Duplicate detection in adverse drug reaction surveillance

The REporting of A Disproportionality Analysis for DrUg Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Explanation and Elaboration

In pharmacovigilance, disproportionality analyses based on individual case safety reports are widely used to detect safety signals. Unfortunately, publishing disproportionality analyses lacks specific guidelines, often leading to incomplete and ambiguous reporting, and carries the risk of incorrect conclusions when data are not placed in the correct context. The REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance (READUS-PV) statement was developed to address this issue by promoting transparent and comprehensive reporting of disproportionality studies. While the statement paper explains in greater detail the procedure followed to develop these guidelines, with this explanation paper we present the 14 items retained for READUS-PV guidelines, together with an in-depth explanation of their rationale and bullet points to illustrate their practical implementation. Our primary objective is to foster the adoption of the READUS-PV guidelines among authors, editors, peer reviewers, and readers of disproportionality analyses. Enhancing transparency, completeness, and accuracy of reporting, as well as proper interpretation of their results, READUS-PV guidelines will ultimately facilitate evidence-based decision making in pharmacovigilance.

A Comparison of Signals of Designated Medical Events and Non-designated Medical Events: Results from a Scoping Review

INTRODUCTION AND OBJECTIVE

The European Medicines Agency (EMA) maintains a list of designated medical events (DMEs), events that are inherently serious and are prioritized for signal detection, irrespective of statistical criteria. We have analysed the results of our previously published scoping review to determine whether DME signals differ from those of other adverse events in terms of time to communication and characteristics of supporting reports of suspected adverse drug reactions.

METHODS

For all signals, we obtained the launch year of medicinal products from textbooks or regulatory agencies, extracted the year of the first report in VigiBase and calculated the interval between the first report and communication (time to communication, TTC). We further retrieved the average completeness (via vigiGrade) of the reports in each case series in the years before the communication. We categorised as DME signals those concerning an event in the EMA's list. We described the two groups of signals using medians and interquartile ranges (IQR) and compared them using the Brunner-Munzel test, calculating 95% confidence intervals (95% CI) and P values.

RESULTS

Of 4520 signals, 919 concerned DMEs and 3601 concerned non-DMEs. Signals of DMEs were supported by a median of 15 reports (IQR 6-38 reports) with a completeness score of 0.52 (IQR 0.43-0.62) and signals of non-DMEs by 20 reports (IQR 6-84 reports) with a completeness score of 0.46 (IQR 0.38-0.56). The probability that a random DME signal was supported by fewer reports than non-DME signals was 0.56 (95% CI 0.54-0.58, P < 0.001) and that of one having lower average completeness was 0.39 (95% CI 0.36-0.41, P < 0.001). The median TTCs of DME and non-DME signals did not differ (10 years), but the TTC was as low as 2 years when signals (irrespective of classification) were supported by reports whose average completeness was > 0.80.

CONCLUSIONS

Signals of designated medical events were supported by fewer reports and higher completeness scores than signals of other adverse events. Although statistically significant, the differences in effect sizes between the two groups were small. This suggests that listing certain adverse events as DMEs is not having the expected effect of encouraging a focus on reports of the types of suspected adverse reactions that deserve special attention. Further enhancing the completeness of the reports of suspected adverse drug reactions supporting signals of designated medical events might shorten their time to communication and reduce the number of reports required to support them.

Navigating duplication in pharmacovigilance databases: a scoping review

OBJECTIVES

Pharmacovigilance databases play a critical role in monitoring drug safety. The duplication of reports in pharmacovigilance databases, however, undermines their data integrity. This scoping review sought to provide a comprehensive understanding of duplication in pharmacovigilance databases worldwide.

DESIGN

A scoping review.

DATA SOURCES

Reviewers comprehensively searched the literature in PubMed, Web of Science, Wiley Online Library, EBSCOhost, Google Scholar and other relevant websites.

ELIGIBILITY CRITERIA

Peer-reviewed publications and grey literature, without language restriction, describing duplication and/or methods relevant to duplication in pharmacovigilance databases from inception to 1 September 2023.

DATA EXTRACTION AND SYNTHESIS

We used the Joanna Briggs Institute guidelines for scoping reviews and conformed with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. Two reviewers independently screened titles, abstracts and full texts. One reviewer extracted the data and performed descriptive analysis, which the second reviewer assessed. Disagreements were resolved by discussion and consensus or in consultation with a third reviewer.

RESULTS

We screened 22 745 unique titles and 156 were eligible for full-text review. Of the 156 titles, 58 (47 peer-reviewed; 11 grey literature) fulfilled the inclusion criteria for the scoping review. Included titles addressed the extent (5 papers), prevention strategies (15 papers), causes (32 papers), detection methods (25 papers), management strategies (24 papers) and implications (14 papers) of duplication in pharmacovigilance databases. The papers overlapped, discussing more than one field. Advances in artificial intelligence, particularly natural language processing, hold promise in enhancing the efficiency and precision of deduplication of large and complex pharmacovigilance databases.

CONCLUSION

Duplication in pharmacovigilance databases compromises risk assessment and decision-making, potentially threatening patient safety. Therefore, efficient duplicate prevention, detection and management are essential for more reliable pharmacovigilance data. To minimise duplication, consistent use of worldwide unique identifiers as the key case identifiers is recommended alongside recent advances in artificial intelligence.

Cognitive dysfunction following finasteride use: a disproportionality analysis of the global pharmacovigilance database

BACKGROUND

Finasteride is commonly prescribed for androgenic alopecia and benign prostatic hyperplasia. However, concerns regarding its safety have been growing as cases of cognitive dysfunction have been reported.

METHODS

A disproportionality analysis was conducted on data collected between 1967 and 2022 to explore the potential association. Cases of cognitive dysfunction associated with finasteride use were identified, and the reporting odds ratio (rOR) was calculated with 95% confidence intervals to determine the strength of the association between the two variables. Sensitivity analyses were conducted to account for confounding by indication.

RESULTS

Among the 54,766 cases of adverse events reported for finasteride use, 1,624 (2.97%) were associated with cognitive dysfunction. The study found a significant disproportionality for cognitive dysfunction related to finasteride use (rOR 5.43, 95% CI 5.17-5.71). Most cases were considered serious (65.83%), with no signs of recovery (58.37%). Sensitivity analyses showed that patients younger than 45 years (rOR 7.30, 95% CI 6.39-8.35) and those with alopecia (rOR 5.52, 95% CI 5.15-5.91) reported more cognitive dysfunctions than their counterparts.

