Drug Safety Surveillance Research Articles

Assessing performance of sequential analysis methods for active drug safety surveillance using observational data

ABSTRACTThe routine use of sequential methods is well established in clinical studies. Recently, there has been increasing interest in applying these methods to prospectively monitor the safety of newly approved drugs through accrual of real-world data. However, the application to marketed drugs using real-world data has been limited and work is needed to determine which sequential approaches are most suited to such data. In this study, the conditional sequential sampling procedure (CSSP), a group sequential method, was compared with a log-linear model with Poisson distribution (LLMP) through a SAS procedure (PROC GENMOD) combined with an alpha-spending function on two large longitudinal US administrative health claims databases. Relative performance in identifying known drug–outcome associations was examined using a set of 50 well-studied drug–outcome pairs. The study finds that neither method correctly identified all pairs but that LLMP often provides better ability and shorter time for identifying the known drug–outcome associations with superior computational performance when compared with CSSP, albeit with more false positives. With the features of flexible confounding control and ease of implementation, LLMP may be a good alternative or complement to CSSP.

Toward multimodal signal detection of adverse drug reactions

ObjectiveImproving mechanisms to detect adverse drug reactions (ADRs) is key to strengthening post-marketing drug safety surveillance. Signal detection is presently unimodal, relying on a single information source. Multimodal signal detection is based on jointly analyzing multiple information sources. Building on, and expanding the work done in prior studies, the aim of the article is to further research on multimodal signal detection, explore its potential benefits, and propose methods for its construction and evaluation. Material and methodsFour data sources are investigated; FDA’s adverse event reporting system, insurance claims, the MEDLINE citation database, and the logs of major Web search engines. Published methods are used to generate and combine signals from each data source. Two distinct reference benchmarks corresponding to well-established and recently labeled ADRs respectively are used to evaluate the performance of multimodal signal detection in terms of area under the ROC curve (AUC) and lead-time-to-detection, with the latter relative to labeling revision dates. ResultsLimited to our reference benchmarks, multimodal signal detection provides AUC improvements ranging from 0.04 to 0.09 based on a widely used evaluation benchmark, and a comparative added lead-time of 7–22 months relative to labeling revision dates from a time-indexed benchmark. ConclusionsThe results support the notion that utilizing and jointly analyzing multiple data sources may lead to improved signal detection. Given certain data and benchmark limitations, the early stage of development, and the complexity of ADRs, it is currently not possible to make definitive statements about the ultimate utility of the concept. Continued development of multimodal signal detection requires a deeper understanding the data sources used, additional benchmarks, and further research on methods to generate and synthesize signals.

Seizur-Like Activity and Recurrent Cardiac Arrests in a Healthy 24-Year-Old: Loperamide Abuse; a Case Report and Literature Review

