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

Optimizing large real-world data analysis with parquet files in R: A step-by-step tutorial

PURPOSE

The use of open-source programming languages can facilitate open science practices in real-world evidence (RWE) studies. Real-world studies often rely on using big data, which makes using such languages complicated. We demonstrate an efficient approach that enables RWE researchers to use R to undertake RWE analysis tasks from cohort building to reporting.

METHODS

Using the Merative Marketscan data (2017-2019), we developed an R function to transform the data into parquet format to be used in R. Then, we compared the differences in data size before and after transformation. We compared the performance of the transformed data in R to the original data in terms of numerical consistency and running times required to complete simple exploratory tasks. To show how the transformed databases can be used in practice, we conducted a simplified replication of an active comparator new user study from the literature. All codes are available on GitHub.

RESULTS

Our approach exhibited high efficiency in data storage, as evidenced by the converted data size, which ranged from 10% to 43% of that of the original data files. The runtime of the exploratory tasks in R generally outperformed that of the original data with SAS. We showed, through example, how the converted data can be efficiently used to implement an RWE study.

CONCLUSION

We demonstrate a free and efficient solution to facilitate the use of open-source programming languages with big real-world databases, which can facilitate the adoption of open science practices.

A Real-World Pharmacovigilance Study of Ceftazidime/Avibactam: Data Mining of the Food and Drug Administration Adverse Event Reporting System Database

Ceftazidime/avibactam (CAZ/AVI) is a combination of a well-known third-generation, broad-spectrum cephalosporin with a new beta-lactamase inhibitor that has been approved for the treatment of various infectious diseases (especially multidrug-resistant Gram-negative bacterial infections) by the Food and Drug Administration (FDA). The current study extensively assessed CAZ/AVI-related adverse events (AEs) in the real world through data mining of the FDA Adverse Event Reporting System (FAERS) database to better understand toxicities. The signals of CAZ/AVI-related AEs were quantified using disproportionality analyses, including the reporting odds ratio, the proportional reporting ratio, the Bayesian confidence propagation neural network, and the multi-item gamma Poisson shrinker algorithms. Out of 10,114,815 records retrieved from the FAERS database, 628 cases were identified, where CAZ/AVI was implicated as the primary suspect drug. A total of 61 preferred terms with significant disproportionality that simultaneously met the criteria of all four algorithms were retained. Several unexpected safety signals may also occur, including melena, hypernatremia, depressed level of consciousness, brain edema, petechiae, delirium, and shock hemorrhagic. The median onset time for AEs associated with CAZ/AVI was 4 days, with most cases occurring within 3 days after CAZ/AVI initiation.

Pharmacovigilance studies without a priori hypothesis: systematic review highlights inappropriate multiple testing correction procedures

OBJECTIVES

The purpose of this study was to systematically review the statistical methods used in pharmacovigilance studies without a priori hypotheses.

STUDY DESIGN AND SETTING

A systematic review was performed on studies published in the MEDLINE database between 2012 and 2021. The included studies were analyzed for database name and type, statistical methods, anatomical therapeutic chemical class for the studied drug(s), and SOC MedDRA classification for the studied adverse drug reaction.

RESULTS

Ninety-two studies were included, with pharmacovigilance databases being the most used type. Disproportionality analysis using frequentist or Bayesian methods was the most common statistical method employed. The most studied drug classes were anti-infectives, nervous system drugs, and antineoplastics and immunomodulators. However, no common procedure was implemented to correct for multiple testing.

CONCLUSION

This review highlights the limited number of statistical methods employed for pharmacovigilance studies without a priori hypotheses, with no established consensus-based method and a lack of interest in multiple testing correction. The establishment of guidelines is recommended to improve the performance of such studies.

Do peripheral neuropathies differ among immune checkpoint inhibitors? Reports from the European post-marketing surveillance database in the past 10 years

