Enabling social listening for cardiac safety monitoring: Proceedings from a drug information association-cardiac safety research consortium cosponsored think tank.

This white paper provides a summary of the presentations and discussions from a think tank on "Enabling Social Listening for Cardiac Safety Monitoring" trials that was cosponsored by the Drug Information Association and the Cardiac Safety Research Consortium, and held at the White Oak headquarters of the US Food and Drug Administration on June 3, 2016. The meeting's goals were to explore current methods of collecting and evaluating social listening data and to consider their applicability to cardiac safety surveillance. Social listening is defined as the act of monitoring public postings on the Internet. It has several theoretical advantages for drug and device safety. First, these include the ability to detect adverse events that are "missed" by traditional sources and the ability to detect adverse events sooner than would be allowed by traditional sources, both by affording near-real-time access to data from culturally and geographically diverse sources. Social listening can also potentially introduce a novel patient voice into the conversation about drug safety, which could uniquely augment understanding of real-world medication use obtained from more traditional methodologies. Finally, it can allow for access to information about drug misuse and diversion. To date, the latter 2 of these have been realized. Although regulators from the Food and Drug Administration and the United Kingdom's Medicines and Healthcare Products Regulatory Agency participated in the think tank along with representatives from industry, academia, and patient groups, this article should not be construed to constitute regulatory guidance.

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.

Development and evaluation of a common data model enabling active drug safety surveillance using disparate healthcare databases.

OBJECTIVE: Active drug safety surveillance may be enhanced by analysis of multiple observational healthcare databases, including administrative claims and electronic health records. The objective of this study was to develop and evaluate a common data model (CDM) enabling rapid, comparable, systematic analyses across disparate observational data sources to identify and evaluate the effects of medicines. DESIGN: The CDM uses a person-centric design, with attributes for demographics, drug exposures, and condition occurrence. Drug eras, constructed to represent periods of persistent drug use, are derived from available elements from pharmacy dispensings, prescriptions written, and other medication history. Condition eras aggregate diagnoses that occur within a single episode of care. Drugs and conditions from source data are mapped to biomedical ontologies to standardize terminologies and enable analyses of higher-order effects. MEASUREMENTS: The CDM was applied to two source types: an administrative claims and an electronic medical record database. Descriptive statistics were used to evaluate transformation rules. Two case studies demonstrate the ability of the CDM to enable standard analyses across disparate sources: analyses of persons exposed to rofecoxib and persons with an acute myocardial infarction. RESULTS: Over 43 million persons, with nearly 1 billion drug exposures and 3.7 billion condition occurrences from both databases were successfully transformed into the CDM. An analysis routine applied to transformed data from each database produced consistent, comparable results. CONCLUSION: A CDM can normalize the structure and content of disparate observational data, enabling standardized analyses that are meaningfully comparable when assessing the effects of medicines.

Semi-automated risk estimation using large databases: quinolones and Clostridium difficile associated diarrhea.

PURPOSE: The availability of large databases with person time information and appropriate statistical methods allow for relatively rapid pharmacovigilance analyses. A semi-automated method was used to investigate the effect of fluoroquinolones on the incidence of C. difficile associated diarrhea (CDAD). METHODS: Two US databases, an electronic medical record (EMR) and a large medical claims database for the period 2006-2007 were evaluated using a semi-automated methodology. The raw EMR and claims datasets were subject to a normalization procedure that aligns the drug exposures and conditions using ontologies; Snowmed for medications and MedDRA for conditions. A retrospective cohort design was used together with matching by means of the propensity score. The association between exposure and outcome was evaluated using a Poisson regression model after taking into account potential confounders. RESULTS: A comparison between quinolones as the target cohort and macrolides as the comparison cohort produced a total of 564,797 subjects exposed to a quinolone in the claims data and 233,090 subjects in the EMR. They were matched with replacement within six strata of the propensity score. Among the matched cohorts there were a total of 488 and 158 outcomes in the claims and the EMR respectively. Quinolones were found to be twice more likely to be significantly associated with CDAD than macrolides adjusting for risk factors (IRR 2.75, 95%CI 2.18-3.48). CONCLUSIONS: Use of a semi-automated method was successfully applied to two observational databases and was able to rapidly identify a potential for increased risk of developing CDAD with quinolones.

Serious adverse events with infliximab: analysis of spontaneously reported adverse events.

BACKGROUND & AIMS: Serious adverse events such as bowel obstruction, heart failure, infection, lymphoma, and neuropathy have been reported with infliximab. The aims of this study were to explore adverse event signals with infliximab by using a long period of post-marketing experience, stratifying by indication. METHODS: The relative reporting of infliximab adverse events to the U.S. Food and Drug Administration (FDA) was assessed with the public release version of the adverse event reporting system (AERS) database from 1968 to third quarter 2005. On the basis of a systematic review of adverse events, Medical Dictionary for Regulatory Activities (MedDRA) terms were mapped to predefined categories of adverse events, including death, heart failure, hepatitis, infection, infusion reaction, lymphoma, myelosuppression, neuropathy, and obstruction. Disproportionality analysis was used to calculate the empiric Bayes geometric mean (EBGM) and corresponding 90% confidence intervals (EB05, EB95) for adverse event categories. RESULTS: Infliximab was identified as the suspect medication in 18,220 reports in the FDA AERS database. We identified a signal for lymphoma (EB05 = 6.9), neuropathy (EB05 = 3.8), infection (EB05 = 2.9), and bowel obstruction (EB05 = 2.8). The signal for granulomatous infections was stronger than the signal for non-granulomatous infections (EB05 = 12.6 and 2.4, respectively). The signals for bowel obstruction and infusion reaction were specific to patients with IBD; this suggests potential confounding by indication, especially for bowel obstruction. CONCLUSIONS: In light of this additional evidence of risk of lymphoma, neuropathy, and granulomatous infections, clinicians should stress this risk in the shared decision-making process.