Electronic healthcare databases for active drug safety surveillance: is there enough leverage?

Active surveillance pharmacovigilance for Clostridioides difficile infection and gastrointestinal bleeding: an analytic framework based on case-control studies

BACKGROUND

Active surveillance pharmacovigilance is an emerging approach to identify medications with unanticipated effects. We previously developed a framework called pharmacopeia-wide association studies (PharmWAS) that limits false positive medication associations through high-dimensional confounding adjustment and set enrichment. We aimed to assess the transportability and generalizability of the PharmWAS framework by using medical claims data to reproduce known medication associations with Clostridioides difficile infection (CDI) or gastrointestinal bleeding (GIB).

METHODS

We conducted case-control studies using Optum's de-identified Clinformatics Data Mart Database of individuals enrolled in large commercial and Medicare Advantage health plans in the United States. Individuals with CDI (from 2010 to 2015) or GIB (from 2010 to 2021) were matched to controls by age and sex. We identified all medications utilized prior to diagnosis and analysed the association of each with CDI or GIB using conditional logistic regression adjusted for risk factors for the outcome and a high-dimensional propensity score.

FINDINGS

For the CDI study, we identified 55,137 cases, 220,543 controls, and 290 medications to analyse. Antibiotics with Gram-negative spectrum, including ciprofloxacin (aOR 2.83), ceftriaxone (aOR 2.65), and levofloxacin (aOR 1.60), were strongly associated. For the GIB study, we identified 450,315 cases, 1,801,260 controls, and 354 medications to analyse. Antiplatelets, anticoagulants, and non-steroidal anti-inflammatory drugs, including ticagrelor (aOR 2.81), naproxen (aOR 1.87), and rivaroxaban (aOR 1.31), were strongly associated.

INTERPRETATION

These studies demonstrate the generalizability and transportability of the PharmWAS pharmacovigilance framework. With additional validation, PharmWAS could complement traditional passive surveillance systems to identify medications that unexpectedly provoke or prevent high-impact conditions.

FUNDING

U.S. National Institute of Diabetes and Digestive and Kidney Diseases.

OHDSI Standardized Vocabularies-a large-scale centralized reference ontology for international data harmonization

IMPORTANCE

The Observational Health Data Sciences and Informatics (OHDSI) is the largest distributed data network in the world encompassing more than 331 data sources with 2.1 billion patient records across 34 countries. It enables large-scale observational research through standardizing the data into a common data model (CDM) (Observational Medical Outcomes Partnership [OMOP] CDM) and requires a comprehensive, efficient, and reliable ontology system to support data harmonization.

MATERIALS AND METHODS

We created the OHDSI Standardized Vocabularies-a common reference ontology mandatory to all data sites in the network. It comprises imported and de novo-generated ontologies containing concepts and relationships between them, and the praxis of converting the source data to the OMOP CDM based on these. It enables harmonization through assigned domains according to clinical categories, comprehensive coverage of entities within each domain, support for commonly used international coding schemes, and standardization of semantically equivalent concepts.

RESULTS

The OHDSI Standardized Vocabularies comprise over 10 million concepts from 136 vocabularies. They are used by hundreds of groups and several large data networks. More than 8600 users have performed 50 000 downloads of the system. This open-source resource has proven to address an impediment of large-scale observational research-the dependence on the context of source data representation. With that, it has enabled efficient phenotyping, covariate construction, patient-level prediction, population-level estimation, and standard reporting.

DISCUSSION AND CONCLUSION

OHDSI has made available a comprehensive, open vocabulary system that is unmatched in its ability to support global observational research. We encourage researchers to exploit it and contribute their use cases to this dynamic resource.

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.

Monitoring the appropriate prescription of low molecular weight heparins and Fondaparinux through administrative data. A retrospective observational study in the Tuscany region

INTRODUCTION

Low Molecular Weight Heparins (LMWHs) and Fondaparinux have been widely used as anticoagulants. Mass prescription may lead to prescriptive inappropriateness, which causes Heparin-induced thrombocytopenia and other side effects.

OBJECTIVES

The study investigates the appropriate prescription of LMWHs and Fondaparinux in Tuscany. We aim to validate the crude measure of prescription appropriateness of the Key Performance Indicator (KPI) "Patients treated with LMWHs and Fondaparinux every hundred residents in Tuscany" as a proxy for monitoring prescription appropriateness.

METHODS

To compare a crude KPI based only on drug consumption with a refined KPI based on exclusions listed in the clinical guidelines, a retrospective observational cohort study was carried out, using the RECORD guidelines for the year 2019. The refined indicator is computed via record linkage of different datasets regarding (a) pharmaceutical services; (b) hospital discharge records; (c) outpatient services; and (d) birth certificates. We apply exclusion criteria to identify the cohort of patients. Values of the KPI are compared, by ranking, with those obtained from its refined version. A Spearman test was performed to validate the use of the crude KPI as a proxy.

RESULTS

208,717 LMWH and Fondaparinux users are identified, of which 103,299 fall within the study's inclusion criteria. 16,817 (16%) of LMWHs and Fondaparinux users are classified as high consumption. The refined version of the KPI produces the same ranking results in terms of local health districts (rho = 0.98 p<0.01).

CONCLUSIONS

Although the crude KPI is less refined and detailed than the adjusted indicator computed by our study, it has proven capable to provide an accurate snapshot of the use of these drugs across the region. This analysis is useful to enable regional and local managers to run rapid and simple indicators to monitor the appropriateness of LMWHs and Fondaparinux. This analysis should be reviewed periodically to confirm its accuracy.

Methods for drug safety signal detection using routinely collected observational electronic health care data: A systematic review

PURPOSE

Signal detection is a crucial step in the discovery of post-marketing adverse drug reactions. There is a growing interest in using routinely collected data to complement established spontaneous report analyses. This work aims to systematically review the methods for drug safety signal detection using routinely collected healthcare data and their performance, both in general and for specific types of drugs and outcomes.

METHODS

We conducted a systematic review following the PRISMA guidelines, and registered a protocol in PROSPERO. MEDLINE, EMBASE, PubMed, Web of Science, Scopus, and the Cochrane Library were searched until July 13, 2021.

RESULTS

The review included 101 articles, among which there were 39 methodological works, 25 performance assessment papers, and 24 observational studies. Methods included adaptations from those used with spontaneous reports, traditional epidemiological designs, methods specific to signal detection with real-world data. More recently, implementations of machine learning have been studied in the literature. Twenty-five studies evaluated method performances, 16 of them using the area under the curve (AUC) for a range of positive and negative controls as their main measure. Despite the likelihood that performance measurement could vary by drug-event pair, only 10 studies reported performance stratified by drugs and outcomes, in a heterogeneous manner. The replicability of the performance assessment results was limited due to lack of transparency in reporting and the lack of a gold standard reference set.

CONCLUSIONS

A variety of methods have been described in the literature for signal detection with routinely collected data. No method showed superior performance in all papers and across all drugs and outcomes, performance assessment and reporting were heterogeneous. However, there is limited evidence that self-controlled designs, high dimensional propensity scores, and machine learning can achieve higher performances than other methods.

Safety surveillance of varicella vaccine using tree-temporal scan analysis

IMPORTANCE

Passive surveillance systems are susceptible to the under-reporting of adverse events (AE) and a lack of information pertaining to vaccinated populations. Conventional active surveillance focuses on predefined AEs. Advanced data mining tools could be used to identify unusual clusters of potential AEs after vaccination.

OBJECTIVE

To assess the feasibility of a novel tree-based statistical approach to the identification of AE clustering following the implementation of a varicella vaccination program among one-year-olds.

SETTING AND PARTICIPANTS

This nationwide safety surveillance was based on data from the Taiwan National Health Insurance database and National Immunization Information System for the period 2004 through 2014. The study population was children aged 12-35 months who received the varicella vaccine.

EXPOSURE

First-dose varicella vaccine.

OUTCOMES AND MEASURES

All incident ICD-9-CM diagnoses (emergency or inpatient departments) occurring 1-56 days after the varicella vaccination were classified within a hierarchical system of diagnosis categories using Multi-Level Clinical Classifications Software. A self-controlled tree-temporal data mining tool was then used to explore the incidence of AE clustering with a variety of potential risk intervals. The comparison interval consisted of days in the 56-day follow-up period that fell outside the risk interval.

