Evaluating Confounding Control in Estimations of Influenza Antiviral Effectiveness in Electronic Health Plan Data.

Observational studies of oseltamivir use and influenza complications could suffer from residual confounding. Using negative control risk periods and a negative control outcome, we examined confounding control in a health-insurance-claims-based study of oseltamivir and influenza complications (pneumonia, all-cause hospitalization, and dispensing of an antibiotic). Within the Food and Drug Administration's Sentinel System, we identified individuals aged ≥18 years who initiated oseltamivir use on the influenza diagnosis date versus those who did not, during 3 influenza seasons (2014-2017). We evaluated primary outcomes within the following 1-30 days (the primary risk period) and 61-90 days (the negative control period) and nonvertebral fractures (the negative control outcome) within days 1-30. We estimated propensity-score-matched risk ratios (RRs) per season. During the 2014-2015 influenza season, oseltamivir use was associated with a reduction in the risk of pneumonia (RR = 0.72, 95% confidence interval (CI): 0.70, 0.75) and all-cause hospitalization (RR = 0.54, 95% CI: 0.53, 0.55) in days 1-30. During days 61-90, estimates were near-null for pneumonia (RR = 1.04, 95% CI: 0.95, 1.15) and hospitalization (RR = 0.94, 95% CI: 0.91, 0.98) but slightly increased for antibiotic dispensing (RR = 1.14, 95% CI: 1.08, 1.21). The RR for fractures was near-null (RR = 1.09, 95% CI: 0.99, 1.20). Estimates for the 2016-2017 influenza season were comparable, while the 2015-2016 season had conflicting results. Our study suggests minimal residual confounding for specific outcomes, but results differed by season.

Clinical Data Extraction During Public Health Emergencies: A Blockchain Technology Assessment.

OBJECTIVE: We sought to explore the technical and legal readiness of healthcare institutions for novel data-sharing methods that allow clinical information to be extracted from electronic health records (EHRs) and submitted securely to the Food and Drug Administration's (FDA's) blockchain through a secure data broker (SDB). MATERIALS AND METHODS: This assessment was divided into four sections: an institutional EHR readiness assessment, legal consultation, institutional review board application submission, and a test of healthcare data transmission over a blockchain infrastructure. RESULTS: All participating institutions reported the ability to electronically extract data from EHRs for research. Formal legal agreements were deemed unnecessary to the project but would be needed in future tests of real patient data exchange. Data transmission to the FDA blockchain met the success criteria of data connection from within the four institutions' firewalls, externally to the FDA blockchain via a SDB. DISCUSSION: The readiness survey indicated advanced analytic capability in hospital institutions and highlighted inconsistency in Fast Healthcare Interoperability Resources format utilitzation across institutions, despite requirements of the 21st Century Cures Act. Further testing across more institutions and annual exercises leveraging the application of data exchange over a blockchain infrastructure are recommended actions for determining the feasibility of this approach during a public health emergency and broaden the understanding of technical requirements for multisite data extraction. CONCLUSION: The FDA's RAPID (Real-Time Application for Portable Interactive Devices) program, in collaboration with Discovery, the Critical Care Research Network's PREP (Program for Resilience and Emergency Preparedness), identified the technical and legal challenges and requirements for rapid data exchange to a government entity using the FDA blockchain infrastructure.

Toward multimodal signal detection of adverse drug reactions.

OBJECTIVE: Improving 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 METHODS: Four 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. RESULTS: Limited 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. CONCLUSIONS: The 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.

Progressive multifocal leukoencephalopathy after natalizumab discontinuation.

OBJECTIVE: To identify cases of laboratory- or biopsy-confirmed progressive multifocal leukoencephalopathy (PML) in patients with multiple sclerosis (MS) who previously discontinued natalizumab (NTZ) for reasons unrelated to suspected or proven PML and assess PML risk factors in these cases. METHODS: We searched the US Food and Drug Administration Adverse Event Reporting System and MEDLINE for reports submitted from 2006 to 2012 of laboratory-confirmed PML with symptom onset ≥30 days following NTZ withdrawal. We only analyzed cases where NTZ discontinuation was unrelated to suspected PML. RESULTS: Seventeen patients discontinued NTZ for reasons unrelated to PML but were subsequently diagnosed with the disease. The median NTZ duration was 47 monthly doses (range = 9-59 doses). All patients presented with compatible clinical symptoms within 6 months following withdrawal, and PML was confirmed by brain biopsy or by identifying JC virus in the cerebrospinal fluid by polymerase chain reaction. Immune reconstitution inflammatory syndrome (IRIS) was reported in 11 patients. Eleven patients (65%) received new MS treatments between NTZ discontinuation and PML confirmation. No deaths were reported. At NTZ withdrawal, 16 patients (94%) had ≥1 PML risk factor, including NTZ duration ≥2 years (n = 13), prior immunosuppressive agents (n = 8), and reported anti-JC virus seropositivity (n = 13). INTERPRETATION: NTZ-treated patients presenting clinically with PML within 6 months after NTZ withdrawal frequently have pre-existing PML risk factors. Clinicians need heightened awareness for new onset PML, IRIS, and MS relapse in evaluating neurological decline following NTZ discontinuation. Ann Neurol 2014;75:108-115.

