The FDA's sentinel initiative-A comprehensive approach to medical product surveillance

Random control selection for conducting high-throughput adverse drug events screening using large-scale longitudinal health data

Case-control design based high-throughput pharmacoinformatics study using large-scale longitudinal health data is able to detect new adverse drug event (ADEs) signals. Existing control selection approaches for case-control design included the dynamic/super control selection approach. The dynamic/super control selection approach requires all individuals to be evaluated at all ADE case index dates, as the individuals' eligibilities as control depend on ADE/enrollment history. Thus, using large-scale longitudinal health data, the dynamic/super control selection approach requires extraordinarily high computational time. We proposed a random control selection approach in which ADE case index dates were matched by randomly generated control index dates. The random control selection approach does not depend on ADE/enrollment history. It is able to significantly reduce computational time to prepare case-control data sets, as it requires all individuals to be evaluated only once. We compared the performance metrics of all control selection approaches using two large-scale longitudinal health data and a drug-ADE gold standard including 399 drug-ADE pairs. The F-scores for the random control selection approach were between 0.586 and 0.600 compared to between 0.545 and 0.562 for dynamic/super control selection approaches. The random control selection approach was ~ 1000 times faster than dynamic/super control selection approach on preparing case-control data sets. With large-scale longitudinal health data, a case-control design-based pharmacoinformatics study using random control selection is able to generate comparable ADE signals than the existing control selection approaches. The random control selection approach also significantly reduces computational time to prepare the case-control data sets.

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.

Increasing trust in real-world evidence through evaluation of observational data quality

Objective

Advances in standardization of observational healthcare data have enabled methodological breakthroughs, rapid global collaboration, and generation of real-world evidence to improve patient outcomes. Standardizations in data structure, such as use of common data models, need to be coupled with standardized approaches for data quality assessment. To ensure confidence in real-world evidence generated from the analysis of real-world data, one must first have confidence in the data itself.

Materials and Methods

We describe the implementation of check types across a data quality framework of conformance, completeness, plausibility, with both verification and validation. We illustrate how data quality checks, paired with decision thresholds, can be configured to customize data quality reporting across a range of observational health data sources. We discuss how data quality reporting can become part of the overall real-world evidence generation and dissemination process to promote transparency and build confidence in the resulting output.

Results

The Data Quality Dashboard is an open-source R package that reports potential quality issues in an OMOP CDM instance through the systematic execution and summarization of over 3300 configurable data quality checks.

Discussion

Transparently communicating how well common data model-standardized databases adhere to a set of quality measures adds a crucial piece that is currently missing from observational research.

Conclusion

Assessing and improving the quality of our data will inherently improve the quality of the evidence we generate.

Systematic risk identification and assessment using a new risk map in pharmaceutical R&D

Delivering transformative therapies to patients while maintaining growth in the pharmaceutical industry requires an efficient use of research and development (R&D) resources and technologies to develop high-impact new molecular entities (NMEs). However, increasing global R&D competition in the pharmaceutical industry, growing impact of generics and biosimilars, more stringent regulatory requirements, as well as cost-constrained reimbursement frameworks challenge current business models of leading pharmaceutical companies. Big data-based analytics and artificial intelligence (AI) approaches have disrupted various industries and are having an increasing impact in the biopharmaceutical industry, with the promise to improve and accelerate biopharmaceutical R&D processes. Here, we systematically analyze, identify, assess, and categorize key risks across the drug discovery and development value chain using a new risk map approach, providing a comprehensive risk-reward analysis for pharmaceutical R&D.

Methodological frontiers in vaccine safety: qualifying available evidence for rare events, use of distributed data networks to monitor vaccine safety issues, and monitoring the safety of pregnancy interventions

While vaccines are rigorously tested for safety and efficacy in clinical trials, these trials do not include enough subjects to detect rare adverse events, and they generally exclude special populations such as pregnant women. It is therefore necessary to conduct postmarketing vaccine safety assessments using observational data sources. The study of rare events has been enabled in through large linked databases and distributed data networks, in combination with development of case-centred methods. Distributed data networks necessitate common protocols, definitions, data models and analytics and the processes of developing and employing these tools are rapidly evolving. Assessment of vaccine safety in pregnancy is complicated by physiological changes, the challenges of mother-child linkage and the need for long-term infant follow-up. Potential sources of bias including differential access to and utilisation of antenatal care, immortal time bias, seasonal timing of pregnancy and unmeasured determinants of pregnancy outcomes have yet to be fully explored. Available tools for assessment of evidence generated in postmarketing studies may downgrade evidence from observational data and prioritise evidence from randomised controlled trials. However, real-world evidence based on real-world data is increasingly being used for safety assessments, and new tools for evaluating real-world evidence have been developed. The future of vaccine safety surveillance, particularly for rare events and in special populations, comprises the use of big data in single countries as well as in collaborative networks. This move towards the use of real-world data requires continued development of methodologies to generate and assess real world evidence.

Commentary on Professor Austin Bradford Hill's Alfred Watson Memorial Lecture

As availability of health care data for research opens up new frontiers in medical statistics, keeping a focus on the science behind the data is more important than ever to promote sound research and protect the validity of research results. Though the electronic databases currently amassed for research far exceed in scale and scope the observational research Professor Hill likely conceived of, his guidance to statisticians to ground our work in the biological and medical processes behind the data remains salient across the decades.

Patient Characteristics and Utilization Patterns of Short-Acting Recombinant Granulocyte Colony-Stimulating Factor (G-CSF) Biosimilars Compared to Their Reference Product

BACKGROUND

Data on short-acting recombinant granulocyte colony-stimulating factor (G-CSF) biosimilar utilization from claims data in the USA are limited.

OBJECTIVE

To evaluate patient baseline characteristics and utilization patterns for short-acting G-CSF products with particular focus on the assessment of filgrastim biosimilar usage relative to the originator product.

PATIENTS AND METHODS

We examined filgrastim, filgrastim-sndz, and tbo-filgrastim use among adult patients between January 2012 and March 2019 across the five health-plan research partners in the BBCIC Distributed Research Network. The publicly available Sentinel System analytic toolkit was used to perform the distributed analyses.

RESULTS

We evaluated over 38 million eligible health-plan members representing more than 88 million person-years of data. We identified 45,204 incident treatment episodes, including 33,118 episodes with filgrastim, 6525 episodes with filgrastim-sndz, and 5,561 episodes with tbo-filgrastim. We observed that the demographic and clinical characteristics of users were comparable across products. While total use of all filgrastim products remained consistent, the proportion of incident episodes of the originator filgrastim steadily decreased since 2014, with filgrastim-sndz and tbo-filgrastim making up the difference. Utilization for the G-CSF biosimilar, filgrastim-sndz, increased from 40 (1%) of 6823 total filgrastim product episodes in 2015, to 2486 (44%) of a total 5668 episodes of filgrastim products in 2018 (partial data for 2018).

CONCLUSION

New episodes of short-acting biosimilar filgrastim products have increased over time while the overall number of new users remained flat. Although barriers to biosimilar use in oncology have been noted, uptake has begun and continues to grow.

Validation of an electronic algorithm for Hodgkin and non-Hodgkin lymphoma in ICD-10-CM

PURPOSE

Lymphoma is a health outcome of interest for drug safety studies. Studies using administrative claims data require the accurate identification of lymphoma cases. We developed and validated an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM)-based algorithm to identify lymphoma in healthcare claims data.

METHODS

We developed a three-component algorithm to identify patients aged ≥15 years who were newly diagnosed with Hodgkin (HL) or non-Hodgkin (NHL) lymphoma from January 2016 through July 2018 among members of four Data Partners within the FDA's Sentinel System. The algorithm identified potential cases as patients with ≥2 ICD-10-CM lymphoma diagnosis codes on different dates within 183 days; ≥1 procedure code for a diagnostic procedure (e.g., biopsy, flow cytometry) and ≥1 procedure code for a relevant imaging study within 90 days of the first lymphoma diagnosis code. Cases identified by the algorithm were adjudicated via chart review and a positive predictive value (PPV) was calculated.