Although the immunotherapy advent has revolutionized cancer treatment, it, unfortunately, does not spare cancer patients from possible immune-related adverse events (irAEs), which can also involve the peripheral nervous system. Immune checkpoint inhibitors (ICIs), blocking cytotoxic T-lymphocyteassociated protein 4 (CTLA-4), programmed cell death protein 1 (PD-1), or programmed cell death ligand 1 (PD-L1), can induce an immune imbalance and cause different peripheral neuropathies (PNs). Considering the wide range of PNs and their high impact on the safety and quality of life for cancer patients and the availability of large post-marketing surveillance databases, we chose to analyze the characteristics of ICI-related PNs reported as suspected drug reactions from 2010 to 2020 in the European real-world context. We analyzed data collected in the European pharmacovigilance database, Eudravigilance, and conducted a systematic and disproportionality analysis. In our study, we found 735 reports describing 766 PNs occurred in patients treated with ICIs. These PNs included Guillain-Barré syndrome, Miller-Fisher syndrome, neuritis, and chronic inflammatory demyelinating polyradiculoneuropathy. These ADRs were often serious, resulting in patient disability or hospitalization. Moreover, our disproportionality analysis revealed an increased reporting frequency of PNs with tezolizumab compared to other ICIs. Guillain-Barré syndrome is a notable potential PN related to ICIs, as it is associated with a significant impact on patient safety and has had unfavorable outcomes, including a fatal one. Continued monitoring of the safety profile of ICIs in real-life settings is necessary, especially considering the increased frequency of PNs associated with atezolizumab compared with other ICIs.

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.

Generalised weibull model-based approaches to detect non-constant hazard to signal adverse drug reactions in longitudinal data

Pharmacovigilance is the process of monitoring the emergence of harm from a medicine once it has been licensed and is in use. The aim is to identify new adverse drug reactions (ADRs) or changes in frequency of known ADRs. The last decade has seen increased interest for the use of electronic health records (EHRs) in pharmacovigilance. The causal mechanism of an ADR will often result in the occurrence being time dependent. We propose identifying signals for ADRs based on detecting a variation in hazard of an event using a time-to-event approach. Cornelius et al. proposed a method based on the Weibull Shape Parameter (WSP) and demonstrated this to have optimal performance for ADRs occurring shortly after taking treatment or delayed ADRs, and introduced censoring at varying time points to increase performance for intermediate ADRs. We now propose two new approaches which combined perform equally well across all time periods. The performance of this new approach is illustrated through an EHR Bisphosphonates dataset and a simulation study. One new approach is based on the power generalised Weibull distribution (pWSP) introduced by Bagdonavicius and Nikulin alongside an extended version of the WSP test, which includes one censored dataset resulting in improved detection across time period (dWSP). In the Bisphosphonates example, the pWSP and dWSP tests correctly signalled two known ADRs, and signal one adverse event for which no evidence of association with the drug exist. A combined test involving both pWSP and dWSP is reliable independently of the time of occurrence of ADRs.

A pharmacovigilance approach for assessing the occurrence of suicide-related events induced by antiepileptic drugs using the Japanese adverse drug event report database

Increased suicidality after antiepileptic drug (AED) treatment remains controversial. This study aimed to investigate the occurrence of suicide-related events (SREs) in Japan. SREs signals with AEDs used orally were evaluated by calculating reporting odds ratios (RORs) and information components (ICs) using the Japanese Adverse Drug Event Report (JADER) database from April 2004 to December 2021. Additionally, factors affecting the occurrence of SREs and time-to-onset from the initial AED treatment were analyzed. Of 22 AEDs, 12 (perampanel hydrate, nitrazepam, levetiracetam, clonazepam, clobazam, sodium valproate, phenobarbital, lamotrigine, lacosamide, gabapentin, zonisamide, and carbamazepine) showed signals of SREs. Patients in their 20 and 30 s, female sex, and concomitant use of multiple AEDs affected the occurrence of SREs. In six AEDs, the median time-to-onset of SREs in patients taking all AEDs was <100 days. The pharmacovigilance approach revealed that several AEDs displayed suicidality signals. Female patients, those in their 20 and 30 s, undergoing combination therapy with ≥2 AEDs, and patients early (<100 days from the initial treatment) in the course of AED therapy should be cautioned about SREs.

Detection of unknown ototoxic adverse drug reactions: an electronic healthcare record-based longitudinal nationwide cohort analysis

Ototoxic medications can lead to significant morbidity. Thus, pre-marketing clinical trials have assessed new drugs that have ototoxic potential. Nevertheless, several ototoxic side effects of drugs may remain undetected. Hence, we sought to retrospectively investigate the potential risk of ototoxic adverse drug reactions among commonly used drugs via a longitudinal cohort study. An electronic health records-based data analysis with a propensity-matched comparator group was carried out. This study was conducted using the MetaNurse algorithm for standard nursing statements on electronic healthcare records and the National Sample Cohort obtained from the South Korea National Health Insurance Service. Five target drugs capable of causing ototoxic adverse drug reactions were identified using MetaNurse; two drugs were excluded after database-based analysis because of the absence of bilateral hearing loss events in patients. Survival analysis, log-rank test, and Cox proportional hazards regression models were used to calculate the incidence, survival rate, and hazard ratio of bilateral hearing loss among patients who were prescribed candidate ototoxic drugs. The adjusted hazard ratio of bilateral hearing loss was 1.31 (1.03–1.68), 2.20 (1.05–4.60), and 2.26 (1.18–4.33) in cimetidine, hydroxyzine, and sucralfate users, respectively. Our results indicated that hydroxyzine and sucralfate may cause ototoxic adverse drug reactions in patients. Thus, clinicians should consider avoiding co-administration of these drugs with other well-confirmed ototoxic drugs should be emphasized.