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.

Central nervous system herpes simplex and varicella zoster virus infections in natalizumab-treated patients.

We report on 20 natalizumab-treated patients with multiple sclerosis who developed laboratory-confirmed central nervous system (CNS) herpesvirus infections. In addition to progressive multifocal leukoencephalopathy, other CNS opportunistic infections have been rarely reported during natalizumab treatment. We encourage heightened awareness due to the risk for serious outcomes.

US-based emergency department visits for fluoroquinolone-associated hypersensitivity reactions.

PURPOSE: To estimate the rate of hypersensitivity reactions per 100,000 prescription dispensings of fluoroquinolones based on care rendered in a nationally representative sample of US hospital emergency departments (ED). METHODS: We analyzed the frequency of fluoroquinolone-associated hypersensitivity reactions using the National Electronic Injury Surveillance System-Cooperative Adverse Drug Event Surveillance system (2004-2010) in conjunction with US retail outpatient prescription data from IMS Health (2004-2010). We further categorized reaction severity into three subgroups (mild, moderate, and severe). RESULTS: Based on 1422 cases of fluoroquinolone-associated hypersensitivity reactions and national drug utilization projections, we estimated risk of hypersensitivity reactions for moxifloxacin, ciprofloxacin, and levofloxacin. The absolute risk of a fluoroquinolone-related hypersensitivity reaction of any severity was low (44.0 (95% CI 34.8-53.3) ED visits/100,000 prescriptions); however, we identified a statistically significant difference in the relative risk (rate ratios) of seeking care in an ED attributed to moxifloxacin hypersensitivity compared to either levofloxacin or ciprofloxacin. For all reaction severities, the estimated ED visits/100,000 prescriptions were 141.3 (95% CI 99.9-182.7) for moxifloxacin, 40.8 (95% CI 31.5-50.0) for levofloxacin, and 26.3 (95% CI 20.8-31.9) for ciprofloxacin. When the rates were stratified by reaction severity category (mild or moderate-severe), moxifloxacin continued to be implicated in more ED visits per 100,000 prescriptions dispensed than either levofloxacin or ciprofloxacin. CONCLUSION: Fluoroquinolones may cause hypersensitivity reactions requiring care in an ED, and relative to use, the rate of moxifloxacin-related hypersensitivity reactions is higher compared to levofloxacin or ciprofloxacin.

Artificial Intelligence-Enabled Software Prototype to Inform Opioid Pharmacovigilance From Electronic Health Records: Development and Usability Study.

Background: The use of patient health and treatment information captured in structured and unstructured formats in computerized electronic health record (EHR) repositories could potentially augment the detection of safety signals for drug products regulated by the US Food and Drug Administration (FDA). Natural language processing and other artificial intelligence (AI) techniques provide novel methodologies that could be leveraged to extract clinically useful information from EHR resources. Objective: Our aim is to develop a novel AI-enabled software prototype to identify adverse drug event (ADE) safety signals from free-text discharge summaries in EHRs to enhance opioid drug safety and research activities at the FDA. Methods: We developed a prototype for web-based software that leverages keyword and trigger-phrase searching with rule-based algorithms and deep learning to extract candidate ADEs for specific opioid drugs from discharge summaries in the Medical Information Mart for Intensive Care III (MIMIC III) database. The prototype uses MedSpacy components to identify relevant sections of discharge summaries and a pretrained natural language processing (NLP) model, Spark NLP for Healthcare, for named entity recognition. Fifteen FDA staff members provided feedback on the prototype's features and functionalities. Results: Using the prototype, we were able to identify known, labeled, opioid-related adverse drug reactions from text in EHRs. The AI-enabled model achieved accuracy, recall, precision, and F1-scores of 0.66, 0.69, 0.64, and 0.67, respectively. FDA participants assessed the prototype as highly desirable in user satisfaction, visualizations, and in the potential to support drug safety signal detection for opioid drugs from EHR data while saving time and manual effort. Actionable design recommendations included (1) enlarging the tabs and visualizations; (2) enabling more flexibility and customizations to fit end users' individual needs; (3) providing additional instructional resources; (4) adding multiple graph export functionality; and (5) adding project summaries. Conclusions: The novel prototype uses innovative AI-based techniques to automate searching for, extracting, and analyzing clinically useful information captured in unstructured text in EHRs. It increases efficiency in harnessing real-world data for opioid drug safety and increases the usability of the data to support regulatory review while decreasing the manual research burden.