RESULTS

We identified 8723 potential lymphoma cases via the algorithm and randomly sampled 213 for validation. We retrieved 138 charts (65%) and adjudicated 134 (63%). The overall PPV was 77% (95% confidence interval: 69%-84%). Most cases also had subtype information available, with 88% of cases identified as NHL and 11% as HL.

CONCLUSIONS

Seventy-seven percent of lymphoma cases identified by an algorithm based on ICD-10-CM diagnosis and procedure codes and applied to claims data were true cases. This novel algorithm represents an efficient, cost-effective way to target an important health outcome of interest for large-scale drug safety and public health surveillance studies.

Utilization of hydroxyprogesterone caproate among pregnancies with live birth deliveries in the sentinel distributed database

BACKGROUND

The U.S. Food and Drug Administration (FDA) approved Makena^® (hydroxyprogesterone caproate [HPC] injection) in February 2011 for reducing the risk of preterm birth (PTB) in women with a singleton pregnancy who had a history of singleton spontaneous PTB (sPTB). Makena was approved under accelerated approval and required a postmarketing study to verify its clinical benefits. However, the postmarketing trial (PROLONG) failed to verify Makena's clinical benefit to neonates and substantiate its effect on reducing the risk of recurrent PTB. This study examined the utilization of HPC, along with another progestogen (vaginal progesterone) used to reduce the risk of sPTB during pregnancy, to inform the landscape of HPC use in the United States.

METHODS

We included pregnant women aged 10-54 years with a live birth delivery from 1 January, 2008 to 31 December, 2018 in the Sentinel Distributed Database (SDD). We examined the prevalence of injectable HPC (Makena and its generics), compounded HPC, and vaginal progesterone use during the second and third trimesters during the study period. We also assessed the proportion of these HPC-exposed pregnancies with obstetrical conditions of interest as potential reasons for use: (1) history of preterm delivery; (2) cervical shortening in the current pregnancy; and (3) preterm labor in the current pregnancy.

RESULTS

We identified a total of 3,445,739 live-birth pregnancies (among 2.9 million women) between 2008 and 2018 in the SDD. Of these pregnancies, 6.5 per 1,000 pregnancies used injectable HPC, 2.3 per 1,000 pregnancies used compounded HPC, and 1.5 per 1,000 pregnancies used vaginal progesterone during the second and/or third trimesters. The yearly uptakeof pregnancies with injectable HPC use increased during the study period from 2.1 per 1,000 pregnancies in 2012 to 12.6 per 1,000 pregnancies in 2018; use of compounded HPC decreased from 3.3 per 1,000 pregnancies to 0.25 per 1,000 pregnancies over the same period. Of 16,524 pregnancies with injectable HPC use, 12,054 (73%) had at least one related obstetrical condition, including 6,439 (39%) with a recorded history of preterm delivery. In addition, 4,665 (28%) had a PTB recorded as the outcome for the current pregnancy.

CONCLUSIONS

We found modest use of HPC during the second and/or third trimesters among all live-birth pregnancies in SDD. The majority of pregnancies with injectable HPC use had at least one of three obstetrical indications of interest recorded before or during the pregnancy.

A COVID-19-ready public health surveillance system: The Food and Drug Administration's Sentinel System

The US Food and Drug Administration's Sentinel System was established in 2009 to use routinely collected electronic health data for improving the national capability to assess post‐market medical product safety. Over more than a decade, Sentinel has become an integral part of FDA's surveillance capabilities and has been used to conduct analyses that have contributed to regulatory decisions. FDA's role in the COVID‐19 pandemic response has necessitated an expansion and enhancement of Sentinel. Here we describe how the Sentinel System has supported FDA's response to the COVID‐19 pandemic. We highlight new capabilities developed, key data generated to date, and lessons learned, particularly with respect to working with inpatient electronic health record data.

Early in the pandemic, Sentinel developed a multi‐pronged approach to support FDA's anticipated data and analytic needs. It incorporated new data sources, created a rapidly refreshed database, developed protocols to assess the natural history of COVID‐19, validated a diagnosis‐code based algorithm for identifying patients with COVID‐19 in administrative claims data, and coordinated with other national and international initiatives.

Sentinel is poised to answer important questions about the natural history of COVID‐19 and is positioned to use this information to study the use, safety, and potentially the effectiveness of medical products used for COVID‐19 prevention and treatment.

Risk of Nonmelanoma Skin Cancer in Association With Use of Hydrochlorothiazide-Containing Products in the United States

Background

European studies reported an increased risk of nonmelanoma skin cancer associated with hydrochlorothiazide (HCTZ)-containing products. We examined the risks of basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) associated with HCTZ compared with angiotensin-converting enzyme inhibitors (ACEIs) in a US population.

Methods

We conducted a retrospective cohort study in the US Food and Drug Administration's Sentinel System. From the date of HCTZ or ACEI dispensing, patients were followed until a SCC or BCC diagnosis requiring excision or topical chemotherapy treatment on or within 30 days after the diagnosis date or a censoring event. Using Cox proportional hazards regression models, we estimated the hazard ratios (HRs), overall and separately by age, sex, and race. We also examined site- and age-adjusted incidence rate ratios (IRRs) by cumulative HCTZ dose within the matched cohort.

Results

Among 5.2 million propensity-score matched HCTZ and ACEI users, the incidence rate (per 1000 person-years) of BCC was 2.78 and 2.82, respectively, and 1.66 and 1.60 for SCC. Overall, there was no difference in risk between HCTZ and ACEIs for BCC (HR = 0.99, 95% confidence interval [CI] = 0.97 to 1.00), but there was an increased risk for SCC (HR = 1.04, 95% CI = 1.02 to 1.06). HCTZ use was associated with higher risks of BCC (HR = 1.09, 95% CI = 1.07 to 1.11) and SCC (HR = 1.15, 95% CI = 1.12 to 1.17) among Caucasians. Cumulative HCTZ dose of 50 000 mg or more was associated with an increased risk of SCC in the overall population (IRR = 1.19, 95% CI = 1.05 to 1.35) and among Caucasians (IRR = 1.27, 95% CI = 1.10 to 1.47).

Conclusions

Among Caucasians, we identified small increased risks of BCC and SCC with HCTZ compared with ACEI. Appropriate risk mitigation strategies should be taken while using HCTZ.

Using and improving distributed data networks to generate actionable evidence: the case of real-world outcomes in the Food and Drug Administration's Sentinel system

The US Food and Drug Administration (FDA) Sentinel System uses a distributed data network, a common data model, curated real-world data, and distributed analytic tools to generate evidence for FDA decision-making. Sentinel system needs include analytic flexibility, transparency, and reproducibility while protecting patient privacy. Based on over a decade of experience, a critical system limitation is the inability to identify enough medical conditions of interest in observational data to a satisfactory level of accuracy. Improving the system's ability to use computable phenotypes will require an "all of the above" approach that improves use of electronic health data while incorporating the growing array of complementary electronic health record data sources. FDA recently funded a Sentinel System Innovation Center and a Community Building and Outreach Center that will provide a platform for collaboration across disciplines to promote better use of real-world data for decision-making.

Challenges Associated with the Safety Signal Detection Process for Medical Devices

Background

Previous safety issues involving medical devices have stressed the need for better safety signal detection. Various European Union (EU) national competent authorities have started to focus on strengthening the analysis of vigilance data. Consequently, article 90 of the new EU regulation states that the European Commission shall put in place systems and processes to actively monitor medical device safety signals.

Methods

A systematic literature review was conducted to synthesize the current state of knowledge and investigate the present tools used for medical device safety signal detection. An electronic literature search was performed in Embase, Medline, Cochrane, Web of science, and Google scholar from inception until January 2017. Articles that included terms related to medical devices and terms associated with safety were selected. A further selection was based on the abstract review. A full review of the remaining articles was conducted to decide on which articles finally to consider relevant for this review. Completeness was assessed based on the content of the articles.