Safety issues and harmful pharmacological interactions of nutritional supplements in Duchenne muscular dystrophy: considerations for Standard of Care and emerging virus outbreaks

At the moment, little treatment options are available for Duchenne muscular dystrophy (DMD). The absence of the dystrophin protein leads to a complex cascade of pathogenic events in myofibres, including chronic inflammation and oxidative stress as well as altered metabolism. The attention towards dietary supplements in DMD is rapidly increasing, with the aim to counteract pathology-related alteration in nutrient intake, the consequences of catabolic distress or to enhance the immunological response of patients as nowadays for the COVID-19 pandemic emergency. By definition, supplements do not exert therapeutic actions, although a great confusion may arise in daily life by the improper distinction between supplements and therapeutic compounds. For most supplements, little research has been done and little evidence is available concerning their effects in DMD as well as their preventing actions against infections. Often these are not prescribed by clinicians and patients/caregivers do not discuss the use with their clinical team. Then, little is known about the real extent of supplement use in DMD patients. It is mistakenly assumed that, since compounds are of natural origin, if a supplement is not effective, it will also do no harm. However, supplements can have serious side effects and also have harmful interactions, in terms of reducing efficacy or leading to toxicity, with other therapies. It is therefore pivotal to shed light on this unclear scenario for the sake of patients. This review discusses the supplements mostly used by DMD patients, focusing on their potential toxicity, due to a variety of mechanisms including pharmacodynamic or pharmacokinetic interactions and contaminations, as well as on reports of adverse events. This overview underlines the need for caution in uncontrolled use of dietary supplements in fragile populations such as DMD patients. A culture of appropriate use has to be implemented between clinicians and patients' groups.

Integrating Regulatory Drug Label Information to Facilitate Evaluation of Adverse Events in Pharmacovigilance

Background:

Efficiency and accuracy for signal detection and evaluation activities are integral components of routine Pharmacovigilance (PV) practices. However, an Individual Case Safety Report (ICSR) may consist of a variety of confounders such as Concomitant Medications (CM), Past Medical History (PMH), and concurrent medical conditions that influence a safety officer’s evaluation of a potential Adverse Event (AE). Limited pharmacovigilance systems are currently available as a tool designed to enhance the efficiency and accuracy of signal detection and management.

Objective:

To introduce a systemic approach to make critical safety information readily available for users in order to discern possible interferences from CM and make informed decisions on the signal evaluation process – saving time while improving quality.

Methods:

Oracle Empirica Signal software was utilized to extract cases with CM that are Known Implicating Medications (KIM) for each AE according to public regulatory information from drug labels – FDA Structured Product Labeling (SPL) or EMA Summary of Product Characteristics (SPC). SAS Enterprise Guide was used to further process the data generated from Oracle Empirica Signal software.

Results:

For any target drug being evaluated for safety purposes, a KIM reference table can be generated, which summarizes all potential causality contributions from CMs.

Conclusion:

In addition to providing standalone KIM table as reference, adoption of this concept and automation may also be fully integrated into commercial signal detection and management software packages for easy use and accessibility and may even lead to reduced False Positive rate in signal detection within the PV space.

Nintedanib and ischemic colitis: Signal assessment with the integrated use of two types of real-world evidence, spontaneous reports of suspected adverse drug reactions, and observational data from large health-care databases

Purpose

Statistical screening of Vigibase, the global database of individual case safety reports, highlighted an association between the MedDRA Preferred Term (PT) “colitis” and nintedanib. Nintedanib is a protein kinase inhibitor authorized in accelerated regulatory procedures for the treatment of idiopathic pulmonary fibrosis (IPF). The aim of this report is to describe the integration of two types of real‐world evidence, spontaneous reports of adverse drug reactions (ADR), and observational health data (OHD) in the assessment of a post‐authorization safety signal of ischemic colitis.

