Recommendations for achieving interoperable and shareable medical data in the USA.

Easy access to large quantities of accurate health data is required to understand medical and scientific information in real-time; evaluate public health measures before, during, and after times of crisis; and prevent medical errors. Introducing a system in the USA that allows for efficient access to such health data and ensures auditability of data facts, while avoiding data silos, will require fundamental changes in current practices. Here, we recommend the implementation of standardized data collection and transmission systems, universal identifiers for individual patients and end users, a reference standard infrastructure to support calibration and integration of laboratory results from equivalent tests, and modernized working practices. Requiring comprehensive and binding standards, rather than incentivizing voluntary and often piecemeal efforts for data exchange, will allow us to achieve the analytical information environment that patients need.

Signaling COVID-19 Vaccine Adverse Events.

INTRODUCTION: Statistical signal detection is a crucial tool for rapidly identifying potential risks associated with pharmaceutical products. The unprecedented environment created by the coronavirus disease 2019 (COVID-19) pandemic for vaccine surveillance predisposes commonly applied signal detection methodologies to a statistical issue called the masking effect, in which signals for a vaccine of interest are hidden by the presence of other reported vaccines. This masking effect may in turn limit or delay our understanding of the risks associated with new and established vaccines. OBJECTIVE: The aim is to investigate the problem of masking in the context of COVID-19 vaccine signal detection, assessing its impact, extent, and root causes. METHODS: Based on data underlying the Vaccine Adverse Event Reporting System, three commonly applied statistical signal detection methodologies, and a more advanced regression-based methodology, we investigate the temporal evolution of signals corresponding to five largely recognized adverse events and two potentially new adverse events. RESULTS: The results demonstrate that signals of adverse events related to COVID-19 vaccines may be undetected or delayed due to masking when generated by methodologies currently utilized by pharmacovigilance organizations, and that a class of advanced methodologies can partially alleviate the problem. The results indicate that while masking is rare relative to all possible statistical associations, it is much more likely to occur in COVID-19 vaccine signaling, and that its extent, direction, impact, and roots are not static, but rather changing in accordance with the changing nature of data. CONCLUSIONS: Masking is an addressable problem that merits careful consideration, especially in situations such as COVID-19 vaccine safety surveillance and other emergency use authorization products.

A dataset of 200 structured product labels annotated for adverse drug reactions.

Adverse drug reactions (ADRs), unintended and sometimes dangerous effects that a drug may have, are one of the leading causes of morbidity and mortality during medical care. To date, there is no structured machine-readable authoritative source of known ADRs. The United States Food and Drug Administration (FDA) partnered with the National Library of Medicine to create a pilot dataset containing standardised information about known adverse reactions for 200 FDA-approved drugs. The Structured Product Labels (SPLs), the documents FDA uses to exchange information about drugs and other products, were manually annotated for adverse reactions at the mention level to facilitate development and evaluation of text mining tools for extraction of ADRs from all SPLs. The ADRs were then normalised to the Unified Medical Language System (UMLS) and to the Medical Dictionary for Regulatory Activities (MedDRA). We present the curation process and the structure of the publicly available database SPL-ADR-200db containing 5,098 distinct ADRs. The database is available at https://bionlp.nlm.nih.gov/tac2017adversereactions/; the code for preparing and validating the data is available at https://github.com/lhncbc/fda-ars.

Harnessing scientific literature reports for pharmacovigilance. Prototype software analytical tool development and usability testing.

OBJECTIVES: We seek to develop a prototype software analytical tool to augment FDA regulatory reviewers' capacity to harness scientific literature reports in PubMed/MEDLINE for pharmacovigilance and adverse drug event (ADE) safety signal detection. We also aim to gather feedback through usability testing to assess design, performance, and user satisfaction with the tool. METHODS: A prototype, open source, web-based, software analytical tool generated statistical disproportionality data mining signal scores and dynamic visual analytics for ADE safety signal detection and management. We leveraged Medical Subject Heading (MeSH) indexing terms assigned to published citations in PubMed/MEDLINE to generate candidate drug-adverse event pairs for quantitative data mining. Six FDA regulatory reviewers participated in usability testing by employing the tool as part of their ongoing real-life pharmacovigilance activities to provide subjective feedback on its practical impact, added value, and fitness for use. RESULTS: All usability test participants cited the tool's ease of learning, ease of use, and generation of quantitative ADE safety signals, some of which corresponded to known established adverse drug reactions. Potential concerns included the comparability of the tool's automated literature search relative to a manual 'all fields' PubMed search, missing drugs and adverse event terms, interpretation of signal scores, and integration with existing computer-based analytical tools. CONCLUSIONS: Usability testing demonstrated that this novel tool can automate the detection of ADE safety signals from published literature reports. Various mitigation strategies are described to foster improvements in design, productivity, and end user satisfaction.