Results

Our search resulted in a total of 20,819 articles, of which 24 met the inclusion criteria and were subject to data extraction and completeness scoring. A wide range of data sources, especially spontaneous reporting systems and registries, used for the detection and assessment of product problems and patient harms associated with the use of medical devices, were studied. Coding is remarkably heterogeneous, no agreement on the preferred methods for signal detection exists, and no gold standard for signal detection has been established thus far.

Conclusion

Data source harmonization, the development of gold standard signal detection methodologies and the standardization of coding dictionaries are amongst the recommendations to support the implementation of a new proactive approach to signal detection. The new safety surveillance system will be able to use real-world evidence to support regulatory decision-making across all jurisdictions.

Real-World Evidence: A Primer

SUMMARY

The insights that real-world data (RWD) can provide, beyond what can be learned within the traditional clinical trial setting, have gained enormous traction in recent years. RWD, which are increasingly available and accessible, can further our understanding of disease, disease progression, and safety and effectiveness of treatments with the speed and accuracy required by the health care environment and patients today. Over the decades since RWD were first recognized, innovation has evolved to take real-world research beyond finding ways to identify, store, and analyze large volumes of data. The research community has developed strong methods to address challenges of using RWD and as a result has increased the acceptance of RWD in research, practice, and policy. Historic concerns about RWD relate to data quality, privacy, and transparency; however, new tools, methods, and approaches mitigate these challenges and expand the utility of RWD to new applications. Specific guidelines for RWD use have been developed and published by numerous groups, including regulatory authorities. These and other efforts have shown that the more RWD are used and understood and the more the tools for handling it are refined, the more useful it will be.

Applying regulatory science in traditional chinese medicines for improving public safety and facilitating innovation in China: a scoping review and regulatory implications

BACKGROUND

The National Medical Products Administration (NMPA) in China has set to advance the regulatory capacity of traditional Chinese medicines (TCMs) with the adoption of regulatory science (RS). However, the priority of actions at the interface of RS and TCMs were yet to be defined. This research aims to identify the priority areas and summarize core actions for advancing RS for traditional medicines in China.

METHODS

A mixed approach of documentary analysis of government policies, regulations and official information about TCMs regulation in China, and a scoping review of literature using 4 databases (PubMed, ScienceDirect, Scopus and CNKI) on major concerns in TCMs regulation was employed.

RESULTS

Ten priority areas in the development of TCM-related regulatory science in China have been identified, including: (1) modernizing the regulatory system with a holistic approach; (2) advancing the methodology for the quality control of TCMs; (3) fostering the control mechanism of TCMs manufacturing process; (4) improving clinical evaluation of TCMs and leveraging real world data; (5) re-evaluation of TCMs injection; (6) developing evaluation standards for classic TCMs formula; (7) harnessing diverse data to improve pharmacovigilance of TCMs; (8) evaluating the value of integrative medicine in clinical practice with scientific research; (9) advancing the regulatory capacity to encourage innovation in TCMs; and (10) advancing a vision of collaboration for RS development in TCMs.

CONCLUSIONS

RS for TCMs in China encompasses revolution of operational procedures, advancement in science and technology, and cross-disciplinary collaborations. Such experiences could be integrated in the communications among drug regulatory authorities to promote standardized and scientific regulation of traditional medicines.

Utilization patterns and characteristics of users of biologic anti-inflammatory agents in a large, US commercially insured population

We report utilization patterns and characteristics of patients treated with biologic anti-inflammatory agents in a large commercially insured patient population in the United States. We identified adult (age ≥18 years) patients receiving biologic anti-inflammatory agents between 1 January 2012 and 31 March 2019 across the five Research Partners in the Biologic and Biosimilars Collective Intelligence Consortium's Distributed Research Network. We examined the number of incident use episodes for each biologic, as well as patient demographic and clinical characteristics. Curated data and analytic tools from the Food and Drug Administration's Sentinel System were used to perform the analyses. We identified 90,360 incident episodes of tumor necrosis factor-alpha inhibitors (TNFi) and 70,506 incident episodes of non-TNFi medications. Adalimumab was the most common TNFi drug (47% of all TNFi episodes) and showed a steady increase in utilization during the study period compared to other TNFi agents. Rituximab was the most commonly initiated non-TNFi medication (44% of non-TNFi episodes). Other non-TNFi agents, namely, ustekinumab, vedolizumab, and secukinumab, demonstrated notable increases in utilization over time. Biosimilar use was limited; we observed 653 incident episodes for infliximab-dyyb and 39 incident episodes for infliximab-abda. As more biologics enter the market, greater variation in the use of biologics with similar indications and between biologic originators and biosimilars is anticipated. Because information on efficacy and safety at the time of drug approval is limited, post-marketing surveillance and research is needed to monitor medication safety and evaluate effectiveness between biologic drugs using real-world data.

Comparability of Event Adjudication Versus Administrative Billing Claims for Outcome Ascertainment in the DAPT Study: Findings From the EXTEND-DAPT Study

BACKGROUND

Data from administrative claims may provide an efficient alternative for end point ascertainment in clinical trials. However, it is uncertain how well claims data compare to adjudication by a clinical events committee in trials of patients with cardiovascular disease.

METHODS

We matched 1336 patients ≥65 years old who received percutaneous coronary intervention in the DAPT (Dual Antiplatelet Therapy) Study with the National Cardiovascular Data Registry CathPCI Registry linked to Medicare claims as part of the EXTEND (Extending Trial-Based Evaluations of Medical Therapies Using Novel Sources of Data) Study. Adjudicated trial end points were compared with Medicare claims data with International Classification of Diseases, Ninth Revision codes from inpatient hospitalizations using time-to-event analyses, sensitivity, specificity, positive predictive value, negative predictive value, and kappa statistics.

RESULTS

At 21-month follow-up, the cumulative incidence of major adverse cardiovascular and cerebrovascular events (combined mortality, myocardial infarction, and stroke) was similar between trial-adjudicated events and claims data (7.9% versus 7.2%, respectively; P=0.50). Bleeding rates were lower using adjudicated events compared with claims (5.0% versus 8.6%, respectively; P<0.001). The sensitivity and positive predictive value of comprehensive billing codes for identifying adjudicated events were 65.6% and 85.7% for myocardial infarction, 61.5% and 47.1% for stroke, and 76.8% and 39.3% for bleeding, respectively. Specificity and negative predictive value for all outcomes ranged from 93.7% to 99.5%. All 39 adjudicated deaths were identified using Medicare data. Kappa statistics assessing agreement between events for myocardial infarction, stroke, and bleeding were 0.73, 0.52, and 0.49, respectively.

CONCLUSIONS

Claims data had moderate agreement with adjudication for myocardial infarction and poor agreement but high specificity for bleeding and stroke in the DAPT Study. Deaths were identified equivalently. Using claims data in clinical trials could be an efficient way to assess mortality among Medicare patients and may help detect other outcomes, although additional monitoring is likely needed to ensure accurate assessment of events.

A federated EHR network data completeness tracking system

OBJECTIVE

The study sought to design, pilot, and evaluate a federated data completeness tracking system (CTX) for assessing completeness in research data extracted from electronic health record data across the Accessible Research Commons for Health (ARCH) Clinical Data Research Network.

MATERIALS AND METHODS

The CTX applies a systems-based approach to design workflow and technology for assessing completeness across distributed electronic health record data repositories participating in a queryable, federated network. The CTX invokes 2 positive feedback loops that utilize open source tools (DQe-c and Vue) to integrate technology and human actors in a system geared for increasing capacity and taking action. A pilot implementation of the system involved 6 ARCH partner sites between January 2017 and May 2018.

RESULTS

The ARCH CTX has enabled the network to monitor and, if needed, adjust its data management processes to maintain complete datasets for secondary use. The system allows the network and its partner sites to profile data completeness both at the network and partner site levels. Interactive visualizations presenting the current state of completeness in the context of the entire network as well as changes in completeness across time were valued among the CTX user base.

DISCUSSION

Distributed clinical data networks are complex systems. Top-down approaches that solely rely on technology to report data completeness may be necessary but not sufficient for improving completeness (and quality) of data in large-scale clinical data networks. Improving and maintaining complete (high-quality) data in such complex environments entails sociotechnical systems that exploit technology and empower human actors to engage in the process of high-quality data curating.