Methods

Assessment of the statistical signal of “nintedanib – colitis” was undertaken using data from VigiBase, OHD from the Observational Heath Data Sciences and Informatics (OHDSI) collaborative, published literature, and openly available regulatory documents. Evidence synthesis was performed to support Bradford Hill criteria in causality assessment.

Results

Evidence for strength of association, specificity, consistency, and analogy was found upon review of the case series. OHD was used to calculate incidence rates of colitis in new users of nintedanib across multiple populations, supportive of consistency, and further evidence for strength of association. Literature review identified support for biological plausibility and analogy. Signal assessment was supplemented with characterization of real‐world users and exploration of potential risk factors using OHD.

Conclusions

An integrated approach using two forms of real‐world data, spontaneous reports of ADRs and data from observational databases allowed a comprehensive and efficient signal assessment of nintedanib and colitis. Further exploration of the complementary use of real‐time OHD in signal assessment could inform more efficient approaches to current signal management practices.

Assessment of factors associated with completeness of spontaneous adverse event reporting in the United States: A comparison between consumer reports and healthcare professional reports

What is known and objective

The objectives of this study were to explore completeness of direct adverse event (AE) reports from consumers and healthcare professionals (HCPs), and to discuss the reasons completeness varied among reporters with different occupations.

Methods

We used a total of 5475 direct AE reports to the United States (US) Food and Drug Administration (FDA) from the first and second quarters of 2016 and assessed completeness of basic information (eg, patient sex, age, weight) and information relevant to AEs (eg, suspect and concomitant drugs). Logistic regression analysis was conducted to evaluate the associations between report completeness and reporting backgrounds.

Results and discussion

The completeness of AE reports from consumers was generally greater than that of reports from HCPs. Completeness of specific items varied among different occupations, which may reflect accessibility to, and/or availability of, relevant information for each type of reporter. There was a clear association between the proportion of ‘known’ ADRs in a report and completeness, suggesting that consumers and HCPs are likely to consult labelling information when reporting AEs.

What is new and conclusion

The quality of AE reports seemed to depend on information costs accrued to potential reporters. Researchers should consider the impact of database heterogeneity and possible sample selection bias when using spontaneous AE reports as a sample of events in the United States.

From Big Data to Smart Data for Pharmacovigilance: The Role of Healthcare Databases and Other Emerging Sources

In the last decade 'big data' has become a buzzword used in several industrial sectors, including but not limited to telephony, finance and healthcare. Despite its popularity, it is not always clear what big data refers to exactly. Big data has become a very popular topic in healthcare, where the term primarily refers to the vast and growing volumes of computerized medical information available in the form of electronic health records, administrative or health claims data, disease and drug monitoring registries and so on. This kind of data is generally collected routinely during administrative processes and clinical practice by different healthcare professionals: from doctors recording their patients' medical history, drug prescriptions or medical claims to pharmacists registering dispensed prescriptions. For a long time, this data accumulated without its value being fully recognized and leveraged. Today big data has an important place in healthcare, including in pharmacovigilance. The expanding role of big data in pharmacovigilance includes signal detection, substantiation and validation of drug or vaccine safety signals, and increasingly new sources of information such as social media are also being considered. The aim of the present paper is to discuss the uses of big data for drug safety post-marketing assessment.

Time Series Disturbance Detection for Hypothesis-Free Signal Detection in Longitudinal Observational Databases

INTRODUCTION

Signal detection remains a cornerstone activity of pharmacovigilance. Routine quantitative signal detection primarily focuses on screening of spontaneous reports. In striving to enhance quantitative signal detection capability further, other data streams are being considered for their potential contribution as sources of emerging signals, one of which is longitudinal observational databases, including electronic medical record (EMR) and transactional insurance claims databases. Quantitative signal detection on such databases is a nascent field-with published methods being primarily based either on individual metrics, which may not effectively represent the complexity of the longitudinal records and their necessary variation for analysis for drug-outcome pairs, or on visualization discovery approaches leveraging multiple aspects of the records, which are not particularly tractable to high-throughput hypothesis-free signal detection. One extensively tested example of the latter is chronographs.

METHODS

We apply a disturbance detection algorithm to chronographs using UK EMR The Health Improvement Network (THIN) data. The algorithm utilizes autoregressive integrated moving average (ARIMA)-based time series methodology designed to find disturbances that occur outside the normal pattern trends of the ARIMA model for the chronograph. Chronographs currently highlight drug-event pairs as potentially worthy of further clinical assessment, via filter-based increases in disproportionality scores from before to after the index drug exposure, tested across a range of case and control windows.