CONCLUSION

This study showed an increased reporting of cognitive dysfunction associated with finasteride use, especially among younger alopecia patients. Finasteride should be prescribed with caution, especially to younger alopecia patients.

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.

Strengthening the Pharmacovigilance System in Mexico: Implementation of VigiFlow and VigiLyze, as ICSR and Signal Detection Management Systems

Pharmacovigilance (PV) activities aim to identify potential risks of medicines and vaccines after they have been authorised in the market by collecting and analysing information on suspected adverse events from different stakeholders. These can be captured and transmitted electronically in the form of Individual Case Safety Reports (ICSRs). Hence, up-to-date ICSRs management systems, like VigiFlow and signal detection and management systems as VigiLyze, have an important role in the PV system of a country. In 2019, after various attempts to establish a PV database that could fulfil the needs of the country, Mexico's National Regulatory Authority, COFEPRIS (Federal Commission for the Prevention against Sanitary Risks) decided to implement these tools. This has been a successful project that is still ongoing, it has involved national and international organisations, and has required the participation and integration of different components of the national PV system. The implementation of these tools has allowed COFEPRIS to increase its reporting trends and quality of reporting, while contributing to make more efficient interactions and processes with PV stakeholders, even during the COVID-19 pandemic. It has also allowed them to strengthen their commitment to the WHO-Programme for International Drug Monitoring, while highlighting opportunities for improvement in the national PV scenario and in the PV tools themselves. The aim of this article is to describe the implementation process, give an overview of current results regarding ICSR data and processes, and highlight the achievements, challenges, and opportunities for improvement after the three years since the beginning of the project.

Association between immune checkpoint inhibitor-induced myocarditis and concomitant use of thiazide diuretics

Although an association has been reported between diuretics and myocarditis, it is unclear whether the risk of immune checkpoint inhibitor (ICI)-induced myocarditis is affected by concomitant diuretics. Thus, the aim of this work was to evaluate the impact of concomitant diuretics on ICI-induced myocarditis. This cross-sectional study used disproportionality analysis and a pharmacovigilance database to assess the risk of myocarditis with various diuretics in patients receiving ICIs via the analysis of data entered into the VigiBase database through December 2022. Multiple logistic regression analysis was performed to identify risk factors for myocarditis in patients who received ICIs. A total of 90 611 patients who received ICIs, including 975 cases of myocarditis, were included as the eligible dataset. A disproportionality in myocarditis was observed for loop diuretic use (reporting odds ratio 1.47, 95% confidence interval [CI] 1.02-2.04, P = .03) and thiazide use (reporting odds ratio 1.76, 95% CI 1.20-2.50, P < .01) in patients who received ICIs. The results of the multiple logistic regression analysis showed that the use of thiazides (odds ratio 1.67, 95% CI 1.15-2.34, P < .01) was associated with an increased risk of myocarditis in patients who received ICIs. Our findings may help to predict the risk of myocarditis in patients receiving ICIs.

Will the future of pharmacovigilance be more automated?

INTRODUCTION

Artificial intelligence (AI) based tools offer new opportunities for pharmacovigilance (PV) activities. Nevertheless, their contribution to PV needs to be tailored to preserve and strengthen medical and pharmacological expertise in drug safety.

AREAS COVERED

This work aims to describe PV tasks in which the contribution of AI and intelligent automation (IA) tools is required, in the context of a continuous increase of spontaneous reporting cases and regulatory tasks. A narrative review with expert selection of pertinent references was performed through Medline. Two areas were covered, management of spontaneous reporting cases and signal detection.

PERSPECTIVE

The use of AI and IA tools will assist a large spectrum of PV activities, both in public and private PV systems, in particular for tasks of low added value (e.g. initial quality check, verification of essential regulatory information, search for duplicates). Testing, validating, and integrating these tools in the PV routine are the actual challenges for modern PV systems, to guarantee high-quality standards in terms of case management and signal detection.

Identifying Safety Subgroups at Risk: Assessing the Agreement Between Statistical Alerting and Patient Subgroup Risk

INTRODUCTION

Identifying individual characteristics or underlying conditions linked to adverse drug reactions (ADRs) can help optimise the benefit-risk ratio for individuals. A systematic evaluation of statistical methods to identify subgroups potentially at risk using spontaneous ADR report datasets is lacking.

OBJECTIVES

In this study, we aimed to assess concordance between subgroup disproportionality scores and European Medicines Agency Pharmacovigilance Risk Assessment Committee (PRAC) discussions of potential subgroup risk.

METHODS

The subgroup disproportionality method described by Sandberg et al., and variants, were applied to statistically screen for subgroups at potential increased risk of ADRs, using data from the US FDA Adverse Event Reporting System (FAERS) cumulative from 2004 to quarter 2 2021. The reference set used to assess concordance was manually extracted from PRAC minutes from 2015 to 2019. Mentions of subgroups presenting potential differentiated risk and overlapping with the Sandberg method were included.

RESULTS

Twenty-seven PRAC subgroup examples representing 1719 subgroup drug-event combinations (DECs) in FAERS were included. Using the Sandberg methodology, 2 of the 27 could be detected (one for age and one for sex). No subgroup examples for pregnancy and underlying condition were detected. With a methodological variant, 14 of 27 examples could be detected.

CONCLUSIONS

We observed low concordance between subgroup disproportionality scores and PRAC discussions of potential subgroup risk. Subgroup analyses performed better for age and sex, while for covariates not well-captured in FAERS, such as underlying condition and pregnancy, additional data sources should be considered.

Investigating a Signal of Acquired Hemophilia Associated with COVID-19 Vaccination: A Systematic Case Review

Acquired hemophilia A (AHA), a rare but life-threatening disorder, most commonly occurs in older people and during pregnancy. During the coronavirus disease 2019 (COVID-19) vaccination campaign, an unexpected number of newly diagnosed AHA patients have been identified in clinical practice that were temporally related to COVID-19 vaccination. We present the result of a signal detection analysis aimed at exploring a possible association between COVID-19 immunization and occurrence of AHA. A disproportionality analysis on the World Health Organization (WHO) database was performed to investigate the presence of a signal of risk for AHA associated with COVID-19 vaccines. Reports of AHA associated with any COVID-19 vaccine included in the WHO database were then integrated with those available on the Food and Drug Administration Vaccine Adverse Events Reporting System and those published in the medical literature. The WHO database included 146 reports of AHA. The information component (IC) was significant for the association of AHA with all COVID-19 vaccines (IC025: 1.1) and with the vaccine product BNT162b2 (IC025: 1.6). After duplicate exclusion, 96 unique cases of AHA following COVID-19 vaccines have been reviewed. Median time to diagnosis was 18 days and 40% of cases documented the occurrence after the second dose. Overall, in 57% of the investigated cases, a preexisting condition predisposing to AHA was excluded. About 22% of cases occurred in subjects with age ≤65 years and there was no case associated with pregnancy. Mortality was 11%. Although we cannot exclude that the unexpected frequency of AHA diagnosis can be explained by a detection bias, the signal for COVID-19 vaccine-related AHA is robust and deserves further investigations.