CONCLUSIONS

The CTX has increased the network's capacity to rapidly identify data completeness issues and empowered ARCH partner sites to get involved in improving the completeness of respective data in their repositories.

Automation in signal management in pharmacovigilance-an insight

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

Underuse of oral anticoagulants in privately insured patients with atrial fibrillation: A population being targeted by the IMplementation of a randomized controlled trial to imProve treatment with oral AntiCoagulanTs in patients with Atrial Fibrillation (IMPACT-AFib)

BACKGROUND

Many studies showing underuse of oral anticoagulants (OACs) in patients with atrial fibrillation (AF) predated the advent of the non-vitamin K antagonist OACs. We retrospectively examined use of OACs in a large commercially insured population.

METHODS

Administrative claims data from 4 research partners participating in FDA-Catalyst, a program of the Sentinel Initiative, were queried in September 2017. Patients were included if they were ≥30 years old with ≥365 days of medical/pharmacy coverage, and had ≥2 diagnosis codes for AF, a CHA₂DS₂-VASc score ≥2, absence of contraindications to OAC use, and no evidence of OAC use in the 365 days before the index AF diagnosis. The main outcome measures of the current analysis were rates of OAC use in the prior 12 months of cohort identification and factors associated with non-use.

RESULTS

A total of 197,806 AF patients met the eligibility criteria prior to assessment of OAC treatment. Of these, 179,580 (91%) patients were ≥65 years old and 73,286 (37%) patients were ≥80 years old. Half of the patients (98,903) were randomized to the early intervention arm in the IMPACT-AFib trial and constitute the cohort for this analysis. Of these, 32,295 (33%) had no evidence of OAC use in the prior 12 months. Compared with patients with evidence of OAC use in the prior 12 months, patients without OAC use were more likely to be ≥80 years old, women, and have a history of anemia (51% vs 47%) and less likely to have diabetes (41% vs 44%), history of stroke or TIA (15% vs 19%), and history of heart failure (39% vs 48%).

CONCLUSIONS

Despite a high risk of stroke, one-third of privately insured patients with AF and no obvious contraindications to an OAC were not treated with an OAC. There is an unmet need for evidence-based interventions that could lead to greater use of OACs in patients with AF at risk for stroke.

Complementary Use of U.S. FDA's Adverse Event Reporting System and Sentinel System to Characterize Direct Oral Anticoagulants-Associated Cutaneous Small Vessel Vasculitis

BACKGROUND

Cutaneous small vessel vasculitis (CSVV) has been reported after exposure to direct oral anticoagulants (DOACs), such as dabigatran, rivaroxaban, apixaban, and edoxaban.

OBJECTIVE

We used the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) to describe clinical characteristics associated with CSVV among DOAC-exposed patients. Furthermore, we characterized this signal in the Sentinel System to relate the clinical data from the individual FAERS cases to population-based electronic healthcare data.

METHODS

We queried FAERS for all cases of CSVV associated with DOACs from U.S. approval date of each DOAC through March 16, 2018. Within the Sentinel System, we identified incident CSVV cases using ICD-9 and ICD-10 diagnosis codes among adults aged ≥ 30 years who received a DOAC in the prior 90 days between January 1, 2010, and June 30, 2018. We excluded patients with evidence of select autoimmune diagnoses in the 183 days prior to their CSVV diagnoses and reported patient characteristics in the 183-day period prior to CSVV diagnoses.

RESULTS

In FAERS, we identified 50 cases of CSVV reported with rivaroxaban (n=26), apixaban (n=14), dabigatran (n=9), and edoxaban (n=1). Approximately 50% of the cases reported time to onset within 10 days after DOAC exposure. When specified, the predominant type of CSVV reported was leukocytoclastic vasculitis (n=31), followed by Henoch-Schonlein purpura (n=4). Hospitalization occurred in most of the cases (n=37). Switching of the offending agent after the development of CSVV was reported (n=26). Three rivaroxaban (n=3) cases and one dabigatran case (n=1) reported positive rechallenge. In the Sentinel system, we identified 3659 CSVV cases with prior DOAC exposure, with 85% of events occurring within 10 days.

CONCLUSIONS

The assessment of FAERS cases, combined with the temporal clustering of the Sentinel System cases suggest a possible causal relationship of DOACs and CSVV. Future efforts should characterize the risk of CSVV among the various DOAC users.

The Impact of a Pharmacist-led Warfarin Educational Video in a Saudi Setting

Context:

Internationally, various warfarin education strategies have been described in the medical literature and delivered by a variety of health-care providers. However, none of these were tested in a Saudi setting.

Aim:

The aim of this study was to assess the impact of pharmacist interventions via an educational video on improving patient knowledge of and satisfaction with warfarin therapy and the international normalized ratio (INR).

Setting and Design:

This study adopted a prospective pre- and posttest design and enrolled 91 patients from an anticoagulant clinic at King Khaled University Hospital in Riyadh, Saudi Arabia, between September 2017 and February 2018.

Materials and Methods:

All patients completed the Anticoagulation Knowledge Assessment (AKA) and Anti-Clot Treatment Satisfaction (ACTS) scales. Subsequently, the patients watched a 10-min educational video containing basic information regarding warfarin and were given relevant informative booklets. The patients were reassessed after a mean follow-up period of approximately 52 days.

Results:

In total, 85 patients completed the study. The impact of the intervention on patient knowledge was highly significant (mean difference = 17.7%, 95% confidence interval (CI) = 21.75–13.58, P < 0.000). In addition, the patients showed significant increases in their ACTS benefits subscale scores (mean difference = 0.73, 95% CI = 1.22–0.24, P = 0.004). Despite being long-term warfarin users, the patients’ INRs had a greater tendency to be within the target range after the intervention (56.63% ± 35% vs. 64.72% ± 35% of the time; mean difference, 8.1 percentage points; effect size = 0.23). However, there was no significant effect on patients’ perceptions of the warfarin burden.

Conclusion:

This study provided evidence that a pharmacist-led audiovisual intervention via an educational video coupled with an informational booklet effectively improved patients’ knowledge retention and satisfaction with warfarin therapy benefits. Longer studies are needed to determine the impact of this intervention on patients’ perceptions of warfarin burdens and their INRs.

Assessing the impact of unmeasured confounders for credible and reliable real-world evidence

PURPOSE

We review statistical methods for assessing the possible impact of bias due to unmeasured confounding in real world data analysis and provide detailed recommendations for choosing among the methods.

METHODS

By updating an earlier systematic review, we summarize modern statistical best practices for evaluating and correcting for potential bias due to unmeasured confounding in estimating causal treatment effect from non-interventional studies.

RESULTS

We suggest a hierarchical structure for assessing unmeasured confounding. First, for initial sensitivity analyses, we strongly recommend applying a recently developed method, the E-value, that is straightforward to apply and does not require prior knowledge or assumptions about the unmeasured confounder(s). When some such knowledge is available, the E-value could be supplemented by the rule-out or array method at this step. If these initial analyses suggest results may not be robust to unmeasured confounding, subsequent analyses could be conducted using more specialized statistical methods, which we categorize based on whether they require access to external data on the suspected unmeasured confounder(s), internal data, or no data. Other factors for choosing the subsequent sensitivity analysis methods are also introduced and discussed, including the types of unmeasured confounders and whether the subsequent sensitivity analysis is intended to provide a corrected causal treatment effect.

CONCLUSION

Various analytical methods have been proposed to address unmeasured confounding, but little research has discussed a structured approach to select appropriate methods in practice. In providing practical suggestions for choosing appropriate initial and, potentially, more specialized subsequent sensitivity analyses, we hope to facilitate the widespread reporting of such sensitivity analyses in non-interventional studies. The suggested approach also has the potential to inform pre-specification of sensitivity analyses before executing the analysis, and therefore increase the transparency and limit selective study reporting.

Using real-world evidence for pharmacovigilance and drug safety-related decision making by a resource-limited health authority: 10 years of experience in Taiwan

PURPOSE

Real-world evidence has become increasingly relevant in regulatory decision making. Compared to large regulatory bodies, the national pharmacovigilance system in Taiwan is still under development, and the aim of this study is to demonstrate how a resource-limited health authority utilizes real-world evidence in decision making.

