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

A Comparison of Active Pharmacovigilance Strategies Used to Monitor Adverse Events to Antiviral Agents: A Systematic Review

INTRODUCTION

The safety of antiviral agents in real-world clinical settings is crucial, as pre-marketing studies often do not capture all adverse events (AE). Active pharmacovigilance strategies are essential for detecting and characterising these AE comprehensively.

OBJECTIVE

The aim of this study was to identify and characterise active pharmacovigilance strategies used in real-world clinical settings for patients under systemic antiviral agents, focusing on the frequency of AE and the clinical data sources used.

METHODS

We conducted a systematic review by searching three electronic bibliographic databases targeting observational prospective active pharmacovigilance studies, phase IV clinical trials for post-marketing safety surveillance, and interventional studies assessing active pharmacovigilance strategies, focusing on individuals exposed to systemic antiviral agents.

RESULTS

We included 36 primary studies, predominantly using Drug Event Monitoring (DEM), with a minority employing sentinel sites and registries. Human immunodeficiency virus (HIV) was the most common condition, with the majority using DEM. Within the DEM, there was a wide range of incidences of patients experiencing at least one AE, and most of these studies used one or two data sources. Sentinel site studies were less common, with two on hepatitis C virus (HCV) and one on HIV, each relying on one or two data sources. The single study using a registry focusing on HIV therapy reported using just one data source. Patient interviews were the most common data source, followed by medical records and laboratory tests. The quality of the studies was considered 'good' in 18/36, 'fair' in 1/36, and 'poor' in 17/36 studies.

CONCLUSION

DEM was the predominant pharmacovigilance strategy, employing multiple data sources, and appears to increase the likelihood of detecting higher AE incidence. Establishing such a framework would facilitate a more detailed and consistent approach across different studies and settings.

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.

Use of Electronic Health Record Data for Drug Safety Signal Identification: A Scoping Review

INTRODUCTION

Pharmacovigilance programs protect patient health and safety by identifying adverse event signals through postmarketing surveillance of claims data and spontaneous reports. Electronic health records (EHRs) provide new opportunities to address limitations of traditional approaches and promote discovery-oriented pharmacovigilance.

METHODS

To evaluate the current state of EHR-based medication safety signal identification, we conducted a scoping literature review of studies aimed at identifying safety signals from routinely collected patient-level EHR data. We extracted information on study design, EHR data elements utilized, analytic methods employed, drugs and outcomes evaluated, and key statistical and data analysis choices.

RESULTS

We identified 81 eligible studies. Disproportionality methods were the predominant analytic approach, followed by data mining and regression. Variability in study design makes direct comparisons difficult. Studies varied widely in terms of data, confounding adjustment, and statistical considerations.

CONCLUSION

Despite broad interest in utilizing EHRs for safety signal identification, current efforts fail to leverage the full breadth and depth of available data or to rigorously control for confounding. The development of best practices and application of common data models would promote the expansion of EHR-based pharmacovigilance.

Challenges and opportunities for mining adverse drug reactions: perspectives from pharma, regulatory agencies, healthcare providers and consumers

Monitoring drug safety is a central concern throughout the drug life cycle. Information about toxicity and adverse events is generated at every stage of this life cycle, and stakeholders have a strong interest in applying text mining and artificial intelligence (AI) methods to manage the ever-increasing volume of this information. Recognizing the importance of these applications and the role of challenge evaluations to drive progress in text mining, the organizers of BioCreative VII (Critical Assessment of Information Extraction in Biology) convened a panel of experts to explore 'Challenges in Mining Drug Adverse Reactions'. This article is an outgrowth of the panel; each panelist has highlighted specific text mining application(s), based on their research and their experiences in organizing text mining challenge evaluations. While these highlighted applications only sample the complexity of this problem space, they reveal both opportunities and challenges for text mining to aid in the complex process of drug discovery, testing, marketing and post-market surveillance. Stakeholders are eager to embrace natural language processing and AI tools to help in this process, provided that these tools can be demonstrated to add value to stakeholder workflows. This creates an opportunity for the BioCreative community to work in partnership with regulatory agencies, pharma and the text mining community to identify next steps for future challenge evaluations.

Evaluating the performance of temporal pattern discovery: new application using statins and rhabdomyolysis in OMOP databases

Background

Temporal pattern discovery (TPD) is a method of signal detection using electronic healthcare databases, serving as an alternative to spontaneous reporting of adverse drug events. Here, we aimed to replicate and optimise a TPD approach previously used to assess temporal signals of statins with rhabdomyolysis (in The Health Improvement Network (THIN) database) by using the OHDSI tools designed for OMOP data sources.

Methods

We used data from the Truven MarketScan US Commercial Claims and the Commercial Claims and Encounters (CCAE). Using an extension of the OHDSI ICTemporalPatternDiscovery package, we ran positive and negative controls through four analytical settings and calculated sensitivity, specificity, bias and AUC to assess performance.

Results

Similar to previous findings, we noted an increase in the Information Component (IC) for simvastatin and rhabdomyolysis following initial exposure and throughout the surveillance window. For example, the change in IC was 0.266 for the surveillance period of 1–30 days as compared to the control period of − 180 to − 1 days. Our modification of the existing OHDSI software allowed for faster queries and more efficient generation of chronographs.

Conclusion

Our OMOP replication matched the we can account forwe can account for of the original THIN study, only simvastatin had a signal. The TPD method is a useful signal detection tool that provides a single statistic on temporal association and a graphical depiction of the temporal pattern of the drug outcome combination. It remains unclear if the method works well for rare adverse events, but it has been shown to be a useful risk identification tool for longitudinal observational databases. Future work should compare the performance of TPD with other pharmacoepidemiology methods and mining techniques of signal detection. In addition, it would be worth investigating the relative TPD performance characteristics using a variety of observational data sources.