Montelukast and Nightmares: Further Characterisation Using Data from VigiBase

INTRODUCTION

Montelukast is a medicine indicated for use in asthma. Psychiatric disorders including nightmares have not been described in clinical trials but during recent years have been included in the product information as having been reported post-marketing, without further description of the events. Previous descriptions in the scientific literature were based on limited numbers of reports or lacked detailed case information.

OBJECTIVE

The study aim was to further characterise post-marketing adverse drug reactions for nightmares, suspected to be induced by montelukast, to facilitate safer use of the medicine by providing additional information to patients and healthcare professionals.

METHODS

We clinically reviewed reports of nightmares with montelukast present in VigiBase, World Health Organization's global database of suspected adverse reactions to medicinal products, developed and maintained by the Uppsala Monitoring Centre, until 3 May, 2020.

RESULTS

There were 1118 reports of nightmares with montelukast in VigiBase, which provided valuable descriptions of the nightmares as well as information about the impact on the daily lives, with many cases describing a severe impact of the nightmares. About half of the reports were classified as serious. Two thirds concerned children, with the largest age group represented being children aged 5-10 years. In most cases, the nightmares disappeared upon discontinuation of the drug but for some patients it took a long time until the nightmares ceased.

CONCLUSIONS

The nature and potential severity of this adverse drug reaction, as described in these reports, present important knowledge for patients and healthcare providers that could help reduce drug-induced harm. This study highlights the value of post-marketing reports for further characterisation of known adverse drug reactions. The benefit-risk balance should be continuously monitored while patients are taking montelukast.

Analysis of drug-induced hand-foot syndrome using a spontaneous reporting system database

Purpose

The aim of our study was to assess the clinical features of hand-foot syndrome (HFS) associated with certain systemic chemotherapeutic drugs in a real-world setting using the Japanese Adverse Drug Event Report (JADER) database.

Methods

HFS was defined using the preferred terms from the Medical Dictionary for Regulatory Activities. We used several indices, such as the reporting odds ratios (RORs) at 95% confidence interval (CI), the time-to-onset profile of HFS, and cluster analysis.

Results

Of 646,779 reports (submission period: April 2004 to September 2020), 1814 reported HFS events. The RORs (95% CI) for axitinib, capecitabine, lapatinib, regorafenib, sorafenib, and sunitinib were 14.9 (11.1-20.1), 54.6 (49.2-60.6), 130.4 (110.7-153.6), 63.3 (55.2-72.6), 29.0 (25.8-32.7), and 13.9 (11.7-16.5), respectively. The analysis of time-to-onset profiles revealed that the median values (interquartile range: 25.0-75.0%) of drug-induced HFS caused by capecitabine, cisplatin, docetaxel, everolimus, regorafenib, sorafenib, and trastuzumab were 21.0 (13.0-42.0), 15.0 (10.0-82.0), 6.0 (3.0-25.0), 86.5 (67.0-90.5), 9.0 (6.0-14.0), 9.0 (6.0-14.0), and 70.0 (15.0-189.0) days, respectively. The number of clusters was set to 4. Among these, one cluster, which included capecitabine, regorafenib, and lapatinib, exhibited a higher reporting ratio and ROR of drug-induced HFS than other drugs.

Conclusions

The RORs and results of time-to-onset analysis obtained in this study indicated the potential risk of HFS associated with chemotherapeutic drugs. Our results suggest that health care professionals must be aware of the potential onset of drug-induced HFS with docetaxel, regorafenib, and sorafenib for at least 4 weeks; therefore, careful observation is recommended.

Plain Language Summary

Elucidation of the relationship between cancer drugs and risk of hand-foot syndrome: Purpose: Hand-foot syndrome (HFS) is an adverse effect of some cancer drugs, which is characterized by symptoms such as redness, swelling, blistering, and pain in the area of palms and soles. HFS reduces the quality of life of patients and can sometimes interfere with anticancer treatment plans. It is important to understand the clinical manifestations of HFS and gain knowledge that will allow for early intervention by clinicians.Methods: In this study, we used a large-scale side effect database of real-world cases for a comprehensive investigation of anticancer-drug-induced HFS. The database contained 646,779 adverse event reports from April 2004 to September 2020; among which, we identified 1814 HFS events. Using these data, we could obtain information on the relationship between 19 types of anticancer drugs and HFS, and the onset time of HFS and HFS prognosis related to each anticancer drug. Results: Our results suggest that clinicians should monitor the risk of HFS with docetaxel, regorafenib, and sorafenib for at least the first 4 weeks after drug administration. Conclusion: These findings are crucial for improving the management of the adverse effects caused by anticancer drugs.

Evaluation of pharmacovigilance systems for reporting medication errors in Africa and the role of patients using a mixed-methods approach

Background

Reviewing the epidemiological profile of medication errors (MEs) reported by African countries and the systems put in place to report such errors is crucial because reporting plays an important role in improving patient safety. The objectives of this study were to characterize the profile of spontaneously reported MEs submitted by African countries to VigiBase; the World Health Organization (WHO) global database of individual case safety reports, describe systems in place for reporting these errors, and explore the challenges and facilitators for spontaneous reporting and understand the potential role of patients.

Methods

In the present study, we used, a mixed-methods sequential explanatory design involving a quantitative review of ME reports over a 21-year period (1997–2018) and qualitative interviews with employees from African countries that are members of the WHO Program for International Drug Monitoring (WHO PIDM). Descriptive statistics were used to summarize key variables of interest.