Emergency use authorization for intravenous peramivir: evaluation of safety in the treatment of hospitalized patients infected with 2009 H1N1 influenza A virus.

BACKGROUND: On 23 October 2009, the US Food and Drug Administration (FDA) issued an Emergency Use Authorization (EUA) for intravenous peramivir, an unapproved antiviral, to treat suspected or confirmed 2009 H1N1 influenza A virus infection. Eligible hospitalized patients were unresponsive to or unable to tolerate available antivirals or lacked dependable oral or inhaled drug delivery routes. The EUA required healthcare providers to report medication errors, selected adverse events (AEs), serious AEs, and deaths to the FDA. METHODS: An FDA safety team analyzed reports submitted to the Adverse Event Reporting System (AERS) and sought follow-up in selected cases. RESULTS: The FDA received AERS reports for 344 patients (including 28 children and 3 pregnant women). Many patients were critically ill on mechanical ventilation (41%) and renal replacement therapies (19%); 38% had received oseltamivir. The most frequently reported serious AEs by MedDRA preferred term were death (15%), H1N1 influenza (8%), respiratory failure (8%), acute renal failure (7%), and acute respiratory distress syndrome (7%). Six medication errors were reported. Most deaths occurred among patients who were obese, immunosuppressed, aged >65 years, or received oseltamivir. Rash was the only treatment-emergent AE attributable to peramivir. Influenza severity, comorbidities, and concomitant medications confounded additional peramivir AE assessments. Missing clinical and laboratory data precluded evaluation of some reports. CONCLUSIONS: Many peramivir recipients under the EUA were critically ill and at risk for influenza-related complications. The safety data were insufficient to assess whether peramivir affected outcome or caused adverse reactions other than rash. Clinical trials in hospitalized patients with serious influenza infections should provide additional information.

Using inpatient electronic medical records to study influenza for pandemic preparedness.

BACKGROUND: We assessed the ability to identify key data relevant to influenza and other respiratory virus surveillance in a large-scale US-based hospital electronic medical record (EMR) dataset using seasonal influenza as a use case. We describe characteristics and outcomes of hospitalized influenza cases across three seasons. METHODS: We identified patients with an influenza diagnosis between March 2017 and March 2020 in 140 US hospitals as part of the US FDA's Sentinel System. We calculated descriptive statistics on the presence of high-risk conditions, influenza antiviral administrations, and severity endpoints. RESULTS: Among 5.1 million hospitalizations, we identified 29,520 hospitalizations with an influenza diagnosis; 64% were treated with an influenza antiviral within 2 days of admission, and 25% were treated >2 days after admission. Patients treated >2 days after admission had more comorbidities than patients treated within 2 days of admission. Patients never treated during hospitalization had more documentation of cardiovascular and other diseases than treated patients. We observed more severe endpoints in patients never treated (death = 3%, mechanical ventilation [MV] = 9%, intensive care unit [ICU] = 26%) or patients treated >2 days after admission (death = 2%, MV = 14%, ICU = 32%) than in patients treated earlier (treated on admission: death = 1%, MV = 5%, ICU = 23%, treated within 2 days of admission: death = 1%, MV = 7%, ICU = 27%). CONCLUSIONS: We identified important trends in influenza severity related to treatment timing in a large inpatient dataset, laying the groundwork for the use of this and other inpatient EMR data for influenza and other respiratory virus surveillance.

Who gets treated for influenza: A surveillance study from the US Food and Drug Administration's Sentinel System.