RESULTS

Among 1,194,189 varicella vaccinees with no other same-day vaccinations, nine diagnoses with clustering features were categorized into four safety signals: fever on days 1-6 (attributable risk [AR] 38.5 per 100,000, p < 0.001), gastritis and duodenitis on days 1-2 (AR 5.9 per 100,000, p < 0.001), acute upper respiratory infection on days 1-5 (AR 11.0 per 100,000, p = 0.006), and varicella infection on days 1-9 (AR 2.7 per 100,000, p < 0.001). These safety profiles and their corresponding risk intervals have been identified in previous safety surveillance studies.

CONCLUSIONS

Unexpected clusters of AEs were not detected after the mass administration of childhood varicella vaccines in Taiwan. The tree-temporal statistical method is a feasible approach to the safety surveillance of vaccines in populations of young children.

Large-Scale Postmarketing Surveillance of Biological Drugs for Immune-Mediated Inflammatory Diseases Through an Italian Distributed Multi-Database Healthcare Network: The VALORE Project

BACKGROUND

Biological drugs have improved the management of immune-mediated inflammatory diseases (IMIDs) despite being associated with important safety issues such as immunogenicity, infections, and malignancies in real-world settings.

OBJECTIVE

The aim of this study was to explore the potential of a large Italian multi-database distributed network for use in the postmarketing surveillance of biological drugs, including biosimilars, in patients with IMID.

METHODS

A retrospective cohort study was conducted using 13 Italian regional claims databases during 2010-2019. A tailor-made R-based tool developed for distributed analysis of claims data using a study-specific common data model was customized for this study. We measured the yearly prevalence of biological drug users and the frequency of switches between originator and biosimilars for infliximab, etanercept, and adalimumab separately and stratified them by calendar year and region. We then calculated the cumulative number of users and person-years (PYs) of exposure to individual biological drugs approved for IMIDs. For a number of safety outcomes (e.g., severe acute respiratory syndrome coronavirus 2 [SARS-COV-2] infection), we conducted a sample power calculation to estimate the PYs of exposure required to investigate their association with individual biological drugs approved for IMIDs, considering different strengths of association.

RESULTS

From a total underlying population of almost 50 million inhabitants from 13 Italian regions, we identified 143,602 (0.3%) biological drug users, with a cumulative exposure of 507,745 PYs during the entire follow-up. The mean age ± standard deviation of biological drug users was 49.3 ± 16.3, with a female-to-male ratio of 1.2. The age-adjusted yearly prevalence of biological drug users increased threefold from 0.7 per 1000 in 2010 to 2.1 per 1000 in 2019. Overall, we identified 40,996 users of biosimilars of tumor necrosis factor (TNF)-α inhibitors (i.e., etanercept, adalimumab, and infliximab) in the years 2015-2019. Of these, 46% (N = 18,845) switched at any time between originator and biosimilars or vice versa. To investigate a moderate association (incidence rate ratio 2) between biological drugs approved for IMIDs and safety events of interest, such as optic neuritis (lowest background incidence rate 10.4/100,000 PYs) or severe infection (highest background incidence rate 4312/100,000 PYs), a total of 43,311 PYs and 104 PYs of exposure to individual biological drugs, respectively, would be required. As such, using this network, of 15 individual biological drugs approved for IMIDs, the association with those adverse events could be investigated for four (27%) and 14 (93%), respectively.

CONCLUSION

The VALORE project multi-database network has access to data on more than 140,000 biological drug users (and > 0.5 million PYs) from 13 Italian regions during the years 2010-2019, which will be further expanded with the inclusion of data from other regions and more recent calendar years. Overall, the cumulated amount of person-time of exposure to biological drugs approved for IMIDs provides enough statistical power to investigate weak/moderate associations of almost all individual compounds and the most relevant safety outcomes. Moreover, this network may offer the opportunity to investigate the interchangeability of originator and biosimilars of several TNFα inhibitors in different therapeutic areas in real-world settings.

Understanding Patient Needs Regarding Adverse Drug Reaction Reporting Smartphone Applications: A Qualitative Insight from Saudi Arabia

BACKGROUND

A pragmatic shift in the healthcare sector characterized by moving from curative to preventive approaches highlights the role of pharmacovigilance in patient safety. There have been few published studies on patient reporting of adverse drug reactions (ADRs) in Saudi Arabia. This qualitative study aims to explore the community opinions and the need for patient-friendly smartphone applications (SPAs) to enhance their participation in ADR reporting.

METHODS

Purposeful sampling was followed to recruit study participants, a semi-structured interview guide was used to conduct interviews, and the saturation was reached after the 13th interviewer; no new information was obtained after two subsequent interviews. All the interviews were audio-recorded, transcribed verbatim, and analyzed by means of a standard content analysis framework.

RESULTS

As per the WHO guidelines, eleven participants were aware of the term "ADR". All the participants denied receiving any prior education and attending events about ADRs and were unaware of the Saudi FDA-ADR reporting systems. The use of technologies such as SPAs has been widely accepted with a high level of concern for data confidentiality and privacy.

CONCLUSIONS

These findings point out the need to build patient-oriented educational programs to increase their awareness of ADR reporting and to prioritize the use of artificial intelligence (AI) to be integrated in the Saudi healthcare system to develop future SPAs for improving both patient safety and signal detection of ADRs.

The impact of regulation changes in the spontaneous reporting system for vaccines on reporting trends and signal detection in Japan

PURPOSE

Spontaneous reporting constitutes one of the most fundamental and important systems for pharmacovigilance. In Japan, important regulation changes in the vaccine spontaneous reporting were implemented between 2009 and 2013; however, no studies had yet assessed the impact of the changes. The objective of this study was to assess the impact on the reporting trends in vaccine reports and on signal detection for vaccines.

METHODS

For assessment of the impact on the reporting trends, we performed the joinpoint trend analysis and descriptively considered number of vaccine reports grouped by the timing of the regulation change. For assessment of the impact on signal detection, we performed signal detection using dataset during the pre or postperiod of the regulation changes, and compared their agreement rates, which was calculated with a reference set for vaccines, created by the Global Research in Paediatrics project.

RESULTS

We retrieved 467 635 spontaneous reports, including 12 287 vaccine reports from April 2004 to March 2019. The average number of vaccine reports per year increased from 231 reports during the preperiod to 1227 during the postperiod. The joinpoint trend analysis found two joinpoints and differentiated three trends, significant increased trend of which was observed when regulations had changed. For signal detection, the agreement rate was improved when using data during the postperiod.

CONCLUSION

We concluded that the regulation changes increased the number of vaccine reports, and could have improved signal detection performance for vaccines by accelerating accumulation of reports, while more spontaneous reports are necessary to optimize signal detection.

Exploratory Study of Signals for Asthma Drugs in Children, Using the EudraVigilance Database of Spontaneous Reports

Introduction

As asthma medications are frequently prescribed for children, knowledge of the safety of these drugs in the paediatric population is important. Although spontaneous reports cannot be used to prove causality of adverse events, they are important in the detection of safety signals.

Objective

Our objective was to provide an overview of adverse drug events associated with asthma medications in children from a spontaneous reports database and to identify new signals.

Methods

Spontaneous reports concerning asthma drugs were obtained from EudraVigilance, the European Medicine Agency’s database for suspected adverse drug reactions. For each drug–event combination, we calculated the proportional reporting ratio (PRR) in the study period 2011–2017. Signals in children (aged 0–17 years) were compared with signals in the whole population. Analyses were repeated for different age categories, by sex and by therapeutic area.

Results

In total, 372,345 reports in children resulted in 385 different signals concerning asthma therapy. The largest group consisted of psychiatric events (65 signals). Only 30 signals were new, with seven, including herpes viral infections, associated with omalizumab. Stratification by age, sex and therapeutic area provided additional new signals, such as hypertrichoses with budesonide and encephalopathies with theophylline. Of all signals in children, 60 (16%) did not appear in the whole population.

Conclusions

The majority of signals regarding asthma therapy in children were already known, but we also identified new signals. We showed that signals can be masked if age stratification is not conducted. Further exploration is needed to investigate the risk and causality of the newly found signals.

Electronic supplementary material

The online version of this article (10.1007/s40264-019-00870-x) contains supplementary material, which is available to authorized users.