Results

A total of 4,205 ME reports were submitted by African countries to VigiBase representing 0.4% of all reports in the database. Only 15 countries out of the 37 WHO PIDM members from Africa contributed ME to reports, with 99% (3,874) of them reports originating from Egypt, Morocco, and South Africa. The reasons given for low reporting of MEs were weak healthcare and pharmacovigilance systems, lack of staff capacity at the national centers, illiteracy, language difficulties, and socio-cultural and religious beliefs. Some facilitators suggested by the participants to promote reporting included proactive engagement of patients regarding issues relating to MEs, leveraging on increased technology, benchmarking and mentoring by more experienced national centers. Sixteen of the twenty countries interviewed had systems for reporting MEs integrated into adverse drug reaction reporting with minimal patient involvement in seven of these countries. Patients were not involved in directly reporting MEs in the remaining 13 countries.

Conclusions

MEs are rarely reported through pharmacovigilance systems in African countries with limited patient involvement. The systems are influenced by multifactorial issues some of which are not directly related to healthcare.

A Standardized Dataset of a Spontaneous Adverse Event Reporting System

One of the largest spontaneous adverse events reporting databases in the world is the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). Unfortunately, researchers face many obstacles in analyzing data from the FAERS database. One of the major obstacles is the unstructured entry of drug names into the FAERS, as reporters might use generic names or trade names with different naming structures from all over the world and, in some cases, with typographical errors. Moreover, report duplication is a known problem in spontaneous adverse event-reporting systems, including the FAERS database. Hence, thorough text processing for database entries, especially drug name entries, coupled with a practical case-deduplication logic, is a prerequisite to analyze the database, which is a time- and resource-consuming procedure. In this study, we provide a clean, deduplicated, and ready-to-import dataset into any relational database management software of the FAERS database up to September 2021. Drug names are standardized to the RxNorm vocabulary and normalized to the single active ingredient level. Moreover, a pre-calculated disproportionate analysis is provided, which includes the reporting odds ratio (ROR), proportional reporting ratio (PRR), Chi-squared analysis with Yates correction (x2), and information component (IC) for each drug-adverse event pair in the database.

Artificial Intelligence and Machine Learning for Safe Medicines

Authors' views on the role of artificial intelligence and machine learning in pharmacovigilance. (MP4  139807 kb).

Automated Drug Coding Using Artificial Intelligence: An Evaluation of WHODrug Koda on Adverse Event Reports

INTRODUCTION

Coding medicinal products described on adverse event (AE) reports to specific entries in standardised drug dictionaries, such as WHODrug Global, is a time-consuming step in case processing activities despite its potential for automation. Many organisations are already partially automating drug coding using text-processing methods and synonym lists, however addressing challenges such as misspellings, abbreviations or ambiguous trade names requires more advanced methods. WHODrug Koda is a drug coding engine using text-processing algorithms, built-in coding rules and machine learning to code drug verbatims to WHODrug Global.

OBJECTIVE

Our aim was to evaluate the drug coding performance of WHODrug Koda on AE reports from VigiBase, the World Health Organization's global database of individual case safety reports, in terms of level of automation and coding quality.

METHODS

Koda was evaluated on 4.8 million drug entries from VigiBase. Automation level was computed as the proportion of drug entries automatically coded by Koda and was compared to a simple case-insensitive text-matching algorithm. Coding quality was evaluated in terms of coding accuracy, by comparing Koda's prediction to the WHODrug entries found on the AE reports in VigiBase. To better understand the cases in which Koda's coding results did not match with the WHODrug entries in VigiBase, a manual assessment of 600 samples of disagreeing encodings was performed by two teams of expert drug coders.

RESULTS

Compared with a simple direct-match baseline, Koda can increase the automation level from 61% to 89%, while providing high coding quality with an accuracy of 97%.

CONCLUSIONS

Even though Koda was designed for use in clinical trials, Koda achieves automation level and coding quality for drug coding of AE reports comparable with the performance observed in a previous evaluation of Koda on clinical trial data. Koda can thus help organisations to automate their drug coding of AE reports to a large degree.

Black Swan Events and Intelligent Automation for Routine Safety Surveillance

Effective identification of previously implausible safety signals is a core component of successful pharmacovigilance. Timely, reliable, and efficient data ingestion and related processing are critical to this. The term 'black swan events' was coined by Taleb to describe events with three attributes: unpredictability, severe and widespread consequences, and retrospective bias. These rare events are not well understood at their emergence but are often rationalized in retrospect as predictable. Pharmacovigilance strives to rapidly respond to potential black swan events associated with medicine or vaccine use. Machine learning (ML) is increasingly being explored in data ingestion tasks. In contrast to rule-based automation approaches, ML can use historical data (i.e., 'training data') to effectively predict emerging data patterns and support effective data intake, processing, and organisation. At first sight, this reliance on previous data might be considered a limitation when building ML models for effective data ingestion in systems that look to focus on the identification of potential black swan events. We argue that, first, some apparent black swan events-although unexpected medically-will exhibit data attributes similar to those of other safety data and not prove algorithmically unpredictable, and, second, standard and emerging ML approaches can still be robust to such data outliers with proper awareness and consideration in ML system design and with the incorporation of specific mitigatory and support strategies. We argue that effective approaches to managing data on potential black swan events are essential for trust and outline several strategies to address data on potential black swan events during data ingestion.

Consensus clustering for case series identification and adverse event profiles in pharmacovigilance

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.

A New Tool for an Awareness Plan Concerning Critical Issues, Needs and Attitudes of Citizens on the Use of Medicines

This article describes a pilot study to test the adequacy of a newly developed tool for an awareness plan on the importance of properly using pharmaceuticals. The new tool consists of face-to-face interviews with adult citizens on their approach to the use of medicines and of the following data analysis with a dedicated software application. The pilot study was carried out in a sample area of Sardinia, in Italy. The data from the interviews collected anonymously and analysed in aggregate actually emphasised the critical issues and needs in the use of pharmaceuticals in the sample area involved, also encouraging communication among different actors. The pilot study revealed that the designed tool could represent a novel strategy to stimulate interchanges of information on the proper use of pharmaceuticals with a potential impact on people's health.

Safety of medicines and vaccines - building next generation capability

The systematic safety surveillance of real-world use of medicinal products and related activities (pharmacovigilance) started in earnest as a scientific field only in the 1960s. While developments have occurred over the past 50 years, adding to its complexity and sophistication, the extent to which some of these advances have positively impacted the capability for ensuring patient safety is questionable. We review how the conduct of safety surveillance has changed, highlight recent scientific advances, and argue how they need to be harnessed to enhance pharmacovigilance in the future. Specifically, we describe five changes that we believe should and will need to happen globally in the coming years: (i) better, more diverse data used for safety; (ii) the switch from manual activities to automation; (iii) removal of limited value, extraneous transactional activities and replacement with sharpened focus on scientific efforts to improve patient safety; (iv) patient-involved and focussed safety; and (v) personalised safety.