SESSION TITLE: Poisoning and Drug Overdose 2 SESSION TYPE: Affiliate Case Report Poster PRESENTED ON: Tuesday, October 31, 2017 at 01:30 PM - 02:30 PM INTRODUCTION: Loperamide is a relatively safe OTC anti-motility agent. However, at higher doses it can produce palliation for opiate withdrawal or even frank euphoria. We present an atypical case of loperamide toxicity with seizure like activity and recurrent cardiac arrests, treated successfully with Isoproterenol. CASE PRESENTATION: 24-year-old female transferred for seizure like activity. Her hospital stay was complicated with recurrent cardiac arrests, including pulseless electrical activity (n=1) and subsequently ventricular tachycardia (n=2) successfully resuscitated with standard ACLS protocol. Workup was remarkable for prolonged QTc at 645 milliseconds with no electrolyte imbalances, cardiac enzymes, negative CT brain, video electroencephalogram, and lumbar puncture. Coronary CT confirmed normal cardiac and coronary anatomy. Transthoracic echocardiogram revealed normal structure and function. Trial of IV Isoproterenol achieved normalization of QTc. DISCUSSION: Loperamide is a μ-opioid receptor agonist with poor oral bioavailability, limited GI absorption, and is metabolized by the liver. At super high doses, it is actively transported through the blood brain barrier via an efflux transporter. Our patient reported one year history of loperamide abuse and was treated successfully with isoproterenol infusion making ours the second case reported. The pro-arrhythmic mechanisms of loperamide induced dysrhythmia has been studied using cloned human cardiac sodium and potassium channels. Loperamide inhibits both the Nav1.5 sodium channel and especially the human ether-a-go-go-related gene potassium channel. This study attributed the QRS prolongation to the inhibition of the sodium channel and further devolution into Torsades to the potassium channel inhibition. CONCLUSIONS: Loperamide is increasingly abused for treatment of opioid withdrawal and euphoria. Because it is available without prescription, is not routinely tested for, and does not have a classic clinical presentation, diagnosis of loperamide overdose requires heightened clinical suspicion. Despite an FDA warning, loperamide abuse is on the rise. Therefore, general population and clinicians should be more attuned to the potential of cardiac toxicity, QTc prolongation, and death from loperamide abuse. Further studies need to be done focused on elucidating optimal treatment strategies and improved initial diagnostics. Reference #1: Swank, K et al. (2017). Adverse event detection using the FDA post-marketing drug safety surveillance system: Cardiotoxicity associated with loperamide abuse and misuse. Journal of the American Pharmacists Association, Vol 10 Reference #2: Eggleston, W et al. (2016). Cardiac Dysrhythmias After Loperamide Abuse - New York 2008 - 2016. Morbidity and Mortality Weekly Report, Vol 65 Reference #3: Kang, J; Compton, D; Vas, R; Rampe, D (2016). Proarrhythmic mechanisms of the common anti-diarrheal medication loperamide; revalations from the opioid abuse epidemic. Naunyn-Schmiedeberg’s Archives of Pharmacology, Vol 389 DISCLOSURE: The following authors have nothing to disclose: Abhinav Mittal, Rahul Sangani, Monica Cerone, Robert Stansbury No Product/Research Disclosure Information

Type I error probability spending for post-market drug and vaccine safety surveillance with binomial data.

Type I error probability spending functions are commonly used for designing sequential analysis of binomial data in clinical trials, but it is also quickly emerging for near-continuous sequential analysis of post-market drug and vaccine safety surveillance. It is well known that, for clinical trials, when the null hypothesis is not rejected, it is still important to minimize the sample size. Unlike in post-market drug and vaccine safety surveillance, that is not important. In post-market safety surveillance, specially when the surveillance involves identification of potential signals, the meaningful statistical performance measure to be minimized is the expected sample size when the null hypothesis is rejected. The present paper shows that, instead of the convex Type I error spending shape conventionally used in clinical trials, a concave shape is more indicated for post-market drug and vaccine safety surveillance. This is shown for both, continuous and group sequential analysis.

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

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. 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. 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 1.19million people in 2015 with 85% of core variables. Resampled annually nationwide, the national claims database included 5.4million people in 2014 with 55% of core variables, and the EMR database included 3million 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. 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.

Type I Error Probability Spending for Post-Market Drug and Vaccine Safety Surveillance With Poisson Data.

Statistical sequential hypothesis testing is meant to analyze cumulative data accruing in time. The methods can be divided in two types, group and continuous sequential approaches, and a question that arises is if one approach suppresses the other in some sense. For Poisson stochastic processes, we prove that continuous sequential analysis is uniformly better than group sequential under a comprehensive class of statistical performance measures. Hence, optimal solutions are in the class of continuous designs. This paper also offers a pioneer study that compares classical Type I error spending functions in terms of expected number of events to signal. This was done for a number of tuning parameters scenarios. The results indicate that a log-exp shape for the Type I error spending function is the best choice in most of the evaluated scenarios.

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

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. 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. 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. 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). 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.

Analysis of the National Pharmacovigilance Database in Jordan (2010-2014)

Introduction Pharmacovigilance (PV) also known as drug safety surveillance is the science of enhancing patient safety through collecting, monitoring, assessing and preventing of adverse drug reactions (ADRs) 1. The objectives of PV are to improve public health and safety in relation to the use of

Contribution of industry funded post-marketing studies to drug safety: survey of notifications submitted to regulatory agencies

Objectives To investigate the practice of post-marketing studies in Germany during a three year period and to evaluate whether these trials meet the aims specified in the German Medicinal Products...