Ability of Primary Care Health Databases to Assess Medicinal Products Discussed by the European Union Pharmacovigilance Risk Assessment Committee

This study measured the exposure to different categories of medicinal products discussed by the European Union (EU) Pharmacovigilance Risk Assessment Committee from September to November 2018 in four electronic primary care health databases: IQVIA Medical Research Data‐UK, IQVIA Medical Research Data‐France, IQVIA Medical Research Data‐Germany, and Clinical Practice Research Datalink Aurum, in the entire lifespan of each database until August 31, 2018. The assessment of 83 centrally authorized products and 45 nationally authorized products showed that coverage was better for products marketed for longer duration and worse for orphan drugs. The ability to detect associations against hypothetical comparators was better for more common events and for larger effect sizes. Coverage of advanced therapies was worse for those typically administered in a specialized rather than primary care setting. This study shows that to enable better informed regulatory decisions there is a need to access complementary data sources, particularly capturing secondary care prescribing.

An Implementation and Visualization of the Tree-Based Scan Statistic for Safety Event Monitoring in Longitudinal Electronic Health Data

INTRODUCTION

Longitudinal electronic healthcare data hold great potential for drug safety surveillance. The tree-based scan statistic (TBSS), as implemented by the TreeScan^® software, allows for hypothesis-free signal detection in longitudinal data by grouping safety events according to branching, hierarchical data coding systems, and then identifying signals of disproportionate recording (SDRs) among the singular events or event groups.

OBJECTIVE

The objective of this analysis was to identify and visualize SDRs with the TBSS in historical data from patients using two antifungal drugs, itraconazole or terbinafine. By examining patients who used either itraconazole or terbinafine, we provide a conceptual replication of a previous TBSS analyses by varying methodological choices and using a data source that had not been previously used with the TBSS, i.e., the Optum Clinformatics™ claims database. With this analysis, we aimed to test a parsimonious design that could be the basis of a broadly applicable method for multiple drug and safety event pairs.

METHODS

The TBSS analysis was used to examine incident events and any itraconazole or terbinafine use among US-based patients from 2002 through 2007. Event frequencies before and after the first day of drug exposure were compared over 14- and 56-day periods of observation in a Bernoulli model with a self-controlled design. Safety events were classified into a hierarchical tree structure using the Clinical Classifications Software (CCS) which mapped International Classification of Diseases, 9th Revision (ICD-9) codes to 879 diagnostic groups. Using the TBSS, the log likelihood ratio of observed versus expected events in all groups along the CCS hierarchy were compared, and groups of events that occurred at disproportionally high frequencies were identified as potential SDRs; p-values for the potential SDRs were estimated with Monte-Carlo permutation based methods. Output from TreeScan^® was visualized and plotted as a network which followed the CCS tree structure.

RESULTS

Terbinafine use (n = 223,968) was associated with SDRs for diseases of the circulatory system (14- and 56-day p = 0.001) and heart (14-day p = 0.026 and 56-day p = 0.001) as well as coronary atherosclerosis and other heart disease (14-day p = 0.003 and 56-day p = 0.004). For itraconazole use (n = 36,025), the TBSS identified SDRs for coronary atherosclerosis and other heart disease (p = 0.002) and complications of an implanted or grafted device (14-day p = 0.001 and 56-day p < 0.05). Use of both drugs was associated with SDRs for diseases of the digestive system at 14 days (p < 0.05) and this SDR had been observed among terbinafine users in a previous TBSS analysis with a different data source. The TreeScan^® visualization facilitated the identification of the atherosclerosis and other heart disease SDRs as well as highlighting the consistency of the SDR for diseases of the digestive system across drugs and data sources.

CONCLUSION

With the TBSS, we identified potential SDRs related to the circulatory system that may reflect the cardiac risk that was described in the itraconazole product label. SDRs for diseases of the digestive system among terbinafine users were also reported in a previous signal detection analysis, although other SDRs from the previous publications were not replicated. The TBSS visualizations aided in the understanding and interpretation of the TBSS output, including the comparisons to the previous publications. In this conceptual replication, differences in the results observed in our analysis and the previous analyses could be attributable to variation in modeling and design choices as well as factors that were intrinsic to the underlying data sources. The broad consistency, but far from perfect concordance, of our results with the known safety profile of these antifungals including the risks from the itraconazole product label supports the rationale for continued investigations of signal detection methods across differing data sources and populations.

Association of atorvastatin with the risk of hepatotoxicity: a pilot prescription sequence symmetry analysis

Purpose

This study aimed to evaluate Atorvastatin (ATO)-associated hepatotoxicity using prescription sequence symmetry analysis (PSSA), based on a health insurance database of a Chinese population living in Jiangsu Province, China.

Methods

Patients prescribed ATO and hepatoprotective drugs in 2017 were identified, and the run-in period was determined based on the "waiting-time" distribution. Adjusted sequence ratio (ASR) and 95% confidence interval (95% CI) were calculated to estimate the risk of ATO-associated hepatotoxicity under different time intervals or based on gender and age stratification.

Results

A total of 2,549 patients, with 1,518 filling the ATO prescription first and 1,031 filling the ATO prescription second, were analyzed. After setting the run-in period as 30 days and the time interval as 15, 30, 60, 90, 120, and 180 days, the ASRs were 1.492 (95% CI: 1.367-1.652), 1.399 (95% CI: 1.308-1.508), 1.280 (95% CI: 1.213-1.357), 1.292 (95% CI: 1.234-1.356), 1.278 (95% CI: 1.226-1.336), and 1.274 (95% CI: 1.229-1.323), respectively. No significant difference was observed between different genders and ages (χ²=0.161, P=0.688; χ²=1.565, P=0.211, respectively).

Conclusion

This is the first study conducted in a real-world setting to evaluate the relationship between ATO and hepatotoxicity using the PSSA in a Chinese population. We found a 1.3- to 1.5-fold increase in risk of hepatotoxicity following ATO, with the greater risk occurring within the first 30 days of treatment.

Web-Based Signal Detection Using Medical Forums Data in France: Comparative Analysis

BACKGROUND

While traditional signal detection methods in pharmacovigilance are based on spontaneous reports, the use of social media is emerging. The potential strength of Web-based data relies on their volume and real-time availability, allowing early detection of signals of disproportionate reporting (SDRs).

OBJECTIVE

This study aimed (1) to assess the consistency of SDRs detected from patients' medical forums in France compared with those detected from the traditional reporting systems and (2) to assess the ability of SDRs in identifying earlier than the traditional reporting systems.

METHODS

Messages posted on patients' forums between 2005 and 2015 were used. We retained 8 disproportionality definitions. Comparison of SDRs from the forums with SDRs detected in VigiBase was done by describing the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, receiver operating characteristics curve, and the area under the curve (AUC). The time difference in months between the detection dates of SDRs from the forums and VigiBase was provided.

RESULTS

The comparison analysis showed that the sensitivity ranged from 29% to 50.6%, the specificity from 86.1% to 95.5%, the PPV from 51.2% to 75.4%, the NPV from 68.5% to 91.6%, and the accuracy from 68% to 87.7%. The AUC reached 0.85 when using the metric empirical Bayes geometric mean. Up to 38% (12/32) of the SDRs were detected earlier in the forums than that in VigiBase.

CONCLUSIONS

The specificity, PPV, and NPV were high. The overall performance was good, showing that data from medical forums may be a valuable source for signal detection. In total, up to 38% (12/32) of the SDRs could have been detected earlier, thus, ensuring the increased safety of patients. Further enhancements are needed to investigate the reliability and validation of patients' medical forums worldwide, the extension of this analysis to all possible drugs or at least to a wider selection of drugs, as well as to further assess performance against established signals.

Signal Detection for Recently Approved Products: Adapting and Evaluating Self-Controlled Case Series Method Using a US Claims and UK Electronic Medical Records Database

INTRODUCTION

The Self-Controlled Case Series (SCCS) method has been widely used for hypothesis testing, but there is limited evidence of its performance for safety signal detection.

OBJECTIVE

The objective of this study was to evaluate SCCS for signal detection on recently approved products.

METHODS

A retrospective study covered the period after three recently marketed drugs were launched through to 31 December 2010 using The Health Improvement Network, a UK primary care database, and Optum, a US claims database. The SCCS method was applied to examine five heterogenous outcomes with desvenlafaxine and escitalopram and six outcomes with adalimumab for Signals of Disproportional Recording (SDRs); a positive finding was determined to be when the lower bound of 95% Confidence Interval of the incidence rate ratio (IRR) estimate was >  1. Multiple design choices were tested and the trend in IRR estimates over calendar time for one drug event pair was examined.

RESULTS

All six outcomes with adalimumab, three of five outcomes with desvenlafaxine, and four of five outcomes with escitalopram had SDRs. SCCS highlighted all acute events in the primary analysis but was less successful with slower-onset outcomes. Performance varied by risk period definition. Changes in IRR estimates over quarterly intervals for adalimumab with herpes zoster showed marked higher SDR within 9 months of drug launch.