Automation in signal management in pharmacovigilance-an insight

Drugs are the imperial part of modern society, but along with their therapeutic effects, drugs can also cause adverse effects, which can be mild to morbid. Pharmacovigilance is the process of collection, detection, assessment, monitoring and prevention of adverse drug events in both clinical trials as well as in the post-marketing phase. The recent trends in increasing unknown adverse events, known as signals, have raised the need to develop an ideal system for monitoring and detecting the potential signals timely. The process of signal management comprises of techniques to identify individual case safety reports systematically. Automated signal detection is highly based upon the data mining of the spontaneous reporting system such as reports from health care professional, observational studies, medical literature or from social media. If a signal is not managed properly, it can become an identical risk associated with the drug which can be hazardous for the patient safety and may have fatal outcomes which may impact health care system adversely. Once a signal is detected quantitatively, it can be further processed by the signal management team for the qualitative analysis and further evaluations. The main components of automated signal detection are data extraction, data acquisition, data selection, and data analysis and data evaluation. This system must be developed in the correct format and context, which eventually emphasizes the quality of data collected and leads to the optimal decision-making based upon the scientific evaluation.

Adverse drug event presentation and tracking (ADEPT): semiautomated, high throughput pharmacovigilance using real-world data

Objective

To advance use of real-world data (RWD) for pharmacovigilance, we sought to integrate a high-sensitivity natural language processing (NLP) pipeline for detecting potential adverse drug events (ADEs) with easily interpretable output for high-efficiency human review and adjudication of true ADEs.

Materials and methods

The adverse drug event presentation and tracking (ADEPT) system employs an open source NLP pipeline to identify in clinical notes mentions of medications and signs and symptoms potentially indicative of ADEs. ADEPT presents the output to human reviewers by highlighting these drug-event pairs within the context of the clinical note. To measure incidence of seizures associated with sildenafil, we applied ADEPT to 149 029 notes for 982 patients with pediatric pulmonary hypertension.

Results

Of 416 patients identified as taking sildenafil, NLP found 72 [17%, 95% confidence interval (CI) 14–21] with seizures as a potential ADE. Upon human review and adjudication, only 4 (0.96%, 95% CI 0.37–2.4) patients with seizures were determined to have true ADEs. Reviewers using ADEPT required a median of 89 s (interquartile range 57–142 s) per patient to review potential ADEs.

Discussion

ADEPT combines high throughput NLP to increase sensitivity of ADE detection and human review, to increase specificity by differentiating true ADEs from signs and symptoms related to comorbidities, effects of other medications, or other confounders.

Conclusion

ADEPT is a promising tool for creating gold standard, patient-level labels for advancing NLP-based pharmacovigilance. ADEPT is a potentially time savings platform for computer-assisted pharmacovigilance based on RWD.

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.

Key Elements in Adverse Drug Reactions Safety Signals: Application of Legal Strategies

Adverse drug reactions, or unintended and harmful outcomes related to the administration of a pharmaceutical product, are a major public health concern, particularly for cancer patients. If counted as a separate cause of death, adverse drug reactions would represent the fourth leading cause of death in the United States. Several legal strategies are available to help mitigate their occurrences and to compensate victims for the harm that results from adverse events. Prior to FDA approval of a drug, the limited size and duration of clinical trials often fail to detect adverse drug reactions. However, after FDA approval, pharmacovigilance efforts are bolstered by recent expansions of FDA post-marketing regulatory powers codified in the 2007 Food and Drug Administration Amendments Act, as well as advances in big data analytics that improve adverse signal detection through data mining of large electronic health records. For victims of adverse drug reactions, tort lawsuits filed in the courts help compensate for the harm suffered and may also serve as warnings to manufacturers to improve drug safety to avoid future legal liability. While encouraging developments have occurred, new and existing legal structures to mitigate and compensate for adverse drug reactions must continue to be refined given increasingly complex pharmaceutical agents.

Evaluation of Electronic Healthcare Databases for Post-Marketing Drug Safety Surveillance and Pharmacoepidemiology in China

INTRODUCTION

Electronic healthcare databases (EHDs) are used increasingly for post-marketing drug safety surveillance and pharmacoepidemiology in Europe and North America. However, few studies have examined the potential of these data sources in China.

METHODS

Three major types of EHDs in China (i.e., a regional community-based database, a national claims database, and an electronic medical records [EMR] database) were selected for evaluation. Forty core variables were derived based on the US Mini-Sentinel (MS) Common Data Model (CDM) as well as the data features in China that would be desirable to support drug safety surveillance. An email survey of these core variables and eight general questions as well as follow-up inquiries on additional variables was conducted. These 40 core variables across the three EHDs and all variables in each EHD along with those in the US MS CDM and Observational Medical Outcomes Partnership (OMOP) CDM were compared for availability and labeled based on specific standards.

RESULTS

All of the EHDs' custodians confirmed their willingness to share their databases with academic institutions after appropriate approval was obtained. The regional community-based database contained 1.19 million people in 2015 with 85% of core variables. Resampled annually nationwide, the national claims database included 5.4 million people in 2014 with 55% of core variables, and the EMR database included 3 million inpatients from 60 hospitals in 2015 with 80% of core variables. Compared with MS CDM or OMOP CDM, the proportion of variables across the three EHDs available or able to be transformed/derived from the original sources are 24-83% or 45-73%, respectively.

CONCLUSIONS

These EHDs provide potential value to post-marketing drug safety surveillance and pharmacoepidemiology in China. Future research is warranted to assess the quality and completeness of these EHDs or additional data sources in China.

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.

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.