METHODS

We described different sources of real-world data available in Taiwan and illustrated the structural framework that integrates real-world evidence into Taiwan's national pharmacovigilance system. Additionally, we reviewed real-world studies conducted in the past 10 years and provided examples to show how these studies influenced drug safety-related decision making in Taiwan.

RESULTS

During the past 10 years, real-world evidence used when making drug safety-related regulatory decisions in Taiwan was mainly generated from nationwide claims databases, but other sources of real-world data, such as national registries and large electronic hospital databases, also became available recently. Different types of real-world evidence, including drug utilization studies, risk evaluation studies, and risk minimization measure evaluation studies, have been used to support regulatory decisions in Taiwan.

CONCLUSIONS

Through collaborations between the government and academics, Taiwan has started to integrate real-world evidence into the national pharmacovigilance system. However, future efforts, including linkages between different sources of real-world data and improvements in procedural and methodological practices, are needed to generate more regulatory-quality real-world evidence.

Understanding when real world data can be used to replicate a clinical trial: A cross-sectional study of medications approved in 2011

PURPOSE

To determine how commonly pre-approval clinical trials could potentially be replicated using real-world data from insurance claims databases.

METHODS

We conducted a cross-sectional study of medications approved by the FDA in 2011. For each medication, we reviewed the drug's label and the details of the pivotal clinical trials supporting its approval. We assessed whether each clinical trial could be replicated using an insurance claims databases by determining whether the following pivotal trial features could be reliably captured in claims data: study outcome, inclusion criteria, exclusion criteria, and the presence of an appropriate active comparator.

RESULTS

In 2011, 28 new medications were approved. The most common disease areas were oncology (N = 8, 29%), infectious disease (N = 5, 18%), and neurology (N = 4, 14%). The primary outcome of pre-approval clinical trials was identifiable in claims databases for six (21%) of the medications. Two (ticagrelor and linagliptin) had at least 80% of inclusion and exclusion criteria that could be identified in claims databases and had an available active comparator. The non-identifiable primary outcomes were related to patient-reported symptoms (N = 9, 32%), imaging findings (N = 5, 18%), laboratory values (N = 5, 18%), or other measurements (eg, blood pressure) not typically available in insurance claims databases (N = 4, 14%).

CONCLUSIONS

Among drugs FDA-approved in 2011, two (7%) had a pre-approval trial that could be replicated using insurance claims databases. In such qualifying trials, replication using claims databases could be useful in assessing whether they provide concordant results.

A Framework for Safety Evaluation Throughout the Product Development Life-Cycle

Evaluation of the safety profile of medicines is moving from a more reactive approach, where safety experts and statisticians have been primarily focusing on the review of clinical trial data and spontaneous reports, to a more proactive endeavor with cross-functional teams strategically evolving their understanding of the safety profile. They do this by anticipating the ultimate benefit-risk profile and its related risk management implications from the start of development. The proposed approach is based on assessments of integrated program-level safety data. These data stem from multiple sources such as preclinical information; clinical and spontaneous adverse event reports; epidemiological, real-world, and registry data; as well as, potentially, data from social media. Blended qualitative and quantitative evaluations allow integration of data from diverse sources. Adding to this, a collaborative multidisciplinary view, which is focused on continuous learning and decision-making via diverse safety management teams, ensures that companies look at their growing safety database and associated risk management implications from every relevant perspective. This multifaceted and iterative approach starts early in the development of a new medicine, continues into the post-marketing setting, and wanes as the product matures and the safety profile becomes more well understood. Not only does this satisfy regulatory requirements but, crucially, it provides the healthcare system and treated patients with a better understanding of the drug's safety profile.

Distributed Regression Analysis Application in Large Distributed Data Networks: Analysis of Precision and Operational Performance

Background

A distributed data network approach combined with distributed regression analysis (DRA) can reduce the risk of disclosing sensitive individual and institutional information in multicenter studies. However, software that facilitates large-scale and efficient implementation of DRA is limited.

Objective

This study aimed to assess the precision and operational performance of a DRA application comprising a SAS-based DRA package and a file transfer workflow developed within the open-source distributed networking software PopMedNet in a horizontally partitioned distributed data network.

Methods

We executed the SAS-based DRA package to perform distributed linear, logistic, and Cox proportional hazards regression analysis on a real-world test case with 3 data partners. We used PopMedNet to iteratively and automatically transfer highly summarized information between the data partners and the analysis center. We compared the DRA results with the results from standard SAS procedures executed on the pooled individual-level dataset to evaluate the precision of the SAS-based DRA package. We computed the execution time of each step in the workflow to evaluate the operational performance of the PopMedNet-driven file transfer workflow.

Results

All DRA results were precise (<10⁻¹²), and DRA model fit curves were identical or similar to those obtained from the corresponding pooled individual-level data analyses. All regression models required less than 20 min for full end-to-end execution.

Conclusions

We integrated a SAS-based DRA package with PopMedNet and successfully tested the new capability within an active distributed data network. The study demonstrated the validity and feasibility of using DRA to enable more privacy-protecting analysis in multicenter studies.

Different Strategies to Execute Multi-Database Studies for Medicines Surveillance in Real-World Setting: A Reflection on the European Model

Although postmarketing studies conducted in population‐based databases often contain information on patients in the order of millions, they can still be underpowered if outcomes or exposure of interest is rare, or the interest is in subgroup effects. Combining several databases might provide the statistical power needed. A multi‐database study (MDS) uses at least two healthcare databases, which are not linked with each other at an individual person level, with analyses carried out in parallel across each database applying a common study protocol. Although many MDSs have been performed in Europe in the past 10 years, there is a lack of clarity on the peculiarities and implications of the existing strategies to conduct them. In this review, we identify four strategies to execute MDSs, classified according to specific choices in the execution: (A) local analyses, where data are extracted and analyzed locally, with programs developed by each site; (B) sharing of raw data, where raw data are locally extracted and transferred without analysis to a central partner, where all the data are pooled and analyzed; (C) use of a common data model with study‐specific data, where study‐specific data are locally extracted, loaded into a common data model, and processed locally with centrally developed programs; and (D) use of general common data model, where all local data are extracted and loaded into a common data model, prior to and independent of any study protocol, and protocols are incorporated in centrally developed programs that run locally. We illustrate differences between strategies and analyze potential implications.

Concomitant Filled Prescriptions of Oxymorphone or Oxycodone with CYP3A Inhibitors and Inducers

BACKGROUND

Oxymorphone's metabolism does not involve the hepatic cytochrome P450 (CYP) system. The effect of this pharmacokinetic feature of oxymorphone on opioid prescribing is unknown.

OBJECTIVE

To assess the relative frequency with which oxymorphone and oxycodone (a CYP3A-metabolized opioid analgesic) were each prescribed to patients concomitantly receiving CYP3A-modifying drugs (i.e., inducers and inhibitors) to characterize opioid-prescribing patterns in patients at risk for CYP3A-related drug interactions.

METHODS

We analyzed the Sentinel Distributed Database from January 1, 2013, to December 31, 2016, to identify the proportion of patients with concomitant dispensing of selected CYP3A modifiers among initiators of oxymorphone. We then repeated the analysis using oxycodone instead of oxymorphone. We conducted sensitivity analyses that varied the washout periods for each opioid to account for potential opioid switching.

RESULTS

In the primary analysis, the proportion of patients with concomitant incident dispensings of oxymorphone and selected CYP3A modifiers was 3.26% (95% CI = 3.09%-3.43%), and the proportion of patients with incident dispensings of oxycodone and selected CYP3A modifiers was 2.82% (95% CI = 2.79%-2.85%). The difference between proportions was 0.43% (95% CI = 0.26%-0.60%). Sensitivity analyses that varied the washout periods for each opioid with respect to the other opioid to account for switching yielded similar results.

CONCLUSIONS

We observed similar proportions of patients using selected CYP3A modifiers concomitantly with both oxymorphone and oxycodone. While the CIs of the point estimates did not overlap, the absolute differences between the proportions were small.