CONCLUSION

SCCS shows promise for signal detection: it may highlight known associations for recent marketed products and has potential for early signal identification. SCCS performance varied by design choice and the nature of both exposure and event pair. Future work is needed to determine how effective the approach is in prospective testing and determining the performance characteristics of the approach.

A Pharmacoepidemiology Database System for Monitoring Risk Due to the Use of Medicines by New Zealand Primary Care Patients

INTRODUCTION

The use of large record-linked healthcare databases for drug safety research and surveillance is now accepted practice. New Zealand's standardized national healthcare datasets provide the potential to automate the conduct of pharmacoepidemiological studies to provide rapid validation of medicine safety signals.

OBJECTIVES

Our objectives were to describe the methodology undertaken by a semi-automated computer system developed to rapidly assess risk due to drug exposure in New Zealand's population of primary care patients and to compare results from three studies with previously published findings.

METHODS

Data from three national databases were linked at the patient level in the automated studies. A retrospective nested case-control design was used to evaluate risk for upper gastrointestinal bleeding (UGIB), acute kidney failure (AKF), and serious arrhythmia associated with individual medicines, therapeutic classes of medicines, and concurrent use of medicines from multiple therapeutic classes.

RESULTS

The patient cohort available for each study included 5,194,256 patients registered between 2007 and 2014, with a total of 34,630,673 patient-years at risk. An increased risk for UGIB was associated with non-steroidal anti-inflammatory drugs (NSAIDs) (adjusted odds ratio [AOR] 4.16, 95% confidence interval [CI] 3.90-4.43, p < 0.001) and selective serotonin reuptake inhibitors (AOR 1.39, 95% CI 1.20-1.62, p < 0.001); an increased risk for AKF was associated with NSAIDs (AOR 1.78, 95% CI 1.73-1.83, p < 0.001) and proton pump inhibitors (AOR 1.78, 95% CI 1.72-1.83, p < 0.001); and an increased risk for serious arrhythmia was associated with fluoroquinolones (AOR 1.38, 95% CI 1.26-151, p < 0.001) and penicillins (AOR 1.69, 95% CI 1.61-1.77, p < 0.001).

CONCLUSIONS

Automated case-control studies using New Zealand's healthcare datasets can replicate associations of risk with drug exposure consistent with previous findings. Their speed of conduct enables systematic monitoring of risk for adverse events associated with a wide range of medicines.

Can Electronic Health Records Databases Complement Spontaneous Reporting System Databases? A Historical-Reconstruction of the Association of Rofecoxib and Acute Myocardial Infarction

Background: Several initiatives have assessed if mining electronic health records (EHRs) may accelerate the process of drug safety signal detection. In Europe, Exploring and Understanding Adverse Drug Reactions (EU-ADR) Project Focused on utilizing clinical data from EHRs of over 30 million patients from several European countries. Rofecoxib is a prescription COX-2 selective Non-Steroidal Anti-Inflammatory Drugs (NSAID) approved in 1999. In September 2004, the manufacturer withdrew rofecoxib from the market because of safety concerns. In this study, we investigated if the signal concerning rofecoxib and acute myocardial infarction (AMI) could have been identified in EHR database (EU-ADR project) earlier than spontaneous reporting system (SRS), and in advance of rofecoxib withdrawal.

Methods: Data from the EU-ADR project and WHO-VigiBase (for SRS) were used for the analysis. Signals were identified when respective statistics exceeded defined thresholds. The SRS analyses was conducted two ways- based on the date the AMI events with rofecoxib as a suspect medication were entered into the database and also the date that the AMI event occurred with exposure to rofecoxib.

Results: Within the databases participating in EU-ADR it was possible to identify a strong signal concerning rofecoxib and AMI since Q3 2000 [RR LGPS = 4.5 (95% CI: 2.84–6.72)] and peaked to 4.8 in Q4 2000. In WHO-VigiBase, for AMI term grouping, the EB05 threshold of 2 was crossed in the Q4 2004 (EB05 = 2.94). Since then, the EB05 value increased consistently and peaked in Q3 2006 (EB05 = 48.3) and then again in Q2 2008 (EB05 = 48.5). About 93% (2260 out of 2422) of AMIs reported in WHO-VigiBase database actually occurred prior to the product withdrawal, however, they were reported after the risk minimization/risk communication efforts.

Conclusion: In this study, EU-EHR databases were able to detect the AMI signal 4 years prior to the SRS database. We believe that for events that are consistently documented in EHR databases, such as serious events or events requiring in-patient medical intervention or hospitalization, the signal detection exercise in EHR would be beneficial for newly introduced medicinal products on the market, in addition to the SRS data.

Drug safety and big clinical data: Detection of drug-induced anaphylactic shock events

RATIONALE, AIMS, AND OBJECTIVES

The spontaneous reporting system currently used in pharmacovigilance is not sufficiently exhaustive to detect all adverse drug reactions (ADRs). With the widespread use of electronic health records, biomedical data collected during the clinical care process can be reused and analysed to better detect ADRs. The aim of this study was to assess whether querying a Clinical Data Warehouse (CDW) could increase the detection of drug-induced anaphylaxis.

METHODS

All known cases of drug-induced anaphylaxis that occurred or required hospitalization at Rennes Academic Hospital in 2011 (n = 19) were retrieved from the French pharmacovigilance database, which contains all reported ADR events. Then, from the Rennes Academic Hospital CDW, a training set (all patients hospitalized in 2011) and a test set (all patients hospitalized in 2012) were extracted. The training set was used to define an optimized query, by building a set of keywords (based on the known cases) and exclusion criteria to search structured and unstructured data within the CDW in order to identify at least all known cases of drug-induced anaphylaxis for 2011. Then, the real performance of the optimized query was tested in the test set.

RESULTS

Using the optimized query, 59 cases of drug-induced anaphylaxis were identified among the 253 patient records extracted from the test set as possible anaphylaxis cases. Specifically, the optimal query identified 41 drug-induced anaphylaxis cases that were not detected by searching the French pharmacovigilance database but missed 7 cases detected only by spontaneous reporting.

DISCUSSION

We proposed an information retrieval-based method for detecting drug-induced anaphylaxis, by querying structured and unstructured data in a CDW. CDW queries are less specific than spontaneous reporting and Diagnosis-related Groups queries, although their sensitivity is much higher. CDW queries can facilitate monitoring by pharmacovigilance experts. Our method could be easily incorporated in the routine practice.

Estimating causal log-odds ratio using the case-control sample and its application in the pharmaco-epidemiology study

One important goal in pharmaco-epidemiology studies is to understand the causal relationship between drug exposures and their clinical outcomes, including adverse drug events. In order to achieve this goal, however, we need to resolve several challenges. Most of pharmaco-epidemiology data are observational and confounding is largely present due to many co-medications. The pharmaco-epidemiology study data set is often sampled from large medical record databases using a matched case-control design, and it may not be representative of the original patient population in the medical record databases. Data analysis method needs to handle a large sample size that cannot be handled using existing statistical analysis packages. In this paper, we tackle these challenges both methodologically and computationally. We propose a conditional causal log-odds ratio (OR) definition to characterize causal effects of drug exposures on a binary adverse drug event adjusting for individual level confounders. Using a case-control design, we present a propensity score estimation using only case samples and we provide sufficient conditions for the consistency of the estimation of the causal log-odds ratio using case-based propensity scores. Computationally, we implement a principle component analysis to reduce high-dimensional confounders. Extensive simulation studies are performed to demonstrate superior performance of our method to existing methods. Finally, we apply the proposed method to analyze drug-induced myopathy data sampled from a de-identified subset of medical record database (close to 5 million patient records), The Indiana Network for Patient Care. Our method identified 70 drug-induced myopathy (p < 0.05) out 72 drugs, which have myoathy side effects on their FDA drug labels. These 70 drugs include three statins who are known for their myopathy side effects.

Is patient exposure preapproval and postapproval a determinant of the timing and frequency of occurrence of safety issues?

BACKGROUND

The amount of drug exposure, pre and post approval, is considered to be a direct determinant of knowledge about the safety of a drug. A larger pre-approval exposed population is supposed to reduce the risk of unanticipated safety issues post-approval. The amount of use in the postapproval population is also expected to influence the occurrence and timing of safety issues. We investigated how the amount of pre and post approval exposure influences the detection of post-approval safety issues.