OBJECTIVE: We describe the baseline characteristics and complications of individuals with influenza in the US FDA's Sentinel System by antiviral treatment timing. DESIGN: Retrospective cohort design. PATIENTS: Individuals aged ≥6 months with outpatient diagnoses of influenza in June 2014-July 2017, 3 influenza seasons. METHODS: We identified the comorbidities, vaccination history, influenza testing, and outpatient antiviral dispensings of individuals with influenza using administrative claims data from 13 data partners including the Centers for Medicare and Medicaid Services, integrated delivery systems, and commercial health plans. We assessed complications within 30 days: hospitalization, oxygen use, mechanical ventilation, critical care, ECMO, and death. RESULTS: There were 1,090,333 influenza diagnoses in 2014-2015; 1,005,240 in 2016-2017; and 578,548 in 2017-2018. Between 49% and 55% of patients were dispensed outpatient treatment within 5 days. In all periods >80% of treated individuals received treatment on the day of diagnosis. Those treated on days 1-5 after diagnosis had higher prevalences of diabetes, chronic obstructive pulmonary disease, asthma, and obesity compared to those treated on the day of diagnosis or not treated at all. They also had higher rates of hospitalization, oxygen use, and critical care. In 2014-2015, among those aged ≥65 years, the rates of hospitalization were 45 per 1,000 diagnoses among those treated on day 0; 74 per 1,000 among those treated on days 1-5; and 50 per 1,000 among those who were untreated. CONCLUSIONS: In a large, national analysis, approximately half of people diagnosed with influenza in the outpatient setting were treated with antiviral medications. Delays in outpatient dispensed treatment were associated with higher prevalence of comorbidities and higher rates of complication.

Registration trials of antibacterial drugs for the treatment of nosocomial pneumonia.

Since 1989, 12 registration trials have been submitted to the US Food and Drug Administration to support the clinical efficacy of 8 antibacterial drugs for the treatment of nosocomial pneumonia. Six trials used noninferiority designs, whereas the others were equivalence trials or lacked a prespecified hypothesis. Patients with nosocomial pneumonia and ventilator-associated pneumonia were frequently enrolled in the same clinical trials. Enrolled patients were predominantly male and had mean Acute Physiology and Chronic Health Evaluation II scores of 12-18. The investigator's assessment of clinical response was the primary end point, which was usually measured 7-14 days after study drug completion. Clinical cure rates among the intent-to-treat population for nosocomial pneumonia and ventilator-associated pneumonia ranged from 37% to 69% and 25% to 58%, respectively. All-cause mortality ranged from 8% to 28%. Pseudomonas aeruginosa, Acinetobacter species, and methicillin-resistant Staphylococcus aureus were common bacterial pathogens. The design, implementation, and limitations of the clinical trials and implications for future clinical research are discussed.

A Comprehensive Updated Review on SARS-CoV-2 and COVID-19.

This literature review aims to provide a comprehensive current summary of the pathogenesis, clinical features, disease course, host immune responses, and current investigational antiviral and immunomodulatory pharmacotherapies to facilitate the development of future therapies and measures for prevention and control.

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.

Neonatal Safety Information Reported to the FDA During Drug Development Studies.

BACKGROUND: Relatively few neonatal drug development studies have been conducted, but an increase is expected with the enactment of the Food and Drug Administration Safety and Innovation Act (FDASIA). Understanding the safety of drugs studied in neonates is complicated by the unique nature of the population and the level of illness. The objective of this study was to examine neonatal safety data submitted to the FDA in studies pursuant to the Best Pharmaceuticals for Children Act (BPCA) and the Pediatric Research Equity Act (PREA) between 1998 and 2015. METHODS: FDA databases were searched for BPCA and/or PREA studies that enrolled neonates. Studies that enrolled a minimum of 3 neonates were analyzed for the presence and content of neonatal safety data. RESULTS: The analysis identified 40 drugs that were studied in 3 or more neonates. Of the 40 drugs, 36 drugs received a pediatric labeling change as a result of studies between 1998 and 2015, that included information from studies including neonates. Fourteen drugs were approved for use in neonates. Clinical trials for 20 of the drugs reported serious adverse events (SAEs) in neonates. The SAEs primarily involved cardiovascular events such as bradycardia and/or hypotension or laboratory abnormalities such as anemia, neutropenia, and electrolyte disturbances. Deaths were reported during studies of 9 drugs. CONCLUSIONS: Our analysis revealed that SAEs were reported in studies involving 20 of the 40 drugs evaluated in neonates, with deaths identified in 9 of those studies. Patients enrolled in studies were often critically ill, which complicated determination of whether an adverse event was drug-related. We conclude that the traditional means for collecting safety information in drug development trials needs to be adjusted for neonates and will require the collaboration of regulators, industry, and the clinical and research communities to establish appropriate definitions and reporting strategies for the neonatal population.