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.

Electronic supplementary material

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

Adverse reactions with levamisole vary according to its indications and misuse: a systematic pharmacovigilance study

AIM

Levamisole was initially prescribed for the treatment of intestinal worms. Because of immunomodulatory properties, levamisole has been used in inflammatory pathologies and in cancers in association with 5-fluorouracil. Levamisole is misused as a cocaine adulterant. Post-marketing reports have implicated levamisole in the occurrence of adverse drug reactions (ADRs) and its use is now limited in Europe and North America. In contrast, all other parts of the World continue to use single-dose as an anthelmintic. The aim of this study was to identify ADRs reported after levamisole exposure in VigiBase, the WHO's pharmacovigilance database, and analyze their frequency compared to other drugs and according to levamisole type of use.

METHODS

All levamisole-related ADRs were extracted from VigiBase®. Disproportionality analyses were conducted to investigate psychiatric, hepatobiliary, renal, vascular, nervous, blood, skin, cardiac, musculoskeletal and general ADRs associated with levamisole and other drugs exposure. In secondary analyses, we compared the frequency of ADRs between levamisole and mebendazole and between levamisole type of use.

RESULTS

Among the 1763 levamisole-related ADRs identified, psychiatric disorders (Reporting Odds-Ratio with 95% confidence intervals: 1.4 [1.2-2.6]), hepatobiliary disorders (2.4 [1.9-4.3]), vasculitis (6.5 [4.1-10.6]), encephalopathy (22.5 [17.4-39.9]), neuropathy (4.3 [2.9-7.1]), hematological disorders, mild rashes and musculoskeletal disorders were more frequently reported with levamisole than with other drug. The majority of levamisole-related ADRs occurred when the drug was administrated for a non-anti-infectious indication.

CONCLUSION

The great majority of the levamisole-related ADRs concerned its immunomodulatory indication and multiple doses regimen. Our results suggest that single-dose treatments for anthelmintic action have a good safety profile.

A Feasibility Study of Drug-Drug Interaction Signal Detection in Regular Pharmacovigilance

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.

Disproportionality Analysis for Pharmacovigilance Signal Detection in Small Databases or Subsets: Recommendations for Limiting False-Positive Associations

Introduction

Uncovering safety signals through the collection and assessment of individual case reports remains a core pharmacovigilance activity. Despite the widespread use of disproportionality analysis in signal detection, recommendations are lacking on the minimum size of databases or subsets of databases required to yield robust results.

Objective

This study aims to investigate the relationship between database size and robustness of disproportionality analysis, with regards to limiting spurious associations.

Methods

Three types of subsets were created from the global database VigiBase: random subsets (500 replicates each of 11 fixed subset sizes between 250 and 100,000 reports), country-specific subsets (all 131 countries available in the original VigiBase extract) and subsets based on the Anatomical Therapeutic Chemical classification. For each subset, a spuriousness rate was computed as the ratio between the number of drug–event combinations highlighted by disproportionality analysis in a permuted version of the subset and the corresponding number in the original subset. In the permuted data, all true reporting associations between drugs and adverse events were broken. Subsets with fewer than five original associations were excluded. Additionally, the set of disproportionately over-reported drug–event combinations in three specific countries at three different time points were clinically assessed for labelledness. These time points corresponded to database sizes of less than 10,000, 5000 and 1000 reports, respectively. All disproportionality analysis was based on the Information Component (IC), implemented as IC₀₂₅ > 0.

Results

Spuriousness rates were below 0.15 for all 110 included countries regardless of subset size, with only seven countries (6%) exceeding the empirical threshold of 0.10 observed for large subsets. All 21 excluded countries had < 500 reports. For random subsets containing 3000–5000 or more reports, the higher end of observed spuriousness rates was close to 0.10. In the clinical assessment, the proportion of labelled or otherwise known drug–event combinations was very high (87–100%) across all countries and time points studied.

Conclusions

To mitigate the risk of highlighting spurious associations with disproportionality analysis, a minimum size of 500 reports is recommended for national databases. For databases or subsets that are not country-specific, our recommendation is 5000 reports. This study does not consider sensitivity, which is expected to be poor in smaller databases.

The Use of Subgroup Disproportionality Analyses to Explore the Sensitivity of a Global Database of Individual Case Safety Reports to Known Pharmacogenomic Risk Variants Common in Japan

INTRODUCTION

Genetic variations of enzymes that affect the pharmacokinetics and hence effects of medications differ between ethnicities, resulting in variation in the risk of adverse drug reactions (ADR) between different populations. Previous work has demonstrated that risk-group considerations can be incorporated into approaches of statistical signal detection. It is unknown whether databases of individual case safety reports (ICSRs) are sensitive to pharmacogenomic differences between populations.

OBJECTIVE

The aim of this study was to explore the sensitivity of a global database of ICSRs to known pharmacogenomic risk variants common in Japan.

METHODS

The data source was VigiBase, the global database of ICSRs, including all reports entered in the version frozen on 5 January 2020. Subgroup disproportionality analysis was used to compare ICSRs of two subgroups, Japan and rest of world (RoW). Reports for UGT1A1-metabolized irinotecan and the CYP2C19-metabolized drugs voriconazole, escitalopram and clopidogrel were selected for comparison between the subgroups based upon known genetic polymorphisms with high prevalence in Japan. Contrast between the subgroups was quantified by IC delta [Formula: see text]), a robust shrinkage observed-to-expected (OE) ratio on a log scale. Harmonic mean p values (HMP) were calculated for each drug to evaluate whether a list of pre-specified ADRs were collectively significantly over- (or under-)reported as hypothesized. Daily drug dosages were calculated for ICSRs with sufficient information, and dose distributions were compared between Japan and RoW and related to differences in regionally approved doses.

RESULTS

The predictions of over-reporting patterns for specific ADRs were observed and confirmed in bootstrap HMP analyses (p = 0.004 for irinotecan and p < 0.001 for each of voriconazole, escitalopram and clopidogrel) and compared with similar drugs with different metabolic pathways. The impact of proactive regulatory action, such as recommended dosing and therapeutic drug monitoring (TDM), was also observable within the global database. For irinotecan and escitalopram, there was evidence of use of lower dosages as recommended in the Japanese labels; for voriconazole, there was evidence of use of TDM with an over-reporting of terms related to drug level measurements and an under-reporting of liver toxicity.