DISCLOSURES

This project was supported by Task Order HHSF22301001T under Master Agreement HHSF223201400030I from the U.S. Food and Drug Administration (FDA). The FDA approved the study protocol, including the statistical analysis plan, and reviewed and approved the manuscript. Coauthors from the FDA participated in the results interpretation and in the preparation and decision to submit the manuscript for publication. Coyle, Money, Staffa, Meyer, and Woods are employed by the FDA. The other authors have no financial conflicts of interest to report. The views expressed are those of the authors and not necessarily those of the U.S. Department of Health and Human Services, U.S. Food and Drug Administration.

Predicting potential adverse events using safety data from marketed drugs

BACKGROUND

While clinical trials are considered the gold standard for detecting adverse events, often these trials are not sufficiently powered to detect difficult to observe adverse events. We developed a preliminary approach to predict 135 adverse events using post-market safety data from marketed drugs. Adverse event information available from FDA product labels and scientific literature for drugs that have the same activity at one or more of the same targets, structural and target similarities, and the duration of post market experience were used as features for a classifier algorithm. The proposed method was studied using 54 drugs and a probabilistic approach of performance evaluation using bootstrapping with 10,000 iterations.

RESULTS

Out of 135 adverse events, 53 had high probability of having high positive predictive value. Cross validation showed that 32% of the model-predicted safety label changes occurred within four to nine years of approval (median: six years).

CONCLUSIONS

This approach predicts 53 serious adverse events with high positive predictive values where well-characterized target-event relationships exist. Adverse events with well-defined target-event associations were better predicted compared to adverse events that may be idiosyncratic or related to secondary target effects that were poorly captured. Further enhancement of this model with additional features, such as target prediction and drug binding data, may increase accuracy.

Transparency in real-world evidence (RWE) studies to build confidence for decision-making: Reporting RWE research in diabetes

Transparency of real-world evidence (RWE) studies is critical to understanding how findings of a specific study were derived and is a necessary foundation to assessing validity and determination of whether decisions should be informed by the findings. In the present paper, we lay out strategies to improve clarity in the reporting of comparative effectiveness studies using real-world data that were generated by the routine operation of a healthcare system. This may include claims data, electronic health records, wearable devices, patient-reported outcomes or patient registries. These recommendations were discussed with multiple stakeholders, including regulators, payers, academics and journal editors, and endorsed by two professional societies that focus on RWE. We remind readers interested in diabetes research of the utility of conceptualizing a target trial that is then emulated by a RWE study when planning and communicating about RWE study implementation. We recommend the use of a graphical representation showcasing temporality of key longitudinal study design choices. We highlight study elements that should be reported to provide the clarity necessary to make a study reproducible. Finally, we suggest registering study protocols to increase process transparency. With these tools the readership of diabetes RWE studies will be able to more efficiently understand each study and be more able to assess a study's validity with reasonably high confidence before making decisions based on its findings.

Pharmacoepidemiology research: delivering evidence about drug safety and effectiveness in mental health

Research that provides an evidence base for the pharmacotherapy of people with mental disorders is needed. The abundance of digital data has facilitated pharmacoepidemiology and, in particular, observational research on the effectiveness of real-world medication. Advantages of pharmacoepidemiological research are the availability of large patient samples, and coverage of under-researched subpopulations in their naturalistic conditions. Such research is also cheaper and quicker to do than randomised controlled trials, meaning that issues regarding generic medication, stopping medication (deprescribing), and long-term outcomes are more likely to be addressed. Pharmacoepidemiological methods can also be extended to pharmacovigilance and to aid the development of new purposes for existing drugs. Drawbacks of observational pharmacoepidemiological studies come from the non-randomised nature of treatment selection, leading to confounding by indication. Potential methods for managing this drawback include active comparison groups, within-individual designs, and propensity scoring. Many of the more rigorous pharmacoepidemiology studies have been strengthened through multiple analytical approaches triangulated to improve confidence in inferred causal relationships. With developments in data resources and analytical techniques, it is encouraging that guidelines are beginning to include evidence from robust observational pharmacoepidemiological studies alongside randomised controlled trials. Collaboration between guideline writers and researchers involved in pharmacoepidemiology could help researchers to answer the questions that are important to policy makers and ensure that results are integrated into the evidence base. Further development of statistical and data science techniques, alongside public engagement and capacity building (data resources and researcher base), will be necessary to take full advantage of future opportunities.

Incidence of Serious Infections and Design of Utilization and Safety Studies for Biologic and Biosimilar Surveillance

BACKGROUND

There is a need for postmarketing evidence generation for novel biologics and biosimilars.

OBJECTIVE

To assess the feasibility, strengths, and limitations of the Biologics and Biosimilars Collective Intelligence Consortium (BBCIC) Distributed Research Network (DRN) to examine the utilization and comparative safety of immune-modulating agents among patients with autoimmune diseases.

METHODS

We conducted a retrospective cohort study among patients enrolled in health insurance plans participating in the BBCIC DRN between January 1, 2006, and September 30, 2015. Eligible patients were adult (≥18 years) new users of a disease-modifying nonbiologic and/or biologic agent with a prior diagnosis of rheumatoid arthritis (RA), other inflammatory conditions (psoriasis, psoriatic arthritis, ankylosing spondylitis), or inflammatory bowel disease (IBD). Follow-up started at treatment initiation and ended at the earliest of outcome occurrence (serious infection); treatment discontinuation; or switching, death, disenrollment, or end of study period. The study leveraged the FDA Sentinel System infrastructure for data management and analysis; descriptive statistics of patient characteristics and unadjusted incidence rates of study outcomes during follow-up were calculated.

RESULTS

Eligible patient drug episodes included 111,611 with RA (75% female), 61,050 with other inflammatory conditions (51% female), and 30,628 with IBD (52% female). Across all 3 cohorts, approximately half of the patient drug episodes initiated a biologic (50% in RA; 60% in psoriasis, psoriatic arthritis, ankylosing spondylitis; and 55% in IBD). The crude incidence rate of serious infection was 9.8 (9.5-10.0) cases per 100 person-years in RA, 7.1 (6.8-7.5) in other inflammatory conditions, and 14.2 (13.6-14.8) in IBD patients.

CONCLUSIONS

This study successfully identified large numbers of new users of biologics and produced results that were consistent with those from earlier published studies. The BBCIC DRN is a potential resource for surveillance of biologics.

DISCLOSURES

This study was funded by the Biologics and Biosimilars Collective Intelligence Consortium (BBCIC). HealthCore conducted this study in collaboration with Harvard Pilgrim Health Care. Zhang and Sridhar were employed by HealthCore at the time of this study. Haynes is employed by HealthCore funded by PCORI, the NIH, and the FDA. Barr and Eichelberger were employed by AMCP at the time of this study. Lockhart is employed by the BBCIC. Holmes and Clewell are employed by AbbVie. Accrott is an employee of and shareholder in Amgen. Marshall and Brown are employed by Harvard Pilgrim Health Care. Barr is a shareholder in Roche/Genentech. Curtis has received research grants from and consults with the following: Amgen, AbbVie, BMS, CORRONA, Lilly, Janssen, Myriad, Pfizer, Roche, Regeneron, and UCB. Brown has received research grants from GSK and Pfizer and consulting fees from Bayer, Roche, and Jazz Pharmaceuticals, along with funding from the Reagan-Udall Foundation for the FDA to conduct studies for medical product manufacturers, including Eli Lilly, Novartis, Abbvie, and Merck. Brown is also funded by PCORI, the NIH, and the FDA. McMahill-Walraven subcontracts with Harvard Pilgrim Health Care Institute for public health and safety surveillance distributed data network activtities and with the FDA, GSK, and Pfizer. She also reports fees from Reagan Udall Foundation for the FDA and the Patient Centered Outcomes Research Institute.