The effect of group aerobic exercise and t'ai chi on functional outcomes and quality of life for persons living with acquired immunodeficiency syndrome.

OBJECTIVE: This study aimed to assess the usefulness of two interventions in a group rehabilitation medicine setting to determine strategies and exercise guidelines for long-term care of the HIV/AIDS population with human immunodeficiency virus (HIV) and/or acquired immunodeficiency syndrome (AIDS). DESIGN: This was a randomized clinical trial investigating the effects of tai chi (TC) and aerobic exercise (EX) on functional outcomes and quality of life (QOL) in patients with AIDS. SETTING: Two outpatient infectious disease clinics in a mid-atlantic state were the setting. SUBJECTS AND INTERVENTION: Thirty-eight (38) subjects with advanced HIV (AIDS) were randomized to one of three groups: TC, EX, or control. Experimental groups exercised twice weekly for 8 weeks. OUTCOME MEASURES: The primary outcomes included QOL as measured by the Medical Outcomes Short Form (MOS-HIV) and Spirituality Well-Being Scale (SWB). Functional measures included the functional reach (FR) for balance, sit and reach (SR) for flexibility, and sit-up (SU) test for endurance. The physical performance test (PPT) was used to determine overall function, and the Profile of Mood States (POMS) was used to evaluate psychologic changes. To consider the patients' explanations for these measurements, qualitative data were collected from subjects' journals, focus groups, and nonparticipant observation. RESULTS: Thirty-eight (38) subjects were included in data analysis: 13 in the TC group, 13 in the EX group, and 12 in the control group. Results of analysis of covariance showed significant changes in the exercise groups in overall functional measures (p < 0.001). The MOS-HIV showed a significant difference on the subscale of overall health (p = 0.04). The POMS showed significant main effect for time in confusion-bewilderment (p = 0.000) and tension-anxiety (p = 0.005). Three dominant themes emerged from the qualitative data, including: positive physical changes, enhanced psychologic coping, and improved social interactions. CONCLUSIONS: This study shows that TC and EX improve physiologic parameters, functional outcomes, and QOL. Group intervention provides a socialization context for management of chronic HIV disease. This study supports the need for more research investigating the effect of other types of group exercise for this population. This study sets the stage for a larger randomized controlled trial to examine the potential short- and long-term effects of group exercise that may prove beneficial in the management of advanced HIV disease. Further research is warranted to evaluate additional exercise interventions that are accessible, safe, and cost-effective for the HIV population.

Exploring adverse drug events at the class level.

BACKGROUND: While the association between a drug and an adverse event (ADE) is generally detected at the level of individual drugs, ADEs are often discussed at the class level, i.e., at the level of pharmacologic classes (e.g., in drug labels). We propose two approaches, one visual and one computational, to exploring the contribution of individual drugs to the class signal. METHODS: Having established a dataset of ADEs from MEDLINE, we aggregate drugs into ATC classes and ADEs into high-level MeSH terms. We compute statistical associations between drugs and ADEs at the drug level and at the class level. Finally, we visualize the signals at increasing levels of resolution using heat maps. We also automate the exploration of drug-ADE associations at the class level using clustering techniques. RESULTS: Using our visual approach, we were able to uncover known associations, e.g., between fluoroquinolones and tendon injuries, and between statins and rhabdomyolysis. Using our computational approach, we systematically analyzed 488 associations between a drug class and an ADE. CONCLUSIONS: The findings gained from our exploratory techniques should be of interest to the curators of ADE repositories and drug safety professionals. Our approach can be applied to different drug-ADE datasets, using different drug classification systems and different signal detection algorithms.

A time-indexed reference standard of adverse drug reactions.

Undetected adverse drug reactions (ADRs) pose a major burden on the health system. Data mining methodologies designed to identify signals of novel ADRs are of deep importance for drug safety surveillance. The development and evaluation of these methodologies requires proper reference benchmarks. While progress has recently been made in developing such benchmarks, our understanding of the performance characteristics of the data mining methodologies is limited because existing benchmarks do not support prospective performance evaluations. We address this shortcoming by providing a reference standard to support prospective performance evaluations. The reference standard was systematically curated from drug labeling revisions, such as new warnings, which were issued and communicated by the US Food and Drug Administration in 2013. The reference standard includes 62 positive test cases and 75 negative controls, and covers 44 drugs and 38 events. We provide usage guidance and empirical support for the reference standard by applying it to analyze two data sources commonly mined for drug safety surveillance.