METHODS

A cohort of innovative drugs approved in Europe was followed for the period of 2012-2016. The main outcome of interest was a new safety issue in the period. Post-approval exposure was collected at 6 month intervals, and pre-approval exposure was collected at the moment of authorisation. Other characteristics collected for the included drugs were anatomical therapeutical chemical (ATC) class, biological status, orphan status and type of approval. We used Cox proportional hazards regression to investigate the association between exposure and the hazard of having a first safety issue.

RESULTS

The pre-approval exposure was not associated with the risk of safety issues after adjusting for ATC class, biological status, and treatment duration. Higher post-approval exposure was associated with more new safety issues identified (HR = 2.44 (95% CI = 1.12-5.31)) for drugs with more than 1,000 patient-years of cumulative exposure compared to drugs with less than 1,000 patient years of exposure.

CONCLUSION

Our results suggest that postapproval exposure influences the detection of safety issues.

The limitations of some European healthcare databases for monitoring the effectiveness of pregnancy prevention programmes as risk minimisation measures

PURPOSE

Pregnancy prevention programmes (PPPs) exist for some medicines known to be highly teratogenic. It is increasingly recognised that the impact of these risk minimisation measures requires periodic evaluation. This study aimed to assess the extent to which some of the data needed to monitor the effectiveness of PPPs may be present in European healthcare databases.

METHODS

An inventory was completed for databases contributing to EUROmediCAT capturing pregnancy and prescription data in Denmark, Norway, the Netherlands, Italy (Tuscany/Emilia Romagna), Wales and the rest of the UK, to determine the extent of data collected that could be used to evaluate the impact of PPPs.

RESULTS

Data availability varied between databases. All databases could be used to identify the frequency and duration of prescriptions to women of childbearing age from primary care, but there were specific issues with availability of data from secondary care and private care. To estimate the frequency of exposed pregnancies, all databases could be linked to pregnancy data, but the accuracy of timing of the start of pregnancy was variable, and data on pregnancies ending in induced abortions were often not available. Data availability on contraception to estimate compliance with contraception requirements was variable and no data were available on pregnancy tests.

CONCLUSION

Current electronic healthcare databases do not contain all the data necessary to fully monitor the effectiveness of PPP implementation, and thus, special data collection measures need to be instituted.

Validity of peptic ulcer disease and upper gastrointestinal bleeding diagnoses in administrative databases: a systematic review protocol

INTRODUCTION

Administrative healthcare databases are useful to investigate the epidemiology, health outcomes, quality indicators and healthcare utilisation concerning peptic ulcers and gastrointestinal bleeding, but the databases need to be validated in order to be a reliable source for research. The aim of this protocol is to perform the first systematic review of studies reporting the validation of International Classification of Diseases, 9th Revision and 10th version (ICD-9 and ICD-10) codes for peptic ulcer and upper gastrointestinal bleeding diagnoses.

METHODS AND ANALYSIS

MEDLINE, EMBASE, Web of Science and the Cochrane Library databases will be searched, using appropriate search strategies. We will include validation studies that used administrative data to identify peptic ulcer disease and upper gastrointestinal bleeding diagnoses or studies that evaluated the validity of peptic ulcer and upper gastrointestinal bleeding codes in administrative data. The following inclusion criteria will be used: (a) the presence of a reference standard case definition for the diseases of interest; (b) the presence of at least one test measure (eg, sensitivity, etc) and (c) the use of an administrative database as a source of data. Pairs of reviewers will independently abstract data using standardised forms and will evaluate quality using the checklist of the Standards for Reporting of Diagnostic Accuracy (STARD) criteria. This systematic review protocol has been produced in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocol (PRISMA-P) 2015 statement.

ETHICS AND DISSEMINATION

Ethics approval is not required given that this is a protocol for a systematic review. We will submit results of this study to a peer-reviewed journal for publication. The results will serve as a guide for researchers validating administrative healthcare databases to determine appropriate case definitions for peptic ulcer disease and upper gastrointestinal bleeding, as well as to perform outcome research using administrative healthcare databases of these conditions.

TRIAL REGISTRATION NUMBER

CRD42015029216.

Useful Interplay Between Spontaneous ADR Reports and Electronic Healthcare Records in Signal Detection

Background and Objective

Spontaneous reporting systems (SRSs) remain the cornerstone of post-marketing drug safety surveillance despite their well-known limitations. Judicious use of other available data sources is essential to enable better detection, strengthening and validation of signals. In this study, we investigated the potential of electronic healthcare records (EHRs) to be used alongside an SRS as an independent system, with the aim of improving signal detection.

Methods

A signal detection strategy, focused on a limited set of adverse events deemed important in pharmacovigilance, was performed retrospectively in two data sources—(1) the Exploring and Understanding Adverse Drug Reactions (EU-ADR) database network and (2) the EudraVigilance database—using data between 2000 and 2010. Five events were considered for analysis: (1) acute myocardial infarction (AMI); (2) bullous eruption; (3) hip fracture; (4) acute pancreatitis; and (5) upper gastrointestinal bleeding (UGIB). Potential signals identified in each system were verified using the current published literature. The complementarity of the two systems to detect signals was expressed as the percentage of the unilaterally identified signals out of the total number of confirmed signals. As a proxy for the associated costs, the number of signals that needed to be reviewed to detect one true signal (number needed to detect [NND]) was calculated. The relationship between the background frequency of the events and the capability of each system to detect signals was also investigated.

Results

The contribution of each system to signal detection appeared to be correlated with the background incidence of the events, being directly proportional to the incidence in EU-ADR and inversely proportional in EudraVigilance. EudraVigilance was particularly valuable in identifying bullous eruption and acute pancreatitis (71 and 42 % of signals were correctly identified from the total pool of known associations, respectively), while EU-ADR was most useful in identifying hip fractures (60 %). Both systems contributed reasonably well to identification of signals related to UGIB (45 % in EudraVigilance, 40 % in EU-ADR) but only fairly for signals related to AMI (25 % in EU-ADR, 20 % in EudraVigilance). The costs associated with detection of signals were variable across events; however, it was often more costly to detect safety signals in EU-ADR than in EudraVigilance (median NNDs: 7 versus 5).

Conclusion

An EHR-based system may have additional value for signal detection, alongside already established systems, especially in the presence of adverse events with a high background incidence. While the SRS appeared to be more cost effective overall, for some events the costs associated with signal detection in the EHR might be justifiable.

Electronic supplementary material

The online version of this article (doi:10.1007/s40264-015-0341-5) contains supplementary material, which is available to authorized users.

Improving Information on Maternal Medication Use by Linking Prescription Data to Congenital Anomaly Registers: A EUROmediCAT Study

Introduction

Research on associations between medication use during pregnancy and congenital anomalies is significative for assessing the safe use of a medicine in pregnancy. Congenital anomaly (CA) registries do not have optimal information on medicine exposure, in contrast to prescription databases. Linkage of prescription databases to the CA registries is a potentially effective method of obtaining accurate information on medicine use in pregnancies and the risk of congenital anomalies.

Methods

We linked data from primary care and prescription databases to five European Surveillance of Congenital Anomalies (EUROCAT) CA registries. The linkage was evaluated by looking at linkage rate, characteristics of linked and non-linked cases, first trimester exposure rates for six groups of medicines according to the prescription data and information on medication use registered in the CA databases, and agreement of exposure.

Results

Of the 52,619 cases registered in the CA databases, 26,552 could be linked. The linkage rate varied between registries over time and by type of birth. The first trimester exposure rates and the agreements between the databases varied for the different medicine groups. Information on anti-epileptic drugs and insulins and analogue medicine use recorded by CA registries was of good quality. For selective serotonin reuptake inhibitors, anti-asthmatics, antibacterials for systemic use, and gonadotropins and other ovulation stimulants, the recorded information was less complete.

Conclusion

Linkage of primary care or prescription databases to CA registries improved the quality of information on maternal use of medicines in pregnancy, especially for medicine groups that are less fully registered in CA registries.

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.

Prescribing of Antidiabetic Medicines before, during and after Pregnancy: A Study in Seven European Regions

AIM

To explore antidiabetic medicine prescribing to women before, during and after pregnancy in different regions of Europe.

METHODS

A common protocol was implemented across seven databases in Denmark, Norway, The Netherlands, Italy (Emilia Romagna/Tuscany), Wales and the rest of the UK. Women with a pregnancy starting and ending between 2004 and 2010, (Denmark, 2004-2009; Norway, 2005-2010; Emilia Romagna, 2008-2010), which ended in a live or stillbirth, were identified. Prescriptions for antidiabetic medicines issued (UK) or dispensed (non-UK) during pregnancy and/or the year before or year after pregnancy were identified. Prescribing patterns were compared across databases and over calendar time.