RESULTS

We replicate the findings on six exemplar chronographs from a previous publication, and show how disturbances can be effectively detected across this set of pairs. Further, 692 disturbances were detected in analysis of all 384 individual READ code outcomes ever recorded 50 or more times for patients prescribed sibutramine. The disturbances are algorithmically further broken into subsets of clinical interest.

CONCLUSION

Overall, the disturbance algorithm approach shows promising capacity for detecting outliers, and shows tractability of the algorithmic approach for large-scale screening. The method offers an array of pattern types for detection and clinical review.

New safety signals assessed by the Pharmacovigilance Risk Assessment Committee at EU level in 2014-2017

BACKGROUND

Safety monitoring of all drugs throughout their entire life cycle is mandatory in order to protect the public health. Our objective was to describe all new safety signals assessed at EU level by the Pharmacovigilance Risk Assessment Committee (PRAC).

METHODS

Publicly available data on signals assessment from PRAC meeting minutes for the period January 2014-November 2017 were analyzed and classified.

RESULTS

A total of 239 new signals for 194 drugs/drug combinations/therapeutic classes were evaluated by PRAC. A total of 154 signals were triggered by spontaneous reporting, 31 by literature case reports, and 26 by observational studies. In 188 signals, the drugs involved were authorized for more than 5 years. The drug classes for which most signals were detected were antineoplastic/immunomodulators (n = 75), anti-infectives (n = 34), and drugs acting on the nervous system (n = 27). Signals were triggered for drug interactions (n = 15), in utero exposure (n = 7), medication errors (n = 6), and for different disorders, among which the skin/subcutaneous tissue disorders were more common. PRAC recommendations consisted in label updates (n = 86), in Direct Healthcare Professional Communications (n = 17), and in eight recommendations for a more complex evaluation through referral procedures.

CONCLUSIONS

Most new signals assessed were triggered by spontaneous reporting and led to routine risk minimization measures, such as updating the product information.

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.

Sources of Safety Data and Statistical Strategies for Design and Analysis: Postmarket Surveillance

BACKGROUND

Safety data are continuously evaluated throughout the life cycle of a medical product to accurately assess and characterize the risks associated with the product. The knowledge about a medical product's safety profile continually evolves as safety data accumulate.

METHODS

This paper discusses data sources and analysis considerations for safety signal detection after a medical product is approved for marketing. This manuscript is the second in a series of papers from the American Statistical Association Biopharmaceutical Section Safety Working Group.

RESULTS

We share our recommendations for the statistical and graphical methodologies necessary to appropriately analyze, report, and interpret safety outcomes, and we discuss the advantages and disadvantages of safety data obtained from passive postmarketing surveillance systems compared to other sources.

CONCLUSIONS

Signal detection has traditionally relied on spontaneous reporting databases that have been available worldwide for decades. However, current regulatory guidelines and ease of reporting have increased the size of these databases exponentially over the last few years. With such large databases, data-mining tools using disproportionality analysis and helpful graphics are often used to detect potential signals. Although the data sources have many limitations, analyses of these data have been successful at identifying safety signals postmarketing. Experience analyzing these dynamic data is useful in understanding the potential and limitations of analyses with new data sources such as social media, claims, or electronic medical records data.

Clinical epidemiology in the era of big data: new opportunities, familiar challenges

Routinely recorded health data have evolved from mere by-products of health care delivery or billing into a powerful research tool for studying and improving patient care through clinical epidemiologic research. Big data in the context of epidemiologic research means large interlinkable data sets within a single country or networks of multinational databases. Several Nordic, European, and other multinational collaborations are now well established. Advantages of big data for clinical epidemiology include improved precision of estimates, which is especially important for reassuring (“null”) findings; ability to conduct meaningful analyses in subgroup of patients; and rapid detection of safety signals. Big data will also provide new possibilities for research by enabling access to linked information from biobanks, electronic medical records, patient-reported outcome measures, automatic and semiautomatic electronic monitoring devices, and social media. The sheer amount of data, however, does not eliminate and may even amplify systematic error. Therefore, methodologies addressing systematic error, clinical knowledge, and underlying hypotheses are more important than ever to ensure that the signal is discernable behind the noise.