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

Drug-drug interaction (DDI) is of serious concern, causing over 30% of all adverse drug reactions and resulting in significant morbidity and mortality. Early discovery of adverse DDI is critical to prevent patient harm. Spontaneous reporting systems have been a major resource for drug safety surveillance that routinely collects adverse event reports from patients and healthcare professionals. In this study, we present a novel approach to discover DDIs from the Food and Drug Administration's adverse event reporting system. Data-driven discovery of DDI is an extremely challenging task because higher-order associations require analysis of all combinations of drugs and adverse events and accurate estimate of the relationships between drug combinations and adverse event require cause-and-effect inference. To efficiently identify causal relationships, we introduce the causal concept into association rule mining by developing a method called Causal Association Rule Discovery (CARD). The properties of V-structures in Bayesian Networks are utilized in the search for causal associations. To demonstrate feasibility, CARD is compared to the traditional association rule mining (AR) method in DDI identification. Based on physician evaluation of 100 randomly selected higher-order associations generated by CARD and AR, CARD is demonstrated to be more accurate in identifying known drug interactions compared to AR, 20% vs. 10% respectively. Moreover, CARD yielded a lower number of drug combinations that are unknown to interact, i.e., 50% for CARD and 79% for AR. Evaluation analysis demonstrated that CARD is more likely to identify true causal drug variables and associations to adverse event.

Adverse event detection using the FDA post-marketing drug safety surveillance system: Cardiotoxicity associated with loperamide abuse and misuse

ObjectiveThe purpose of this investigation was to identify and characterize post-marketing reports of cardiotoxicity, including torsades de pointes (TdP), associated with loperamide use. MethodsWe searched the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database for post-marketing reports of serious cardiac adverse events associated with loperamide use from December 28, 1976 (U.S. drug approval date), through December 14, 2015. We also conducted a Pubmed and Google Scholar search to identify additional published reports of cardiotoxicity associated with loperamide in the medical literature through February 11, 2016. ResultsForty-eight cases of serious cardiac adverse events associated with loperamide use composed the case series. The most frequently reported cardiac adverse events were syncope (n = 24), cardiac arrest (n = 13), QT-interval prolongation (n = 13), ventricular tachycardia (n = 10), and TdP (n = 7). There were 10 cases that resulted in death. Of the 48 cases, the most commonly reported reasons for use can be characterized as drug abuse (n = 22) and diarrhea treatment (n = 17). More than one-half of the 48 cases were reported after 2010. Of the 22 drug abuse cases, the median daily dose was 250 mg (range 70 mg to 1600 mg) and events occurred as early as 6 hours after a dose and as long as 18 months after initiation of loperamide. Thirteen of the 22 cases reported using loperamide for euphoric or analgesic effects, and 9 reported use to prevent opioid withdrawal symptoms. ConclusionThe FAERS case reports provide evidence to suggest that high doses of loperamide are associated with TdP and other serious cardiac adverse events. The majority of cases in this series occurred in the setting of drug abuse for the purpose of preventing opioid withdrawal or to produce euphoric effects. It is important for both clinicians and patients to be aware of this potential risk, because prompt therapy and discontinuation of the offending agent are often essential to management and prevention of loperamide-induced cardiac arrhythmias.

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

ABSTRACTObjective: 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.

Overview of phase IV clinical trials for postmarket drug safety surveillance: a status report from the ClinicalTrials.gov registry

ObjectivePhase IV trials are often used to investigate drug safety after approval. However, little is known about the characteristics of contemporary phase IV clinical trials and whether these studies are...

Completeness of Spontaneous Adverse Drug Reaction Reports Sent by General Practitioners to a Regional Pharmacovigilance Centre: A Descriptive Study.