RESULTS

1,082,673 live/stillbirths were identified. Pregestational insulin prescribing during the year before pregnancy ranged from 0.27% (CI95 0.25-0.30) in Tuscany to 0.45% (CI95 0.43-0.47) in Norway, and increased between 2004 and 2009 in all countries. During pregnancy, insulin prescribing peaked during the third trimester and increased over time; third trimester prescribing was highest in Tuscany (2.2%) and lowest in Denmark (0.5%). Of those prescribed an insulin during pregnancy, between 50.5% in Denmark and 88.8% in the Netherlands received an insulin analogue alone or in combination with human insulin, this proportion increasing over time. Oral products were mainly metformin and prescribing was highest in the 3 months before pregnancy. Metformin use during pregnancy increased in some countries.

CONCLUSION

Pregestational diabetes is increasing in many areas of Europe. There is considerable variation between and within countries in the choice of medication for treating pregestational diabetes in pregnancy, including choice of insulin analogues and oral antidiabetics, and very large variation in the treatment of gestational diabetes despite international guidelines.

Risk of cardiac valvulopathy with use of bisphosphonates: a population-based, multi-country case-control study

Analyses of healthcare data from 30 million individuals in three countries showed that current use of bisphosphonates may be associated with a small increased risk of cardiac valvulopathy (vs. those not exposed within the previous year), although confounding cannot be entirely ruled out. The observed tendency for decreased valvulopathy risk with cumulative duration of bisphosphonate use >6 months may even indicate a protective effect with prolonged use. Further studies are still needed to evaluate whether bisphosphonates increase or decrease the risk of valvulopathy.

INTRODUCTION

A signal of cardiac valve disorders with use of bisphosphonates was identified in the literature and EudraVigilance database, which contains reports of suspected adverse drug reactions from worldwide sources. The aim of this study was to evaluate the association using population-based healthcare data.

METHODS

This was a case-control study among users of bisphosphonates and other drugs for osteoporosis in six healthcare databases covering over 30 million individuals in Italy, Netherlands and the UK from 1996 to 2012. Prescriptions/dispensations were used to assess drug exposure. Newly diagnosed cases of cardiac valvulopathy were identified via disease codes/free-text search. Controls were matched to each case by age, sex, database and index date. Adjusted odds ratios (ORs) were estimated using conditional logistic regression for the pooled data and meta-analysis of individual database risk estimates.

RESULTS

A small but statistically significant association was found between exposure to bisphosphonates as a class and risk of valvulopathy. Overall risk was 18 % higher (95 % CI 12-23 %) in those currently exposed to any bisphosphonate (mainly alendronate and risedronate) vs. those not exposed within the previous year. Risk of valve regurgitation was 14 % higher (95 % CI 7-22 %). Decreased valvulopathy risk was observed with longer cumulative duration of bisphosphonate use, compared to use of less than 6 months. Meta-analyses of database-specific estimates confirmed results from pooled analyses.

CONCLUSIONS

The observed increased risks of cardiac valvulopathy with bisphosphonate use, although statistically significant, were quite small and unlikely to be clinically significant. Further studies are still needed to evaluate whether bisphosphonates increase or decrease the risk of valvulopathy and to investigate possible mechanisms for the association.

Asthma medication prescribing before, during and after pregnancy: a study in seven European regions

OBJECTIVES

To explore utilisation patterns of asthma medication before, during and after pregnancy as recorded in seven European population-based databases.

DESIGN

A descriptive drug utilisation study.

SETTING

7 electronic healthcare databases in Denmark, Norway, the Netherlands, Italy (Emilia Romagna and Tuscany), Wales, and the Clinical Practice Research Datalink representing the rest of the UK.

PARTICIPANTS

All women with a pregnancy ending in a delivery that started and ended between 2004 and 2010, who had been present in the database for the year before, throughout and the year following pregnancy.

MAIN OUTCOME MEASURES

The percentage of deliveries where the woman received an asthma medicine prescription, based on prescriptions issued (UK) or dispensed (non-UK), during the year before, throughout or during the year following pregnancy. Asthma medicine prescribing patterns were described for 3-month time periods and the choice of asthma medicine and changes in prescribing over the study period were evaluated in each database.

RESULTS

In total, 1,165,435 deliveries were identified. The prevalence of asthma medication prescribing during pregnancy was highest in the UK and Wales databases (9.4% (CI95 9.3% to 9.6%) and 9.4% (CI95 9.1% to 9.6%), respectively) and lowest in the Norwegian database (3.7% (CI95 3.7% to 3.8%)). In the year before pregnancy, the prevalence of asthma medication prescribing remained constant in all regions. Prescribing levels peaked during the second trimester of pregnancy and were at their lowest during the 3-month period following delivery. A decline was observed, in all regions except the UK, in the prescribing of long-acting β-2-agonists during pregnancy. During the 7-year study period, there were only small changes in prescribing patterns.

CONCLUSIONS

Differences were found in the prevalence of prescribing of asthma medications during and surrounding pregnancy in Europe. Inhaled β-2 agonists and inhaled corticosteroids were, however, the most popular therapeutic regimens in all databases.

Antiepileptic drug prescribing before, during and after pregnancy: a study in seven European regions

PURPOSE

The aim of this study was to explore antiepileptic drug (AED) prescribing before, during and after pregnancy as recorded in seven population-based electronic healthcare databases.

METHODS

Databases in Denmark, Norway, the Netherlands, Italy (Emilia Romagna/Tuscany), Wales and the Clinical Practice Research Datalink, representing the rest of the UK, were accessed for the study. Women with a pregnancy starting and ending between 2004 and 2010, which ended in a delivery, were identified. AED prescriptions issued (UK) or dispensed (non-UK) at any time during pregnancy and the 6 months before and after pregnancy were identified in each of the databases. AED prescribing patterns were analysed, and the choice of AEDs and co-prescribing of folic acid were evaluated.

RESULTS

In total, 978 957 women with 1 248 713 deliveries were identified. In all regions, AED prescribing declined during pregnancy and was lowest during the third trimester, before returning to pre-pregnancy levels by 6 months following delivery. For all deliveries, the prevalence of AED prescribing during pregnancy was 51 per 10 000 pregnancies (CI95 49-52%) and was lowest in the Netherlands (43/10 000; CI95 33-54%) and highest in Wales (60/10 000; CI95 54-66%). In Denmark, Norway and the two UK databases lamotrigine was the most commonly prescribed AED; whereas in the Italian and Dutch databases, carbamazepine, valproate and phenobarbital were most frequently prescribed. Few women prescribed with AEDs in the 3 months before pregnancy were co-prescribed with high-dose folic acid: ranging from 1.0% (CI95 0.3-1.8%) in Emilia Romagna to 33.5% (CI95 28.7-38.4%) in Wales.

CONCLUSION

The country's differences in prescribing patterns may suggest different use, knowledge or interpretation of the scientific evidence base. The low co-prescribing of folic acid indicates that more needs to be done to better inform clinicians and women of childbearing age taking AEDs about the need to offer and receive complete preconception care.

Using real-world healthcare data for pharmacovigilance signal detection - the experience of the EU-ADR project

A prospective pharmacovigilance signal detection study, comparing the real-world healthcare data (EU-ADR) and two spontaneous reporting system (SRS) databases, US FDA's Adverse Event Reporting System and WHO's Vigibase is reported. The study compared drug safety signals found in the EU-ADR and SRS databases. The potential for signal detection in the EU-ADR system was found to be dependent on frequency of the event and utilization of drugs in the general population. The EU-ADR system may have a greater potential for detecting signals for events occurring at higher frequency in general population and those that are commonly not considered as potentially a drug-induced event. Factors influencing various differences between the datasets are discussed along with potential limitations and applications to pharmacovigilance practice.

The role of electronic healthcare record databases in paediatric drug safety surveillance: a retrospective cohort study

AIM

Electronic healthcare record (EHR)-based surveillance systems are increasingly being developed to support early detection of safety signals. It is unknown what the power of such a system is for surveillance among children and adolescents. In this paper we provide estimates of the number and classes of drugs, and incidence rates (IRs) of events, that can be monitored in children and adolescents (0-18 years).