Use of data mining at the Food and Drug Administration.

OBJECTIVES: This article summarizes past and current data mining activities at the United States Food and Drug Administration (FDA). TARGET AUDIENCE: We address data miners in all sectors, anyone interested in the safety of products regulated by the FDA (predominantly medical products, food, veterinary products and nutrition, and tobacco products), and those interested in FDA activities. SCOPE: Topics include routine and developmental data mining activities, short descriptions of mined FDA data, advantages and challenges of data mining at the FDA, and future directions of data mining at the FDA.

Leveraging MEDLINE indexing for pharmacovigilance - Inherent limitations and mitigation strategies.

BACKGROUND: Traditional approaches to pharmacovigilance center on the signal detection from spontaneous reports, e.g., the U.S. Food and Drug Administration (FDA) adverse event reporting system (FAERS). In order to enrich the scientific evidence and enhance the detection of emerging adverse drug events that can lead to unintended harmful outcomes, pharmacovigilance activities need to evolve to encompass novel complementary data streams, for example the biomedical literature available through MEDLINE. OBJECTIVES: (1) To review how the characteristics of MEDLINE indexing influence the identification of adverse drug events (ADEs); (2) to leverage this knowledge to inform the design of a system for extracting ADEs from MEDLINE indexing; and (3) to assess the specific contribution of some characteristics of MEDLINE indexing to the performance of this system. METHODS: We analyze the characteristics of MEDLINE indexing. We integrate three specific characteristics into the design of a system for extracting ADEs from MEDLINE indexing. We experimentally assess the specific contribution of these characteristics over a baseline system based on co-occurrence between drug descriptors qualified by adverse effects and disease descriptors qualified by chemically induced. RESULTS: Our system extracted 405,300 ADEs from 366,120 MEDLINE articles. The baseline system accounts for 297,093 ADEs (73%). 85,318 ADEs (21%) can be extracted only after integrating specific pre-coordinated MeSH descriptors and additional qualifiers. 22,889 ADEs (6%) can be extracted only after considering indirect links between the drug of interest and the descriptor that bears the ADE context. CONCLUSIONS: In this paper, we demonstrate significant improvement over a baseline approach to identifying ADEs from MEDLINE indexing, which mitigates some of the inherent limitations of MEDLINE indexing for pharmacovigilance. ADEs extracted from MEDLINE indexing are complementary to, not a replacement for, other sources.

Medication Exposures and Subsequent Development of Ewing Sarcoma: A Review of FDA Adverse Event Reports.

Background. Ewing sarcoma family of tumors (ESFT) are rare but deadly cancers of unknown etiology. Few risk factors have been identified. This study was undertaken to ascertain any possible association between exposure to therapeutic drugs and ESFT. Methods. This is a retrospective, descriptive study. A query of the FDA Adverse Event Reporting System (FAERS) was conducted for all reports of ESFT, January 1, 1998, through December 31, 2013. Report narratives were individually reviewed for patient characteristics, underlying conditions and drug exposures. Results. Over 16 years, 134 ESFT reports were identified, including 25 cases of ESFT following therapeutic drugs and biologics including immunosuppressive agents and hormones. Many cases were confounded by concomitant medications and other therapies. Conclusions. This study provides a closer look at medication use and underlying disorders in patients who later developed ESFT. While this study was not designed to demonstrate any clear causative association between ESFT and prior use of a single product or drug class, many drugs were used to treat immune-related disease and growth or hormonal disturbances. Further studies may be warranted to better understand possible immune or neuroendocrine abnormalities or exposure to specific classes of drugs that may predispose to the later development of ESFT.

Profiling cumulative proportional reporting ratios of drug-induced liver injury in the FDA Adverse Event Reporting System (FAERS) database.