Spontaneous reporting of adverse drug reactions (ADRs) remains the cornerstone of postmarketing drug safety surveillance (pharmacovigilance); however, one of its main limitations is incomplete data, thus limiting conclusions about causality assessment. The primary aim of this study was to assess the completeness of ADR reports sent by general practitioners (GPs) to regional pharmacovigilance centres and the secondary objective was to identify factors associated with complete ADR reports. All ADR reports sent by GPs to the Midi-Pyrénées Regional Pharmacovigilance Center (Toulouse, France) from 1January 2010 to 31December 2013 were reviewed. Healthcare professionals and patients can forward an ADR using either an online form through the Pharmacology Information Bulletin website ( http://www.bip31.fr ) or 'traditional' ADR reports (i.e. email, letter or fax). According to information provided in ADR reports (i.e. patient identification, ADR, date of occurrence, clinical description, drugs, etc.), reports were classified into three groups: 'well-documented', 'slightly documented' or 'poorly documented'. A multivariate logistic regression was performed to investigate potential factors associated with a 'well-documented' ADR report. During the study period, 613 ADR reports were analysed. Among these reports, only 12.7% were classified as 'well-documented', 68.5% as 'slightly documented' and 18.8% as 'poorly documented'. An association between a 'well-documented' ADR report and its 'seriousness' was found (odds ratio=1.70 [95% CI 1.04-2.76], p=0.01). No association between report completeness ('well-documented' report) and GP practice location or mode of ADR reporting was found. The study shows that only one out of eight ADR reports from GPs was 'well-documented'. Therefore, it appears to be important to promote further information being available regarding the data required in ADR reports to optimise the evaluation of drug causality.

A study on drug safety monitoring program in India.

Pharmacovigilance is useful in assuring the safety of medicines and protecting the consumers from their harmful effects. A number of single drugs as well as fixed dose combinations have been banned from manufacturing, marketing and distribution in India. An important issue about the availability of banned drugs over the counter in India is that sufficient adverse drug reactions data about these drugs have not been reported. The most common categories of drugs withdrawn in the last decade were nonsteroidal antiinflammatory drugs (28%), antidiabetics (14.28%), antiobesity (14.28%), antihistamines (14.28%), gastroprokinetic drugs (7.14%), breast cancer and infertility drugs (7.14%), irritable bowel syndrome and constipation drugs (7.14%) and antibiotics (7.14%). Drug withdrawals from market were made mainly due to safety issues involving cardiovascular events (57.14%) and liver damage (14.28%). Majority of drugs have been banned since 3-5 years in other countries but are still available for sale in India. The present study compares the drug safety monitoring systems in the developed countries such as the USA and UK and provides implications for developing a system that can ensure the safety and efficacy of drugs in India. Absence of a gold standard for a drug safety surveillance system, variations in culture and clinical practice across countries makes it difficult for India to completely adopt another country's practices. There should be a multidisciplinary approach towards drug safety that should be implemented throughout the entire duration spanning from drug discovery to usage by consumers.

Adverse drug event reporting systems: a systematic review.

Adverse drug events (ADEs) are harmful and unintended consequences of medications. Their reporting is essential for drug safety monitoring and research, but it has not been standardized internationally. Our aim was to synthesize information about the type and variety of data collected within ADE reporting systems. We developed a systematic search strategy, applied it to four electronic databases, and completed an electronic grey literature search. Two authors reviewed titles and abstracts, and all eligible full-texts. We extracted data using a standardized form, and discussed disagreements until reaching consensus. We synthesized data by collapsing data elements, eliminating duplicate fields and identifying relationships between reporting concepts and data fields using visual analysis software. We identified 108 ADE reporting systems containing 1782 unique data fields. We mapped them to 33 reporting concepts describing patient information, the ADE, concomitant and suspect drugs, and the reporter. While reporting concepts were fairly consistent, we found variability in data fields and corresponding response options. Few systems clarified the terminology used, and many used multiple drug and disease dictionaries such as the Medical Dictionary for Regulatory Activities (MedDRA). We found substantial variability in the data fields used to report ADEs, limiting the comparability of ADE data collected using different reporting systems, and undermining efforts to aggregate data across cohorts. The development of a common standardized data set that can be evaluated with regard to data quality, comparability and reporting rates is likely to optimize ADE data and drug safety surveillance.

Uptake of new drugs in the early post-approval period in the Mini-Sentinel distributed database.