METHODS

Data were obtained from seven population-based EHR databases in Denmark, Italy, and the Netherlands during the period 1996-2010. We estimated the number of drugs for which specific adverse events can be monitored as a function of actual drug use, minimally detectable relative risk (RR) and IRs for 10 events.

RESULTS

The population comprised 4 838 146 individuals (25 575 132 person years (PYs)), who were prescribed 2170 drugs (1 610 631 PYs drug-exposure). Half of the total drug-exposure in PYs was covered by only 18 drugs (0.8%). For a relatively frequent event like upper gastrointestinal bleeding there were 39 drugs for which an association with a RR ≥4, if present, could be investigated. The corresponding number of drugs was eight for a rare event like anaphylactic shock.

CONCLUSION

Drug use in children is rare and shows little variation. The number of drugs with enough exposure to detect rare adverse events in children and adolescents within an EHR-based surveillance system such as EU-ADR is limited. Use of additional sources of paediatric drug exposure information and global collaboration are imperative in order to optimize EHR data for paediatric safety surveillance.

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.

Pediatric drug safety signal detection: a new drug-event reference set for performance testing of data-mining methods and systems

BACKGROUND

Better evidence regarding drug safety in the pediatric population might be generated from existing data sources such as spontaneous reporting systems and electronic healthcare records. The Global Research in Paediatrics (GRiP)-Network of Excellence aims to develop pediatric-specific methods that can be applied to these data sources. A reference set of positive and negative drug-event associations is required.

OBJECTIVE

The aim of this study was to develop a pediatric-specific reference set of positive and negative drug-event associations.

METHODS

Considering user patterns and expert opinion, 16 drugs that are used in individuals aged 0-18 years were selected and evaluated against 16 events, regarded as important safety outcomes. A cross-table of unique drug-event pairs was created. Each pair was classified as potential positive or negative control based on information from the drug's Summary of Product Characteristics and Micromedex. If both information sources consistently listed the event as an adverse event, the combination was reviewed as potential positive control. If both did not, the combination was evaluated as potential negative control. Further evaluation was based on published literature.

RESULTS

Selected drugs include ibuprofen, flucloxacillin, domperidone, methylphenidate, montelukast, quinine, and cyproterone/ethinylestradiol. Selected events include bullous eruption, aplastic anemia, ventricular arrhythmia, sudden death, acute kidney injury, psychosis, and seizure. Altogether, 256 unique combinations were reviewed, yielding 37 positive (17 with evidence from the pediatric population and 20 with evidence from adults only) and 90 negative control pairs, with the remainder being unclassifiable.

CONCLUSION

We propose a drug-event reference set that can be used to compare different signal detection methods in the pediatric population.

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

Purpose

To explore whether and how longitudinal medical records could be used as a source of reference in the early phases of signal detection and analysis of novel adverse drug reactions (ADRs) in a global pharmacovigilance database.

Methods

Drug and ADR combinations from the routine signal detection process of VigiBase® in 2011 were matched to combinations in The Health Improvement Network (THIN). The number and type of drugs and ADRs from the data sets were investigated. For unlabelled combinations, graphical display of longitudinal event patterns (chronographs) in THIN was inspected to determine if the pattern supported the VigiBase combination.

Results

Of 458 combinations in the VigiBase data set, 190 matched to corresponding combinations in THIN (after excluding drugs with less than 100 prescriptions in THIN). Eighteen percent of the VigiBase and 9% of the matched THIN combinations referred to new drugs reported with serious reactions. Of the 112 unlabelled combinations matched to THIN, 52 chronographs were inconclusive mainly because of lack of data; 34 lacked any outstanding pattern around the time of prescription; 24 had an elevation of events in the pre‐prescription period, hence weakened the suspicion of a drug relationship; two had an elevated pattern of events exclusively in the post‐prescription period that, after review of individual patient histories, did not support an association.

Conclusions

Longitudinal medical records were useful in understanding the clinical context around a drug and suspected ADR combination and the probability of a causal relationship. A drawback was the paucity of data for newly marketed drugs with serious reactions. © 2015 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons, Ltd.

Patient stratification and identification of adverse event correlations in the space of 1190 drug related adverse events

Purpose: New pharmacovigilance methods are needed as a consequence of the morbidity caused by drugs. We exploit fine-grained drug related adverse event information extracted by text mining from electronic medical records (EMRs) to stratify patients based on their adverse events and to determine adverse event co-occurrences.

Methods: We analyzed the similarity of adverse event profiles of 2347 patients extracted from EMRs from a mental health center in Denmark. The patients were clustered based on their adverse event profiles and the similarities were presented as a network. The set of adverse events in each main patient cluster was evaluated. Co-occurrences of adverse events in patients (p-value < 0.01) were identified and presented as well.

Results: We found that each cluster of patients typically had a most distinguishing adverse event. Examination of the co-occurrences of adverse events in patients led to the identification of potentially interesting adverse event correlations that may be further investigated as well as provide further patient stratification opportunities.

Conclusions: We have demonstrated the feasibility of a novel approach in pharmacovigilance to stratify patients based on fine-grained adverse event profiles, which also makes it possible to identify adverse event correlations. Used on larger data sets, this data-driven method has the potential to reveal unknown patterns concerning adverse event occurrences.

Defining a reference set to support methodological research in drug safety

BACKGROUND

Methodological research to evaluate the performance of methods requires a benchmark to serve as a referent comparison. In drug safety, the performance of analyses of spontaneous adverse event reporting databases and observational healthcare data, such as administrative claims and electronic health records, has been limited by the lack of such standards.

OBJECTIVES

To establish a reference set of test cases that contain both positive and negative controls, which can serve the basis for methodological research in evaluating methods performance in identifying drug safety issues.

RESEARCH DESIGN

Systematic literature review and natural language processing of structured product labeling was performed to identify evidence to support the classification of drugs as either positive controls or negative controls for four outcomes: acute liver injury, acute kidney injury, acute myocardial infarction, and upper gastrointestinal bleeding.

RESULTS

Three-hundred and ninety-nine test cases comprised of 165 positive controls and 234 negative controls were identified across the four outcomes. The majority of positive controls for acute kidney injury and upper gastrointestinal bleeding were supported by randomized clinical trial evidence, while the majority of positive controls for acute liver injury and acute myocardial infarction were only supported based on published case reports. Literature estimates for the positive controls shows substantial variability that limits the ability to establish a reference set with known effect sizes.

CONCLUSIONS

A reference set of test cases can be established to facilitate methodological research in drug safety. Creating a sufficient sample of drug-outcome pairs with binary classification of having no effect (negative controls) or having an increased effect (positive controls) is possible and can enable estimation of predictive accuracy through discrimination. Since the magnitude of the positive effects cannot be reliably obtained and the quality of evidence may vary across outcomes, assumptions are required to use the test cases in real data for purposes of measuring bias, mean squared error, or coverage probability.

The impact of drug and outcome prevalence on the feasibility and performance of analytical methods for a risk identification and analysis system

BACKGROUND

A systematic risk identification system has the potential to study all marketed drugs. However, the rates of drug exposure and outcome occurrences in observational databases, the database size and the desired risk detection threshold determine the power and therefore limit the feasibility of the application of appropriate analytical methods. Drugs vary dramatically for these parameters because of their prevalence of indication, cost, time on the market, payer formularies, market pressures and clinical guidelines.

OBJECTIVES

Evaluate (i) the feasibility of a risk identification system based on commercially available observational databases, (ii) the range of drugs that can be studied for certain outcomes, (iii) the influence of underpowered drug-outcome pairs on the performance of analytical methods estimating the strength of their association and (iv) the time required from the introduction of a new drug to accumulate sufficient data for signal detection.

METHODS

As part of the Observational Medical Outcomes Partnership experiment, we used data from commercially available observational databases and calculated the minimal detectable relative risk of all pairs of marketed drugs and eight health outcomes of interest. We then studied an array of analytical methods for their ability to distinguish between pre-determined positive and negative drug-outcome test pairs. The positive controls contained active ingredients with evidence of a positive association with the outcome, and the negative controls had no such evidence. As a performance measure we used the area under the receiver operator characteristics curve (AUC). We compared the AUC of methods using all test pairs or only pairs sufficiently powered for detection of a relative risk of 1.25. Finally, we studied all drugs introduced to the market in 2003-2008 and determined the time required to achieve the same minimal detectable relative risk threshold.

RESULTS

The performance of methods improved after restricting them to fully powered drug-outcome pairs. The availability of drug-outcome pairs with sufficient power to detect a relative risk of 1.25 varies enormously among outcomes. Depending on the market uptake, drugs can generate relevant signals in the first month after approval, or never reach sufficient power.