CONCLUSIONS

Pharmaco-ethnic vulnerabilities caused by pharmacogenomic differences between populations may contribute to differences in ADR reporting between countries in a global database of ICSRs. Regional analyses within a global database can inform on the effectiveness of local risk minimization measures and should be leveraged to catalyse the conversion of real-world usage into safer use of drugs in ethnically tailored ways.

Serious adverse reactions associated with ivermectin: A systematic pharmacovigilance study in sub-Saharan Africa and in the rest of the World

BACKGROUND

Ivermectin is known to cause severe encephalopathies in subjects infected with loiasis, an endemic parasite in Sub-Saharan Africa (SSA). In addition, case reports have described ivermectin-related serious adverse drug reactions (sADRs) such as toxidermias, hepatic and renal disorders. The aim of this study was to identify suspected sADRs reported after ivermectin administration in VigiBase, the World Health Organization's global individual case safety reports database and analyze their frequency relative to the frequency of these events after other antinematodal drugs reported in SSA and other areas of the world (ROW).

METHODS

All antinematodal-related sADRs were extracted from VigiBase. Disproportionality analyses were conducted to investigate nervous, cutaneous, psychiatric, respiratory, renal, hepatic and cardiac suspected sADRs reported after ivermectin and benzimidazole drug administration across the world, in SSA and RoW.

PRINCIPAL FINDINGS

2041 post-ivermectin or post-benzimidazole suspected sADRs were identified including 667 after ivermectin exposure (208 in SSA and 459 in the RoW). We found an increased reporting for toxidermias, encephalopathies, confusional disorders after ivermectin compared to benzimidazole drug administration. Encephalopathies were not only reported from SSA but also from the RoW (adjusted reporting odds ratios [aROR] 6.30, 95% confidence interval: 2.68-14.8), highlighting the fact these types of sADR occur outside loiasis endemic regions.

CONCLUSION

We described for the first time suspected sADRs associated with ivermectin exposure according to geographical origin. While our results do not put in question ivermectin's excellent safety profile, they show that as for all drugs, appropriate pharmacovigilance for adverse reactions is indicated.

A safety signal of somnambulism with the use of antipsychotics and lithium: A pharmacovigilance disproportionality analysis

AIMS

Antipsychotics and lithium are widely used in psychiatry, particularly in schizophrenia and bipolar disorders. Recently, some cases of somnambulism or sleep-related eating disorder (SRED) have been reported in patients treated with these drugs. This study investigated the risk of reporting somnambulism or SRED associated with the use of antipsychotics and lithium.

METHODS

The World Health Organization pharmacovigilance database (VigiBase), comprising >18 million adverse events, was queried. All somnambulism or SRED reports related to antipsychotics or lithium were identified. The association between antipsychotics or lithium and somnambulism or SRED was computed using the proportional reporting ratio (PRR) and information component.

RESULTS

Among the 5784 cases reporting somnambulism or SRED, 508 suspected at least 1 antipsychotic or lithium. Most patients were aged 18-64 years (62.0%), and 37.0% were men. In most cases (77.6%), antipsychotic or lithium were the only drug class involved, and 53.3% of cases suspected quetiapine. Somnambulism was reported in 88.6% of cases and SRED in 18.1%. A significant association was found for second-generation antipsychotics (PRR 3.44, 95% confidence interval 3.13) and lithium (PRR 2.03, [1.22; 3.37]), but not for first-generation antipsychotics (PRR 0.99, [0.68; 1.44]).

CONCLUSIONS

We found a significant signal of somnambulism or SRED related to second-generation antipsychotics and lithium. While case reports mentioned mostly quetiapine and olanzapine, almost all second-generation antipsychotics were associated with somnambulism or SRED.

Artificial Intelligence, Real-World Automation and the Safety of Medicines

Despite huge technological advances in the capabilities to capture, store, link and analyse data electronically, there has been some but limited impact on routine pharmacovigilance. We discuss emerging research in the use of artificial intelligence, machine learning and automation across the pharmacovigilance lifecycle including pre-licensure. Reasons are provided on why adoption is challenging and we also provide a perspective on changes needed to accelerate adoption, and thereby improve patient safety. Last, we make clear that while technologies could be superimposed on existing pharmacovigilance processes for incremental improvements, these great societal advances in data and technology also provide us with a timely opportunity to reconsider everything we do in pharmacovigilance operations to maximise the benefit of these advances.

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.

Electronic supplementary material

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

Recommendations for the Use of Social Media in Pharmacovigilance: Lessons from IMI WEB-RADR

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.

Reported adverse drug reactions in women and men: Aggregated evidence from globally collected individual case reports during half a century

BACKGROUND

Adverse drug reactions (ADRs) are an important cause of morbidity and mortality. Reports on differences in reporting patterns between women and men exist nationally. The goal of the present study was to assess the global evidence on spontaneous post-marketing ADR reporting differences between reports for women and men.

METHODS

We analysed data collected within VigiBase, the WHO global database of individual case safety reports, between 1967-2 January 2018. VigiBase contains more than 18 million reports from the 131 member countries of the WHO Programme for International Drug Monitoring.

FINDINGS

Of the reports with information on sex, 9,056,566 (60.1%) concerned female and 6,012,804 (39.9%) male children and adults. More female ADR reports were submitted in all regions of the world and by all types of reporters. A higher proportion of female reports was seen in all age groups from the age group 12-17 years and older. The largest difference was observed in the age group of 18-44 years and could not be explained by hormonal contraceptive use. The proportion of serious and fatal reports was higher for male reports.

INTERPRETATION

Global post marketing surveillance data on spontaneous reports indicate that women, from puberty and onwards and especially in their reproductive years, report more ADRs than men. However, there is a higher proportion of serious and fatal ADRs among male reports. Our results suggest important underlying sex-related differences in ADRs. These findings highlight the importance of considering sex throughout the entire life-cycle of drug development and surveillance and understanding the underlying reasons for reporting ADRs.