Emerging opportunities to harness real world data: An introduction to data sources, concepts, and applications

While randomized controlled trials (RCTs) are the gold standard for comparative effectiveness research, they are unable to provide the answers to all pertinent clinical and research questions. Real world evidence (RWE), that is, clinical evidence obtained outside RCTs and often through routine clinical practice, offers the potential to conduct observational studies that accelerate advances in care, improve outcomes for patients, and provide important insights that can answer important questions. Once appropriate information technology is available, real world data can be cost-effective to generate. RWE serves a vital role in the evaluation of treatment strategies for which there are no RCTs and for describing patterns of care. RWE also serves as an important adjunct to RCTs and can be used to determine if benefits seen in RCTs extend to clinical practice, provide insight into the findings of RCTs, generate hypotheses for future RCTs, and inform the design of future RCTs. These potential benefits must be balanced against some of the important limitations of RWE, including variable data quality, lack of granularity for important clinical variables, and the potential for bias and confounding. By using appropriate analytic techniques and study design, these limitations can be minimized but not eliminated. Going forward, RWE studies may be enhanced by using rigorous data quality standards, incorporating randomization, developing more prospective registries, and better leveraging data from electronic health records.

Can We Rely on Results From IQVIA Medical Research Data UK Converted to the Observational Medical Outcome Partnership Common Data Model?: A Validation Study Based on Prescribing Codeine in Children

Exploring and combining results from more than one real‐world data (RWD) source might be necessary in order to explore variability and demonstrate generalizability of the results or for regulatory requirements. However, the heterogeneous nature of RWD poses challenges when working with more than one source, some of which can be solved by analyzing databases converted into a common data model (CDM). The main objective of the study was to evaluate the implementation of the Observational Medical Outcome Partnership (OMOP) CDM on IQVIA Medical Research Data (IMRD)‐UK data. A drug utilization study describing the prescribing of codeine for pain in children was used as a case study to be replicated in IMRD‐UK and its corresponding OMOP CDM transformation. Differences between IMRD‐UK source and OMOP CDM were identified and investigated. In IMRD‐UK updated to May 2017, results were similar between source and transformed data with few discrepancies. These were the result of different conventions applied during the transformation regarding the date of birth for children younger than 15 years and the start of the observation period, and of a misclassification of two drug treatments. After the initial analysis and feedback provided, a rerun of the analysis in IMRD‐UK updated to September 2018 showed almost identical results for all the measures analyzed. For this study, the conversion to OMOP CDM was adequate. Although some decisions and mapping could be improved, these impacted on the absolute results but not on the study inferences. This validation study supports six recommendations for good practice in transforming to CDMs.

Use of FDA's Sentinel System to Quantify Seizure Risk Immediately Following New Ranolazine Exposure

INTRODUCTION

Neurological complications including seizures have been reported with ranolazine. We sought to quantify the risk of seizure-related hospitalizations or emergency department events following ranolazine exposure in the Sentinel System (2006-2015).

STUDY DESIGN AND SETTING

Eligibility criteria were new use of ranolazine after 183 days washout period and absence of seizure diagnoses, anti-epileptic drugs, or seizure-related disorders during the baseline period.

RESULTS

Among 52,155 ranolazine users, we identified 28 seizures in the 1-32 days after new ranolazine dispensing: 12 occurring in days 1-10 (high-risk window), 11 in days 11-20 (moderate-risk window) and 5 in the control window (days 21-32). Assuming an equal likelihood of seizure events across the 32-day observation window, we estimate an attributable risk of 0.9 excess cases per 10,000 exposed users. Using a self-controlled risk interval design with exact logistic regression, seizures were elevated in the high-risk window (relative risk [RR] 2.88 (95% confidence interval [CI] 1.01-8.33) compared with the control window. No significant increased risk was observed in the moderate window. Half of the seizure cases had a diagnosis of renal disease, although seizure risk was not significant (RR 3.20 [CI 0.82-14.01]). A majority of patients in both risk windows were 75 years or older.

CONCLUSION

Our study suggests risk among younger ranolazine patients is rare. Given the imprecision of the risk estimates, we interpret the elevated seizure risk following ranolazine exposure with caution. Further analysis in a larger elderly population is warranted.

Big Data in the Assessment of Pediatric Medication Safety

Big data (BD) in pediatric medication safety research provides many opportunities to improve the safety and health of children. The number of pediatric medication and device trials has increased in part because of the past 20 years of US legislation requiring and incentivizing study of the effects of medical products in children (Food and Drug Administration Modernization Act of 1997, Pediatric Rule in 1998, Best Pharmaceuticals for Children Act of 2002, and Pediatric Research Equity Act of 2003). There are some limitations of traditional approaches to studying medication safety in children. Randomized clinical trials within the regulatory context may not enroll patients who are representative of the general pediatric population, provide the power to detect rare safety signals, or provide long-term safety data. BD sources may have these capabilities. In recent years, medical records have become digitized, and cell phones and personal devices have proliferated. In this process, the field of biomedical science has progressively used BD from those records coupled with other data sources, both digital and traditional. Additionally, large distributed databases that include pediatric-specific outcome variables are available. A workshop entitled "Advancing the Development of Pediatric Therapeutics: Application of 'Big Data' to Pediatric Safety Studies" held September 18 to 19, 2017, in Silver Spring, Maryland, formed the basis of many of the ideas outlined in this article, which are intended to identify key examples, critical issues, and future directions in this early phase of an anticipated dramatic change in the availability and use of BD.

Analytic and Data Sharing Options in Real-World Multidatabase Studies of Comparative Effectiveness and Safety of Medical Products

A wide range of analytic and data sharing options are available in nonexperimental multidatabase studies designed to assess the real-world benefits and risks of medical products. Researchers often consider six scientific domains when choosing among these options-study design, exposure type, outcome type, covariate summarization technique, covariate adjustment method, and data sharing approach. This article reviews available analytic and data sharing options and discusses key scientific and practical considerations when choosing among these options in multidatabase studies of comparative effectiveness and safety of medical products. The scientific considerations must be balanced against what the data-contributing sites are able or willing to share. While pooling of person-level data sets remains the most familiar and analytically flexible approach, newer analytic and data sharing approaches that share less granular summary-level information may be equally valid and preferred in some multidatabase studies, especially when sharing of person-level data is challenging or infeasible.

An evaluation of statistical approaches to postmarketing surveillance

Safety of medical products presents a serious concern worldwide. Surveillance systems of postmarket medical products have been established for continual monitoring of adverse events (AEs) in many countries, and the proliferation of electronic health record systems further facilitates continual monitoring for AEs. We review existing statistical methods for signal detection that are mostly in use in postmarketing safety surveillance of spontaneously reported AEs and we study their performance characteristics by simulation. We compare those with the likelihood ratio test (LRT) method (appropriately modified for use in pharmacovigilance) and use three different methods to generate data (AE based, drug based, and a modification of the method of Ahmed et al). Performance metrics include type I error, power, sensitivity, and false discovery rate, among others. The results show superior performance of the LRT method in almost all simulation experiments. An application to the FDA Adverse Event Reporting System database is illustrated using rhabdomyolysis-related preferred terms reported to FDA during the third-quarter of 2014 to the first-quarter of 2017 for statin drugs. We present a critical discussion and recommendations for use of these methods.

Stretta procedure versus proton pump inhibitors for the treatment of nonerosive reflux disease: A 6-month follow-up

To compare the Stretta procedure with proton pump inhibitors for the treatment of nonerosive reflux disease (NERD).From July 2018 to April 2019, patients diagnosed with NERD and referred for treatment were enrolled. They were treated with either Stretta procedure or proton pump inhibitor (PPI) medication and followed-up for 6 months. The symptom control, quality of life, lower esophageal sphincter (LES) pressure, 24-hour pH parameters, PPI usage and satisfaction rate were evaluated. The complications were assessed. The outcomes of the 2 groups were analyzed and compared.Twenty-eight patients in the Stretta group and 21 patients in the PPI group completed the 6-month follow-up. No severe adverse events occurred in both groups. Both interventions were effective in improvement of symptom and quality of life. The symptom score improvement was significantly superior in the Stretta group compared to the PPI group (6.3 ± 3.4 vs 8.5 ± 4.1, P = .03). LES pressure increased significantly in the Stretta group compared to the PPI group (14.2 ± 4.4 mm Hg vs 10.0 ± 4.0 mm Hg, P < .01). Although both interventions improved 24-hour pH parameters, including number of acid episodes (P = .27), acid exposure time (P = .39), and DeMeester score (P = .28), no difference was found between the 2 groups. Complete PPI cessation rate (82% vs 52%, P = .03) as well as satisfaction rate (89% vs 57%, P = .02) was much higher in Stretta group than those in the PPI groupThe Stretta procedure was safe and effective in the short term for the management of NERD. The Stretta procedure resulted in higher LES pressure and achieved better improvement of symptom control and PPI cessation than did PPI in the short term.