CONCLUSION

The incidence of drugs and important outcomes determines sample size and method performance in estimating drug-outcome associations. Careful consideration is therefore necessary to choose databases and outcome definitions, particularly for newly introduced drugs.

Combining multiple healthcare databases for postmarketing drug and vaccine safety surveillance: why and how?

A growing number of international initiatives (e.g. EU-ADR, Sentinel, OMOP, PROTECT and VAESCO) are based on the combined use of multiple healthcare databases for the conduct of active surveillance studies in the area of drug and vaccine safety. The motivation behind combining multiple healthcare databases is the earlier detection and validation, and hence earlier management, of potential safety issues. Overall, the combination of multiple healthcare databases increases statistical sample size and heterogeneity of exposure for postmarketing drug and vaccine safety surveillance, despite posing several technical challenges. Healthcare databases generally differ by underlying healthcare systems, type of information collected, drug/vaccine and medical event coding systems and language. Therefore, harmonization of medical data extraction through homogeneous coding algorithms across highly different databases is necessary. Although no standard procedure is currently available to achieve this, several approaches have been developed in recent projects. Another main challenge involves choosing the work models for data management and analyses whilst respecting country-specific regulations in terms of data privacy and anonymization. Dedicated software (e.g. Jerboa) has been produced to deal with privacy issues by sharing only anonymized and aggregated data using a common data model. Finally, storage and safe access to the data from different databases requires the development of a proper remote research environment. The aim of this review is to provide a summary of the potential, disadvantages, methodological issues and possible solutions concerning the conduct of postmarketing multidatabase drug and vaccine safety studies, as demonstrated by several international initiatives.

Illustration of the weibull shape parameter signal detection tool using electronic healthcare record data

BACKGROUND

The WSP tool has previously been proposed as a method to detect signals for adverse drug reactions utilising time-to-event data without the need for a reference population. The aim of this study was to assess the performance of the tool on two well-known and two suspected adverse drug reactions for bisphosphonates that varied in both frequency and accuracy of reporting time.

METHODS

The use of the WSP tool was investigated on data from a matched population cohort study involving data from UK primary care patients exposed to oral bisphosphonates. Four listed/suspected ADRs were selected for investigation: headache, musculoskeletal pain, alopecia and carpal tunnel syndrome. For each suspected ADR, a graphical exploratory analysis was performed and the WSP tool was applied for two censoring periods each.

RESULTS

Both of the well-known and common ADRs (headache and musculoskeletal pain) were detected using the WSP tool, and the signals were present regardless of the censoring intervals used. A signal was also detected when the event was uncommon and the timing was likely to be an accurate reflection of onset time (alopecia). This signal was only present for some of the censoring intervals. As anticipated, no signals were raised in the control groups for these events regardless of the censoring interval used. The suspected ADR, which was uncommon and where reporting times may not reflect onset time accurately (carpal tunnel syndrome), was not detected. A signal was raised in the control group but its false-positive nature was visible in the exploratory graphical analysis, which led to it (frequent but for only a limited number of consecutive dates).

CONCLUSION

This study illustrates the usability and examines the reliability of the WSP tool as a method for signal detection in electronic health records. When the events are uncommon the success of this method may depend on the reporting time accurately reflecting the true event onset time. The study has shown that further work is required to define the censoring periods. The addition of a control group is not required but may enhance causal inference by showing that other causes than the exposure may lead to a signal.

Computing limits on medicine risks based on collections of individual case reports

Background

Quantifying a medicine’s risks for adverse effects is crucial in assessing its value as a therapeutic agent. Rare adverse effects are often not detected until after the medicine is marketed and used in large and heterogeneous patient populations, and risk quantification is even more difficult. While individual case reports of suspected harm from medicines are instrumental in the detection of previously unknown adverse effects, they are currently not used for risk quantification. The aim of this article is to demonstrate how and when limits on medicine risks can be computed from collections of individual case reports.

Methods

We propose a model where drug exposures in the real world may be followed by adverse episodes, each containing one or several adverse effects. Any adverse episode can be reported at most once, and each report corresponds to a single adverse episode. Based on this model, we derive upper and lower limits for the per-exposure risk of an adverse effect for a given drug.

Results

An upper limit for the per-exposure risk of the adverse effect Y for a given drug X is provided by the reporting ratio of X together with Y relative to all reports on X, under two assumptions: (i) the average number of adverse episodes following exposure to X is one or less; and (ii) adverse episodes that follow X and contain Y are more frequently reported than adverse episodes in general that follow X. Further, a lower risk limit is provided by dividing the number of reports on X together with Y by the total number of exposures to X, under the assumption that exposures to X that are followed by Y generate on average at most one report on X together with Y. Using real data, limits for the narcolepsy risk following Pandemrix vaccination and the risk of coeliac disease following antihypertensive treatment were computed and found to conform to reference risk values from epidemiological studies.

Conclusions

Our framework enables quantification of medicine risks in situations where this is otherwise difficult or impossible. It has wide applicability, but should be particularly useful in structured benefit-risk assessments that include rare adverse effects.

Gathering and exploring scientific knowledge in pharmacovigilance

Pharmacovigilance plays a key role in the healthcare domain through the assessment, monitoring and discovery of interactions amongst drugs and their effects in the human organism. However, technological advances in this field have been slowing down over the last decade due to miscellaneous legal, ethical and methodological constraints. Pharmaceutical companies started to realize that collaborative and integrative approaches boost current drug research and development processes. Hence, new strategies are required to connect researchers, datasets, biomedical knowledge and analysis algorithms, allowing them to fully exploit the true value behind state-of-the-art pharmacovigilance efforts. This manuscript introduces a new platform directed towards pharmacovigilance knowledge providers. This system, based on a service-oriented architecture, adopts a plugin-based approach to solve fundamental pharmacovigilance software challenges. With the wealth of collected clinical and pharmaceutical data, it is now possible to connect knowledge providers' analysis and exploration algorithms with real data. As a result, new strategies allow a faster identification of high-risk interactions between marketed drugs and adverse events, and enable the automated uncovering of scientific evidence behind them. With this architecture, the pharmacovigilance field has a new platform to coordinate large-scale drug evaluation efforts in a unique ecosystem, publicly available at http://bioinformatics.ua.pt/euadr/.

Population-based analysis of non-steroidal anti-inflammatory drug use among children in four European countries in the SOS project: what size of data platforms and which study designs do we need to assess safety issues?

BACKGROUND

Data on utilization patterns and safety of non-steroidal anti-inflammatory drugs (NSAIDs) in children are scarce. The purpose of this study was to investigate the utilization of NSAIDs among children in four European countries as part of the Safety Of non-Steroidal anti-inflammatory drugs (SOS) project.

METHODS

We used longitudinal patient data from seven databases (GePaRD, IPCI, OSSIFF, Pedianet, PHARMO, SISR, and THIN) to calculate prevalence rates of NSAID use among children (0-18 years of age) from Germany, Italy, Netherlands, and United Kingdom. All databases contained a representative population sample and recorded demographics, diagnoses, and drug prescriptions. Prevalence rates of NSAID use were stratified by age, sex, and calendar time. The person-time of NSAID exposure was calculated by using the duration of the prescription supply. We calculated incidence rates for serious adverse events of interest. For these adverse events of interest, sample size calculations were conducted (alpha = 0.05; 1-beta = 0.8) to determine the amount of NSAID exposure time that would be required for safety studies in children.

RESULTS

The source population comprised 7.7 million children with a total of 29.6 million person-years of observation. Of those, 1.3 million children were exposed to at least one of 45 NSAIDs during observation time. Overall prevalence rates of NSAID use in children differed across countries, ranging from 4.4 (Italy) to 197 (Germany) per 1000 person-years in 2007. For Germany, United Kingdom, and Italian pediatricians, we observed high rates of NSAID use among children aged one to four years. For all four countries, NSAID use increased with older age categories for children older than 11. In this analysis, only for ibuprofen (the most frequently used NSAID), enough exposure was available to detect a weak association (relative risk of 2) between exposure and asthma exacerbation (the most common serious adverse event of interest).

CONCLUSIONS

Patterns of NSAID use in children were heterogeneous across four European countries. The SOS project platform captures data on more than 1.3 million children who were exposed to NSAIDs. Even larger data platforms and the use of advanced versions of case-only study designs may be needed to conclusively assess the safety of these drugs in children.