Analysis of drug-induced hearing loss by using a spontaneous reporting system database

Many drugs can cause hearing loss, leading to sensorineural deafness. The aim of this study was to evaluate the risk of drug-induced hearing loss (DIHL) by using the Japanese Adverse Drug Event Report (JADER) database and to obtain profiles of DIHL onset in clinical settings. We relied on the Medical Dictionary for Regulatory Activities preferred terms and standardized queries, and calculated the reporting odds ratios (RORs). Furthermore, we applied multivariate logistic regression analysis, association rule mining, and time-to-onset analysis using Weibull proportional hazard models. Of 534688 reports recorded in the JADER database from April 2004 to June 2018, adverse event signals were detected for platinum compounds, sulfonamides (plain) (loop diuretics), interferons, ribavirin, other aminoglycosides, papillomavirus vaccines, drugs used in erectile dysfunction, vancomycin, erythromycin, and pancuronium by determining RORs. The RORs of other aminoglycosides, other quaternary ammonium compounds, drugs used in erectile dysfunction, and sulfonamides (plain) were 29.4 (22.4–38.6), 18.5 (11.2–30.6), 15.4 (10.6–22.5), and 12.6 (10.0–16.0), respectively. High lift score was observed for patients with congenital diaphragmatic hernia treated with pancuronium using association rule mining. The median durations (interquartile range) for DIHL due to platinum compounds, sulfonamides (plain), interferons, antivirals for treatment of hepatitis C virus (HCV) infections, other aminoglycosides, carboxamide derivatives, macrolides, and pneumococcal vaccines were 25.5 (7.5–111.3), 80.5 (4.5–143.0), 64.0 (14.0–132.0), 53.0 (9.0–121.0), 11.0 (3.0–26.8), 1.5 (0.3–11.5), 3.5 (1.3–6.8), and 2.0 (1.0–4.5), respectively. Our results demonstrated potential risks associated with several drugs based on their RORs. We recommend to closely monitor patients treated with aminoglycosides for DIHL for at least two weeks. Moreover, individuals receiving platinum compounds, sulfonamides (plain), interferons, and antivirals for HCV infection therapy should be carefully observed for DIHL for at least several months.

Characteristics, Quality and Contribution to Signal Detection of Spontaneous Reports of Adverse Drug Reactions Via the WEB-RADR Mobile Application: A Descriptive Cross-Sectional Study

Introduction

Spontaneous reporting of suspected adverse drug reactions is key for efficient post-marketing safety surveillance. To increase usability and accessibility of reporting tools, the Web-Recognising Adverse Drug Reactions (WEB-RADR) consortium developed a smartphone application (app) based on a simplified reporting form.

Objective

The objective of this study was to evaluate the characteristics, quality and contribution to signals of reports submitted via the WEB-RADR app.

Methods

The app was launched in the UK, the Netherlands and Croatia between July 2015 and May 2016. Spontaneous reports submitted until September 2016 with a single reporter were included. For each country, app reports and reports received through conventional means in the same time period were compared to identify characteristic features. A random subset of reports was assessed for clinical quality and completeness. The contribution to signal detection was assessed by a descriptive analysis.

Results

Higher proportions of app reports were submitted by patients in the UK (28 vs. 18%) and Croatia (32 vs. 7%); both p < 0.01. In the Netherlands, the difference was small (60 vs. 57%; p = 0.5). The proportion of female patients and the median patient ages in app reports submitted by patients were similar to the reference. The proportion of reports of at least moderate quality was high in both samples (app: 78–85%, reference: 78–98%), for all countries. App reports contributed to detecting eight potential safety signals at the national level, four of which were eventually signalled.

Conclusion

The WEB-RADR app offers a new route of spontaneous reporting that shows promise in attracting reports from patients and that could become an important tool in the future. Patient demographics are similar to conventional routes, report quality is sufficient despite a simplified reporting form, and app reports show potential in contributing to signal detection.

Electronic supplementary material

The online version of this article (10.1007/s40264-018-0679-6) contains supplementary material, which is available to authorized users.

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.

Safety Concerns Reported by Patients Identified in a Collaborative Signal Detection Workshop using VigiBase: Results and Reflections from Lareb and Uppsala Monitoring Centre

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.

Identification of adverse drug-drug interactions through causal association rule discovery from spontaneous adverse event reports

OBJECTIVE

Drug-drug interaction (DDI) is of serious concern, causing over 30% of all adverse drug reactions and resulting in significant morbidity and mortality. Early discovery of adverse DDI is critical to prevent patient harm. Spontaneous reporting systems have been a major resource for drug safety surveillance that routinely collects adverse event reports from patients and healthcare professionals. In this study, we present a novel approach to discover DDIs from the Food and Drug Administration's adverse event reporting system.

METHODS

Data-driven discovery of DDI is an extremely challenging task because higher-order associations require analysis of all combinations of drugs and adverse events and accurate estimate of the relationships between drug combinations and adverse event require cause-and-effect inference. To efficiently identify causal relationships, we introduce the causal concept into association rule mining by developing a method called Causal Association Rule Discovery (CARD). The properties of V-structures in Bayesian Networks are utilized in the search for causal associations. To demonstrate feasibility, CARD is compared to the traditional association rule mining (AR) method in DDI identification.

RESULTS

Based on physician evaluation of 100 randomly selected higher-order associations generated by CARD and AR, CARD is demonstrated to be more accurate in identifying known drug interactions compared to AR, 20% vs. 10% respectively. Moreover, CARD yielded a lower number of drug combinations that are unknown to interact, i.e., 50% for CARD and 79% for AR.

CONCLUSION

Evaluation analysis demonstrated that CARD is more likely to identify true causal drug variables and associations to adverse event.

Retrospective review of paediatric case reports of Stevens-Johnson syndrome and toxic epidermal necrolysis with lamotrigine from an international pharmacovigilance database

OBJECTIVES

This study aims to characterise paediatric reports with lamotrigine (LTG) and Stevens-Johnson syndrome or toxic epidermal necrolysis (SJS/TEN), and to explore whether potential risk factors can be identified.

DESIGN

This is a retrospective review of suspected adverse drug reaction (ADR) reports. Reported time from LTG start to SJS/TEN onset, indication for use and dose was explored. To identify potential risk groups, report features (eg, ages, patient sex, co-reported drugs) for LTG and SJS/TEN were contrasted with two reference groups in the same database, using shrinkage logOR.

SETTING

Reports were retrieved from VigiBase, the WHO global database of individual case safety reports, in January 2015.

PATIENTS

Data for patients aged ≤17 years old were extracted.

RESULTS

There were 486 reports of SJS/TEN in LTG-treated paediatric patients. Ninety-seven per cent of the cases with complete information on time to onset of SJS/TEN occurred within 8 weeks of initiation of LTG therapy. The median time to onset was 15 days (IQR: 10-22 days). The proportion of SJS/TEN with LTG and valproic acid (VPA) co-reporting was significantly more than non-cutaneous ADRs (43% vs 19%, (logOR: 1.60 (99% CI: 1.33 to 1.84)).