BACKGROUND: Early prediction and accurate characterization of risk for serious liver injury associated with newly marketed drugs remains an important challenge for clinicians, the pharmaceutical industry, and regulators. To date, a biomarker that specifically indicates exposure to a drug as the etiologic cause of liver injury has not been identified. OBJECTIVES: Using cumulative proportional reporting ratios (PRRs), we investigated 'real-time' profiles of a set of pharmaceuticals, over the first 3 years of US marketing, for the signaling of clinically serious drug-induced liver injury (DILI) in a large spontaneous-reporting database. METHODS: Using report counts of hepatic failure or clinically serious liver injury obtained from the FDA Adverse Events Reporting System (FAERS) database, PRRs of adverse drug event terms were calculated by division of counts of domestic reports of these events by counts of all serious adverse events for each of 13 selected drugs associated with a broad range of hepatotoxic risk (including three linked to only rare instances of clinically apparent liver injury) with reference to all other drugs in the database. Drug-specific cumulative PRRs were measured at successive intervals (calendar quarters) using cumulative tallies of FAERS reports to generate time-based profiles over the initial 3 years of US marketing. RESULTS: In the set of drugs analyzed, those with no known hepatotoxic risk demonstrated time-based cumulative PRR profiles that approximate the background rates of hepatic failure and serious liver injury reported in the entire FAERS database. In contrast, those that were removed from marketing or subjected to marketing restrictions due to their potential to cause liver injury were associated with profiles of rapidly rising cumulative PRRs that were greater than 5 within the first 10 million domestic prescriptions or the first four quarters of US marketing. The systematic tracking and identification of rising PRRs for DILI associated with newly marketed pharmaceutical and biological agents is a valuable tool for identification of safety signals within the FAERS database. LIMITATIONS: Disproportionality profiling of spontaneous reports in FAERS (e.g., cumulative PRR measurements), which signals an association between a recently marketed drug and liver injury, is not a method to quantitatively measure drug-related risk. Regulatory actions in response to emerging drug safety concerns often depend on an accurate assessment of risks using multiple sources of data and the consideration of overall benefits and risks of the agent. Causality must be determined through analysis of individual cases to exclude other etiologies of liver injury. CONCLUSION: The FAERS database can be used to advance empiric hepatotoxicity time-trending reporting levels for newly marketed agents in order to rapidly identify recently launched potential hepatotoxic agents and initiate further evaluation.

Eosinophilic pneumonia in patients treated with daptomycin: review of the literature and US FDA adverse event reporting system reports.

BACKGROUND: Eosinophilic pneumonia (EP) has been noted in association with daptomycin use. The product labelling was recently updated to include EP in the Warnings and Precautions and Post-Marketing Experience sections. OBJECTIVE: The objective of this study was to analyse adverse event (AE) reports submitted to the US FDA as well as published cases to characterize the clinical features and course of EP in daptomycin-treated patients. METHODS: We searched for EP cases associated with daptomycin administration in the FDA Adverse Event Reporting System (AERS) submitted from 2004 to 2010, and the published literature. Cases were defined as definite, probable, possible and unlikely in terms of the diagnosis of EP and the potential association with daptomycin exposure. Definite cases had concurrent exposure to daptomycin, fever, dyspnoea with increased oxygen requirement or required mechanical ventilation, new infiltrates on chest imaging, bronchoalveolar lavage with >25% eosinophils and clinical improvement following daptomycin withdrawal. Additionally, we assessed inpatient daptomycin utilization. RESULTS: We identified 7 definite, 13 probable, 38 possible cases of daptomycin-induced EP, and 23 unlikely cases. The seven definite EP cases had resolution after daptomycin was stopped, including two with EP recurrence following daptomycin rechallenge. Regarding the definite cases: (i) ages ranged from 60 to 87 years; (ii) dosing ranged from 4.4 to 8.0 mg/kg/day; and (iii) EP developed 10 days to 4 weeks after starting daptomycin. There was a gradual increase in the number of patients with an inpatient hospital discharge billing for daptomycin from the year 2004 to 2010. CONCLUSIONS: We report 7 definite, 13 probable and 38 possible EP cases associated with daptomycin administration. As AERS is based on voluntary reporting, the incidence of EP cannot be assessed. Healthcare providers should have heightened awareness of this serious AE associated with daptomycin use.

Atypical antipsychotics and pituitary tumors: a pharmacovigilance study.