Several factors limit the statistical power of drug safety surveillance during the early post-approval period, including uptake of the drug and lag in data availability. This study characterized new drug uptake in the Mini-Sentinel Distributed Database and determined statistical power to detect levels of risk in post-launch safety assessments. The cumulative exposure among initiators of 46 new molecular entities approved from 2008 to 2011 was assessed. Using a Poisson estimation method, minimum incidence rate ratios (IRRs) detectable, with 80% power, were calculated under varying background incidence rates. Twelve products (26.1%) had more than 15 000 new users after 2 years. With comparator group incidence rate of 1/1000 person-years, 16 (33.3%) products had enough exposure to detect an IRR of 5 with 24 months of data collected that would be available for assessment at 33 months post-launch. With an incidence rate of 5/1000 person-years, 23 (50%) products had enough exposure to detect an IRR of ≥3 with 2 years of data collected. At 33 months post-launch, only two (4.3%) of the drugs examined had enough data availability to detect IRR of <2, and eight (17.4%) of <3, with a background rate of 1/1000 person-years. This study highlights the importance of drug uptake and data availability in early post-approval drug safety surveillance in Mini-Sentinel. There is limited ability to detect rate ratios below three for events with background rates of 1/1000 person-years or lower. This is largely due to low product uptake. Copyright © 2016 John Wiley & Sons, Ltd.

Safety of anti-rheumatic drugs for rheumatoid arthritis in pregnancy and lactation.

Women with rheumatoid arthritis (RA) are often of childbearing age and therefore questions regarding reproductive health and the use of medications, including disease-modifying anti-rheumatic drugs (DMARDs) may arise during the clinical consultation. Each patient requires individual assessment in order to effectively manage the disease while minimizing any treatment-associated risks to the fetus. Although good-quality controlled trials are lacking, there is an increasing volume of evidence surrounding the use of immunosuppressive therapies in pregnancy and lactation. This review summarizes the currently available information which can be of benefit to clinicians guiding patients and their families through the risks and benefits of continuing RA therapy during pregnancy and lactation. Further studies and ongoing surveillance of drug safety in pregnancy are required to resolve the uncertainties that remain regarding synthetic and biologic DMARDs.

Big data analytics to improve cardiovascular care: promise and challenges.

The potential for big data analytics to improve cardiovascular quality of care and patient outcomes is tremendous. However, the application of big data in health care is at a nascent stage, and the evidence to date demonstrating that big data analytics will improve care and outcomes is scant. This Review provides an overview of the data sources and methods that comprise big data analytics, and describes eight areas of application of big data analytics to improve cardiovascular care, including predictive modelling for risk and resource use, population management, drug and medical device safety surveillance, disease and treatment heterogeneity, precision medicine and clinical decision support, quality of care and performance measurement, and public health and research applications. We also delineate the important challenges for big data applications in cardiovascular care, including the need for evidence of effectiveness and safety, the methodological issues such as data quality and validation, and the critical importance of clinical integration and proof of clinical utility. If big data analytics are shown to improve quality of care and patient outcomes, and can be successfully implemented in cardiovascular practice, big data will fulfil its potential as an important component of a learning health-care system.

On variance estimate for covariate adjustment by propensity score analysis.

Propensity score (PS) methods have been used extensively to adjust for confounding factors in the statistical analysis of observational data in comparative effectiveness research. There are four major PS-based adjustment approaches: PS matching, PS stratification, covariate adjustment by PS, and PS-based inverse probability weighting. Though covariate adjustment by PS is one of the most frequently used PS-based methods in clinical research, the conventional variance estimation of the treatment effects estimate under covariate adjustment by PS is biased. As Stampf et al. have shown, this bias in variance estimation is likely to lead to invalid statistical inference and could result in erroneous public health conclusions (e.g., food and drug safety and adverse events surveillance). To address this issue, we propose a two-stage analytic procedure to develop a valid variance estimator for the covariate adjustment by PS analysis strategy. We also carry out a simple empirical bootstrap resampling scheme. Both proposed procedures are implemented in an R function for public use. Extensive simulation results demonstrate the bias in the conventional variance estimator and show that both proposed variance estimators offer valid estimates for the true variance, and they are robust to complex confounding structures. The proposed methods are illustrated for a post-surgery pain study. Copyright © 2016 John Wiley & Sons, Ltd.