting databases and evaluate the impact of outlier removal on disproportionality analysis.We propose an algorithm that identifies influential outliers and carries out parallel analysis after their omission. It considers masking of drugs as well as of adverse drug reactions (ADRs), uses a direct measure of the masking effect and makes no assumptions regarding the number of outliers per drug or ADR. The occurrence of masking is characterized in the WHO Global Individual Case Safety Report database, VigiBase and a regional collection of reports from Shanghai, China.For WHO-ART critical terms such as myocardial infarction, rhabdomyolysis and hypoglycaemia outlier removal led to a 25-50% increase in the number of Statistics of Disproportionate Reporting (SDR) and gains in time to detection of 1-2 years, while keeping the rate of spurious SDRs from the parallel analysis at 1%. Twenty-three per cent of VigiBase and 18% of Shanghai SRS reports listed an influential outlier. Twenty-seven per cent of the ADRs and 5% of the drugs in VigiBase, and 2% of the ADRs and 3% of the drugs in Shanghai SRS were involved in an outlier. The overall increase in the number of SDRs for both datasets was 3%.Masking by outliers has substantial impact on specific ADRs including, in VigiBase, rhabdomyolysis, myocardial infarction and hypoglycaemia. It is a local phenomenon involving a fair number of reports but yielding a limited number of additional SDRs.

As a first step towards implementing routine screening of safety issues specifically related to children at the Uppsala Monitoring Centre, this study was performed to explore reporting patterns of adverse reactions in children.The first aim of this study was to characterize and contrast child reports against adult reports in an overall drug and adverse reaction review. The second aim was to highlight increases in reporting of specific adverse reactions during recent years subdivided by age group.This was an exploratory study of internationally compiled individual case safety reports (ICSRs).Reports were extracted from the WHO global ICSR database, VigiBase, up until 5 February 2010. The reports in VigiBase originate from 97 countries and the likelihood that a medicine caused the adverse effect may vary from case to case. Suspected duplicate and vaccine reports were excluded from the analysis, as were reports with age not specified. The Medical Dictionary for Regulatory Activities (MedDRA®) and the WHO Anatomical Therapeutic Chemical (ATC) classification were used to group adverse reactions and drugs.In the general review, reports from 1968 to 5 February 2010 were divided into child (aged 0-17 years) and adult (≥ 18 years) age groups. To highlight increases in reporting rates of specific adverse reactions during recent years, reports from 2005 to February 2010 were compared with reports from 1995 to 1999. The ten adverse reactions with the greatest difference in the proportion of reports between the two time periods were reviewed. In the latter analysis, the reports were subdivided into age groups: neonates ≤ 27 days; infants 28 days-23 months; children 2-11 years; and adolescents 12-17 years.A total of 3,472,183 reports were included in the study, of which 7.7% (268,145) were reports for children (0-17 years). Fifty-three percent of the child reports were for males, whilst 39% of reports in the adult group were for males. The proportion of reports involving children among Asian reports was 14% and was 15% among reports from Africa and Latin America, including the Caribbean. Among reports from North America, Oceania and Europe, 7% of the reports involved children. For the ATC drug classification groups, the largest difference in percentage units between the child and adult groups was seen for the anti-infective (33 vs 15%), respiratory (11 vs 5%) and dermatological (12 vs 7%) drug groups. Skin reactions were most commonly reported for the children; these were recorded in 35% of all reports for children and 23% of all reports for adults. Medication error-related terms in the younger age groups were reported with an increased frequency during recent years. This was particularly noticeable for the infants aged 28 days-23 months, recorded with accidental overdose and drug toxicity. Reactions reported in suspected connection to medicines used for attention-deficit hyperactivity disorders (ADHD) completely dominated the 2- to 11-year age group and were also common for the adolescents. This study presents variations in the reporting pattern in different age groups in VigiBase which, in some cases, could be due to susceptibilities to specific drug-related problems in certain age groups. Other likely explanations might be common drug usage and childhood diseases in these age groups.Reports in VigiBase received internationally for more than 40 years reflect real concerns for children taking medicines. The study highlights adverse reactions with an increased reporting during recent years, particularly those connected to the introduction of ADHD medicines in the child population. To enhance patient safety, medication errors indicating administration and dosing difficulties of drugs, especially in the younger age groups, require further attention.

The introduction of database-wide disproportionality screening for signal detection in spontaneous reporting systems (SRS) sparked a renaissance in pharmacovigilance research notable for numerous peer reviewed research articles, three expert working groups/white papers, countless meetings, symposia, workshops, graduate school theses and aggressive promotion of proprietary software. In addition to expanding the pharmacovigilance toolkit, this research has yielded ancillary benefits beyond patient safety, including an increased awareness of data quality issues such as case report duplication, the importance of adverse event coding terminology, the proper definition of signal in drug safety, the logic of signal detection and an admonition that conflicts of interest, both intellectual and financial, may not only involve the ‘usual suspects’ such as software vendors, but also other stakeholders that may not normally come tomind, such as regulatory authorities. The absence of standard test beds for systematic evaluation of signal detection methodologies is an impediment to progress. A fundamental question is what steps of a comprehensive screening strategy to test and what to compare performance against. Herein, we discuss the value of data mining evaluation studies that continue to appear, and related issues, in the hope of facilitating pharmacovigilance system benchmarking and optimization. As a basis for discussion we focus on three recent performance evaluations published in Drug Safety (see table I).

Background and objectives: Automated screening for excessive adverse drug reaction (ADR) reporting rates has proven useful as a tool to direct clinical review in large-scale drug safety signal detection. Some measures of disproportionality can be adjusted to eliminate any undue influence on the ADR reporting rate of covariates, such as patient age or country of origin, by using a weighted average of stratum-specific measures of disproportionality. Arguments have been made in favour of routine adjustment for a set of common potential confounders using stratification. The aim of this paper is to investigate the impact of using adjusted observed-to-expected ratios, as implemented for the Empirical Bayes Geometric Mean (EBGM) and the information component (IC) measures of disproportionality, for first-pass analysis of the WHO database. Methods: A simulation study was carried out to investigate the impact of simultaneous adjustment for several potential confounders based on stratification. Comparison between crude and adjusted observed-to-expected ratios were made based on random allocation of reports to a set of strata with a realistic distribution of stratum sizes. In a separate study, differences between the crude IC value and IC values adjusted for (combinations of) patient sex, age group, reporting quarter and country of origin, with respect to their concordance with a literature comparison were analysed. Comparison was made to the impact on signal detection performance of a triage criterion requiring reports from at least two countries before a drug-ADR pair was highlighted for clinical review. Results: The simulation study demonstrated a clear tendency of the adjusted observed-to-expected ratio to spurious (and considerable) underestimation relative to the crude one, in the presence of any very small strata in a stratified database. With carefully implemented stratification that did not yield any very small strata, this tendency could be avoided. Routine adjustment for potential confounders improved signal detection performance relative to the literature comparison, but the magnitude of the improvement was modest. The improvement from the triage criterion was more considerable. Discussion and conclusions: Our results indicate that first-pass screening based on observed-to-expected ratios adjusted with stratification may lead to missed signals in ADR surveillance, unless very small strata are avoided. In addition, the improvement in signal detection performance due to routine adjustment for a set of common confounders appears to be smaller than previously assumed. Other approaches to improving signal detection performance such as the development of refined triage criteria may be more promising areas for future research.