CONCLUSIONS

The results suggest that VPA co-medication with LTG therapy is a risk factor for SJS/TEN in the paediatric population. Although this relationship has been identified from individual case reports, this is the first supportive study from a large compilation of cases. SJS/TEN risk is highest in first 8 weeks of treatment with LTG in children and clinicians should be aware of this risk during this period.

Good Signal Detection Practices: Evidence from IMI PROTECT

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.

Data mining spontaneous adverse drug event reports for safety signals in Singapore - a comparison of three different disproportionality measures

OBJECTIVES

Quantitative data mining methods can be used to identify potential signals of unexpected relationships between drug and adverse event (AE). This study aims to compare and explore the use of three data mining methods in our small spontaneous AE database.

METHODS

We consider reporting odds ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN) and Gamma Poisson Shrinker (GPS) assuming two different sets of criteria: (1) ROR-1.96SE>1, IC-1.96SD>0, EB05>1 (2) ROR-1.96SE>2, IC-1.96SD>1, EB05 >2. Count of drug-AE pairs ≥3 was considered for ROR and GPS.

RESULTS

The Health Sciences Authority, Singapore received 151,180 AE reports between 1993 and 2013. ROR, BCPNN and GPS identified 2,835, 2,311 and 2,374 significant drug-AE pairs using Criterion 1, and 1,899, 1,101 and 1,358 respectively using Criterion 2. The performance of the three methods with respect to specificity, positive predictive value and negative predictive value were similar, although ROR yielded a higher sensitivity and larger area under the receiver operating characteristic curve. ROR and GPS picked up some potential signals which BCPNN missed.

CONCLUSIONS

The defined threshold used for ROR (Criterion 1) is a useful screening tool for our small database. It may be used in conjunction with GPS to avoid missed signals.

Postmarketing Safety Study Tool: A Web Based, Dynamic, and Interoperable System for Postmarketing Drug Surveillance Studies

Postmarketing drug surveillance is a crucial aspect of the clinical research activities in pharmacovigilance and pharmacoepidemiology. Successful utilization of available Electronic Health Record (EHR) data can complement and strengthen postmarketing safety studies. In terms of the secondary use of EHRs, access and analysis of patient data across different domains are a critical factor; we address this data interoperability problem between EHR systems and clinical research systems in this paper. We demonstrate that this problem can be solved in an upper level with the use of common data elements in a standardized fashion so that clinical researchers can work with different EHR systems independently of the underlying information model. Postmarketing Safety Study Tool lets the clinical researchers extract data from different EHR systems by designing data collection set schemas through common data elements. The tool interacts with a semantic metadata registry through IHE data element exchange profile. Postmarketing Safety Study Tool and its supporting components have been implemented and deployed on the central data warehouse of the Lombardy region, Italy, which contains anonymized records of about 16 million patients with over 10-year longitudinal data on average. Clinical researchers in Roche validate the tool with real life use cases.

Pediatric Drug Safety Surveillance in FDA-AERS: A Description of Adverse Events from GRiP Project

Individual case safety reports (ICSRs) are a cornerstone in drug safety surveillance. The knowledge on using these data specifically for children is limited. We studied characteristics of pediatric ICSRs reported to the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). Public available ICSRs reported in children (0–18 years) to FAERS were downloaded from the FDA-website for the period Jan 2004-Dec 2011. Characteristics of these ICSRs, including the reported drugs and events, were described and stratified by age-groups. We included 106,122 pediatric ICSRs (55% boys and 58% from United States) with a median of 1 drug [range 1–3] and 1 event [1–2] per ICSR. Mean age was 9.1 years. 90% was submitted through expedited (15-days) (65%) or periodic reporting (25%) and 10% by non-manufacturers. The proportion and type of pediatric ICSRs reported were relatively stable over time. Most commonly reported drug classes by decreasing frequency were ‘nervous system drugs’ (58%), ‘antineoplastics’ (32%) and ‘anti-infectives’ (25%). Most commonly reported system organ classes were ‘general’ (13%), ‘nervous system’ (12%) and ‘psychiatric’ (11%) disorders. Duration of use could be calculated for 19.7% of the reported drugs, of which 14.5% concerned drugs being used long-term (>6 months). Knowledge on the distribution of the drug classes and events within FAERS is a key first step in developing pediatric specific methods for drug safety surveillance. Because of several differences in terms of drugs and events among age-categories, drug safety signal detection analysis in children needs to be stratified by each age group.

Improved statistical signal detection in pharmacovigilance by combining multiple strength-of-evidence aspects in vigiRank

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.

Adverse drug reaction reports for cardiometabolic drugs from sub-Saharan Africa: a study in VigiBase

OBJECTIVE

Identifying key features in individual case safety reports (ICSR) of suspected adverse drug reactions (ADRs) with cardiometabolic drugs from sub-Saharan Africa (SSA) compared with reports from the rest of the world (RoW).

METHODS

Reports on suspected ADRs of cardiometabolic drugs (ATC: A10[antidiabetic], B01[antithrombotics] and C[cardiovascular]) were extracted from WHO Global database, VigiBase(®) (1992-2013). We used vigiPoint, a logarithmic odds ratios (log2 OR)-based method to study disproportional reporting between SSA and RoW. Case-defining features were considered relevant if the lower limit of the 99% CI > 0.5.

RESULTS

In SSA, 3773 (9%) of reported ADRs were for cardiometabolic drugs, in RoW for 18%. Of these, 79% originated from South Africa and 81% were received after 2007. Most reports were for drugs acting on the renin-angiotensin system (36% SSA & 14% RoW). Compared with RoW, reports were more often sent for patients 18-44 years old (log2 OR 0.95 [99 CI 0.80; 1.09]) or with non-fatal outcome (log2 OR 1.16 [99 CI 1.10; 1.22]). Eight ADRs (cough, angioedema, lip swelling, face oedema, swollen tongue, throat irritation, drug ineffective and blood glucose abnormal) and seven drugs (enalapril, rosuvastatin, perindopril, vildagliptin, insulin glulisine, nifedipine and insulin lispro) were disproportionally more reported in SSA than in the RoW.

CONCLUSIONS

'In recent years, the number of adverse drug reactions (ADRs) reported in Sub-Saharan Africa (SSA) has sharply increased. The data showed the well-known population-based differential ADR profile of ACE inhibitors in the SSA population.'