Social Media Listening for Routine Post-Marketing Safety Surveillance

INTRODUCTION

Post-marketing safety surveillance primarily relies on data from spontaneous adverse event reports, medical literature, and observational databases. Limitations of these data sources include potential under-reporting, lack of geographic diversity, and time lag between event occurrence and discovery. There is growing interest in exploring the use of social media ('social listening') to supplement established approaches for pharmacovigilance. Although social listening is commonly used for commercial purposes, there are only anecdotal reports of its use in pharmacovigilance. Health information posted online by patients is often publicly available, representing an untapped source of post-marketing safety data that could supplement data from existing sources.

OBJECTIVES

The objective of this paper is to describe one methodology that could help unlock the potential of social media for safety surveillance.

METHODS

A third-party vendor acquired 24 months of publicly available Facebook and Twitter data, then processed the data by standardizing drug names and vernacular symptoms, removing duplicates and noise, masking personally identifiable information, and adding supplemental data to facilitate the review process. The resulting dataset was analyzed for safety and benefit information.

RESULTS

In Twitter, a total of 6,441,679 Medical Dictionary for Regulatory Activities (MedDRA(®)) Preferred Terms (PTs) representing 702 individual PTs were discussed in the same post as a drug compared with 15,650,108 total PTs representing 946 individual PTs in Facebook. Further analysis revealed that 26 % of posts also contained benefit information.

CONCLUSION

Social media listening is an important tool to augment post-marketing safety surveillance. Much work remains to determine best practices for using this rapidly evolving data source.

Exploiting heterogeneous publicly available data sources for drug safety surveillance: computational framework and case studies

OBJECTIVE

Driven by the need of pharmacovigilance centres and companies to routinely collect and review all available data about adverse drug reactions (ADRs) and adverse events of interest, we introduce and validate a computational framework exploiting dominant as well as emerging publicly available data sources for drug safety surveillance.

METHODS

Our approach relies on appropriate query formulation for data acquisition and subsequent filtering, transformation and joint visualization of the obtained data. We acquired data from the FDA Adverse Event Reporting System (FAERS), PubMed and Twitter. In order to assess the validity and the robustness of the approach, we elaborated on two important case studies, namely, clozapine-induced cardiomyopathy/myocarditis versus haloperidol-induced cardiomyopathy/myocarditis, and apixaban-induced cerebral hemorrhage.

RESULTS

The analysis of the obtained data provided interesting insights (identification of potential patient and health-care professional experiences regarding ADRs in Twitter, information/arguments against an ADR existence across all sources), while illustrating the benefits (complementing data from multiple sources to strengthen/confirm evidence) and the underlying challenges (selecting search terms, data presentation) of exploiting heterogeneous information sources, thereby advocating the need for the proposed framework.

CONCLUSIONS

This work contributes in establishing a continuous learning system for drug safety surveillance by exploiting heterogeneous publicly available data sources via appropriate support tools.

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.

Impact on Drug Safety of Variation in Adherence: The Need for Routinely Reporting Measures of Dose Intensity in Medication Safety Studies Using Electronic Health Data

Randomized controlled trials always report the dose assessed and usually include a measure of adherence. By comparison, observational studies assessing medication safety often fail to report the dose used and rarely report any measure of adherence to therapy. This limits the ability to control for differences in doses used when undertaking meta-analyses. Non-adherence with therapy is common in the practice setting and varies across countries and settings. Inter-country differences in the registration of medicines may also result in different product strengths being available in different countries. These two factors combined means that observational studies undertaken for the same medicine in different settings may be assessing the same medicine but in circumstances where quite different dosages are used. Given that many adverse drug effects are dose dependent, differences in dosages used could be a factor explaining differences in risk estimates observed across studies. We argue that dose intensity, which can be defined as a product of the dose prescribed and adherence to the dose prescribed over the course of treatment, should be routinely reported in observational studies of medication safety. We illustrate the issue with the example of dabigatran. The randomized controlled trial evidence underpinning dabigatran’s marketing authorization resulted in uncertainty about the appropriate dose for efficacy versus safety. As a result, different dosages of dabigatran were registered in the USA and Europe. The USA registered the 150- and 75-mg dabigatran products, while the 150- and 110-mg dabigatran products were registered in Europe. Among five observational studies subsequently undertaken to resolve the safety question concerning dabigatran and risk of bleeding, only one stratified results by dose. None of the US studies stratified results by the 75-mg dabigatran dose, despite this dose not being assessed in the original trial. None of the five studies reported adherence measures, despite three separate observational studies finding between 25 and 40 % of patients were non-adherent to dabigatran. The STROBE and RECORD statements should consider adding the requirement for reporting measures of dose intensity and its component products to improve observational study reports.