STUDY OBJECTIVE: To analyze the disproportionality of reporting of hyperprolactinemia, galactorrhea, and pituitary tumors with seven widely used antipsychotic drugs. DESIGN: Retrospective pharmacovigilance study. DATA SOURCE: United States Food and Drug Administration's Adverse Event Reporting System (AERS) database. INTERVENTION: We initially identified higher-than-expected postmarketing reports of pituitary tumors associated with risperidone, a potent dopamine D2-receptor antagonist antipsychotic, by analyzing reporting patterns of these tumors in the AERS database. To further examine this association, we analyzed disproportionate reporting patterns of pituitary tumor reports for seven antipsychotics with different affinities for blocking D2 receptors: aripiprazole, clozapine, olanzapine, quetiapine, risperidone, ziprasidone, and haloperidol. MEASUREMENTS AND MAIN RESULTS: To conduct both of these analyses, we used the Multi-item Gamma Poisson Shrinker (MGPS) data mining algorithm applied to the AERS database. The MGPS uses a Bayesian model to calculate adjusted observed:expected ratios of drug-adverse event associations (Empiric Bayes Geometric Mean [EBGM] values) in huge drug safety databases. The higher the adjusted reporting ratio, or EBGM value, the greater the strength of the association between a drug and an adverse event. Risperidone had the highest adjusted reporting ratios for hyperprolactinemia (EBGM 34.9, 90% confidence interval [CI] 32.8-37.1]), galactorrhea (EBGM 19.9, 90% CI 18.6-21.4), and pituitary tumor (EBGM 18.7, 90% CI 14.9-23.3) among the seven antipsychotics, and one of the highest scores for all drugs in the AERS database. Some tumors were associated with visual field defects, hemorrhage, convulsions, surgery, and severe (>10-fold) prolactin elevations. The EBGM values for risperidone for these adverse events were higher in women, but high EBGM values for these events were also seen in men and children. Moreover, the rank order of the EBGM values for pituitary tumors corresponded to the affinities of these seven drugs for D2 receptors. CONCLUSION: Treatment with potent D2-receptor antagonists, such as risperidone, may be associated with pituitary tumors. These findings are consistent with animal (mice) studies and raise the need for clinical awareness and longitudinal studies.

Perspectives on the use of data mining in pharmaco-vigilance.

In the last 5 years, regulatory agencies and drug monitoring centres have been developing computerised data-mining methods to better identify reporting relationships in spontaneous reporting databases that could signal possible adverse drug reactions. At present, there are no guidelines or standards for the use of these methods in routine pharmaco-vigilance. In 2003, a group of statisticians, pharmaco-epidemiologists and pharmaco-vigilance professionals from the pharmaceutical industry and the US FDA formed the Pharmaceutical Research and Manufacturers of America-FDA Collaborative Working Group on Safety Evaluation Tools to review best practices for the use of these methods.In this paper, we provide an overview of: (i) the statistical and operational attributes of several currently used methods and their strengths and limitations; (ii) information about the characteristics of various postmarketing safety databases with which these tools can be deployed; (iii) analytical considerations for using safety data-mining methods and interpreting the results; and (iv) points to consider in integration of safety data mining with traditional pharmaco-vigilance methods. Perspectives from both the FDA and the industry are provided. Data mining is a potentially useful adjunct to traditional pharmaco-vigilance methods. The results of data mining should be viewed as hypothesis generating and should be evaluated in the context of other relevant data. The availability of a publicly accessible global safety database, which is updated on a frequent basis, would further enhance detection and communication about safety issues.

Pharmacovigilance in the 21st century: new systematic tools for an old problem.

The large number of adverse-event reports generated by marketed drugs and devices argues for the application of validated computerized algorithms to supplement traditional methods of detecting adverse-event signals. Difficulties in accurately estimating patient exposure and background rates for a given event in a specific population hinder risk estimation in spontaneous adverse-event databases. The United States Food and Drug Administration (FDA) is evaluating a Bayesian data mining system called Multi-item Gamma Poisson Shrinker (MGPS) to enhance the FDA's ability to monitor the safety of drugs, biologics, and vaccines after they have been approved for use. The MGPS computes adjusted higher-than-expected reporting relationships between drugs and adverse events across 35 years of data relative to internal background rates. The MGPS can also adjust for random noise by using a model derived from the data, and corrects for temporal trends and confounding related to age, sex, and other variables by stratifying over 900 categories. Signals can then be compared with or used in conjunction with other sources (e.g. clinical trials, general practice databases) to further study the adverse-event risk. The example of pancreatitis risk with atypical antipsychotics, valproic acid, and valproate is used to discuss the strengths and limitations of MGPS versus traditional methods. Validated data mining techniques offer great promise to enhance pharmacovigilance practices.