Privacy-protecting estimation of adjusted risk ratios using modified Poisson regression in multi-center studies

BACKGROUND

Multi-center studies can generate robust and generalizable evidence, but privacy considerations and legal restrictions often make it challenging or impossible to pool individual-level data across data-contributing sites. With binary outcomes, privacy-protecting distributed algorithms to conduct logistic regression analyses have been developed. However, the risk ratio often provides a more transparent interpretation of the exposure-outcome association than the odds ratio. Modified Poisson regression has been proposed to directly estimate adjusted risk ratios and produce confidence intervals with the correct nominal coverage when individual-level data are available. There are currently no distributed regression algorithms to estimate adjusted risk ratios while avoiding pooling of individual-level data in multi-center studies.

METHODS

By leveraging the Newton-Raphson procedure, we adapted the modified Poisson regression method to estimate multivariable-adjusted risk ratios using only summary-level information in multi-center studies. We developed and tested the proposed method using both simulated and real-world data examples. We compared its results with the results from the corresponding pooled individual-level data analysis.

RESULTS

Our proposed method produced the same adjusted risk ratio estimates and standard errors as the corresponding pooled individual-level data analysis without pooling individual-level data across data-contributing sites.

CONCLUSIONS

We developed and validated a distributed modified Poisson regression algorithm for valid and privacy-protecting estimation of adjusted risk ratios and confidence intervals in multi-center studies. This method allows computation of a more interpretable measure of association for binary outcomes, along with valid construction of confidence intervals, without sharing of individual-level data.

Medication class enrichment analysis: a novel algorithm to analyze multiple pharmacologic exposures simultaneously using electronic health record data

Objective

Observational studies analyzing multiple exposures simultaneously have been limited by difficulty distinguishing relevant results from chance associations due to poor specificity. Set-based methods have been successfully used in genomics to improve signal-to-noise ratio. We present and demonstrate medication class enrichment analysis (MCEA), a signal-to-noise enhancement algorithm for observational data inspired by set-based methods.

Materials and Methods

We used The Health Improvement Network database to study medications associated with Clostridium difficile infection (CDI). We performed case-control studies for each medication in The Health Improvement Network to obtain odds ratios (ORs) for association with CDI. We then calculated the association of each pharmacologic class with CDI using logistic regression and MCEA. We also performed simulation studies in which we assessed the sensitivity and specificity of logistic regression compared to MCEA for ORs 0.1-2.0.

Results

When analyzing pharmacologic classes using logistic regression, 47 of 110 pharmacologic classes were identified as associated with CDI. When analyzing pharmacologic classes using MCEA, only fluoroquinolones, a class of antibiotics with biologically confirmed causation, and heparin products were associated with CDI. In simulation, MCEA had superior specificity compared to logistic regression across all tested effect sizes and equal or better sensitivity for all effect sizes besides those close to null.

Discussion

Although these results demonstrate the promise of MCEA, additional studies that include inpatient administered medications are necessary for validation of the algorithm.

Conclusions

In clinical and simulation studies, MCEA demonstrated superior sensitivity and specificity for identifying pharmacologic classes associated with CDI compared to logistic regression.

Descriptive Analysis of Long- and Intermediate-Acting Insulin and Key Safety Outcomes in Adults with Type 2 Diabetes Mellitus

BACKGROUND

As new biosimilar and follow-on insulins enter the market, more data are needed on safety, effectiveness, and patterns of use for these products to inform prescriber and patient decision-making regarding treatment. Additionally, data are needed regarding real-world patterns of use to inform future studies comparing the safety and effectiveness of bio-similars to already approved agents for diabetes treatment.

OBJECTIVE

To analyze the medication use patterns, adverse events, and availability of glycated hemoglobin (A1c) values for adult patients with type 2 diabetes mellitus (T2DM) who use long-acting insulin (LAI) or neutral protamine Hagedorn (NPH), an intermediate-acting insulin.

METHODS

We used the Biologics and Biosimilars Collective Intelligence Consortium's (BBCIC) distributed research network (DRN) for this descriptive analysis. The analysis time frame was January 1, 2011, to September 30, 2015, and included patients continuously insured for at least 183 days before the first date of a filled prescription for LAI or NPH insulin alone or with rapid- or short-acting insulin or sulfonylureas, whether newly starting insulin or switching to a different product. Insulin exposure episodes were the unit of analysis, and patients were classified in cohorts according to treatment. We followed patients until end of health plan enrollment or the end of the study period. We used occurrence of a study outcome, switch to another medication regimen, discontinuation of the current medication, or study end date to mark the end of an insulin episode. We describe demographics and availability of A1c values for analysis. Study outcomes included severe hypoglycemic events and major adverse cardiac events (MACE).

RESULTS

We identified 103,951 patients with T2DM from a database of 39.1 million patients with commercial or Medicare Advantage pharmacy and medical benefits, who contributed 279,533 unique insulin exposure episodes. Most episodes (89%) included patients using LAI, and 52% of patients contributed data to 2 or more exposure cohorts. Insulin episodes lasted an average of 3.5 months, and patients had an average follow-up of 8.6 months. The unadjusted rate of severe hypoglycemic events requiring medical attention was 96.9 per 10,000 patient-years at risk (10kPYR). The unadjusted incident MACE rate was 676.9 events per 10kPYR. 38,330 T2DM patients in the BBCIC DRN had a baseline A1c available, and of those, less than 50% had a follow-up A1c result.

CONCLUSIONS

Among patients with T2DM, our observed insulin patterns of use and rates of severe hypoglycemic outcomes and MACE are consistent with other studies. We noted a paucity of A1c results available, which implies that additional data sources may be needed to augment the BBCIC DRN.

DISCLOSURES

This study was coordinated and funded by the Biologics and Biosimilars Collective Intelligence Consortium (BBCIC) and represents the independent findings of the BBCIC Insulins Principal Investigator and the BBCIC Insulins Research Team. Lockhart is employed by the BBCIC and the Academy of Managed Care Pharmacy (AMCP). Eichelberger was employed by the BBCIC and AMCP at the time of this study. McMahill-Walraven is employed by Aetna, a CVS Health business. Panozzo, Marshall, and Brown are employed by Harvard Pilgrim Healthcare Institute. Aetna was reimbursed for data and analytic support from Harvard Pilgrim Healthcare Institute and the Reagan Udall Foundation for the U.S. Food and Drug Administration. Aetna receives external funding through research grants and subcontracts with Harvard Pilgrim Healthcare Institute, which are funded by the FDA, NIH, PCORI, BBCIC, Pfizer, and GSK; the Reagan-Udall Foundation for IMEDS; and PCORI for the ADAPTABLE Study. This work was previously presented as a poster at AMCP Nexus 2018; October 22-25, 2018; in Orlando, FL.

Reproducing Protocol-Based Studies Using Parameterizable Tools-Comparison of Analytic Approaches Used by Two Medical Product Surveillance Networks

The US Sentinel System and the Canadian Network for Observational Drug Effect Studies (CNODES) are two medical product safety surveillance networks. Using Sentinel's preprogrammed, parameterizable analytic tools, we reproduced two protocol-based studies conducted by CNODES to assess the risks of acute pancreatitis and heart failure (HF) associated with the use of incretin-based drugs, compared with use of ≥ 2 oral hypoglycemic agents. Results from the replication new-user cohort analyses aligned with those from the CNODES nested case-control studies. The adjusted hazard ratios were 0.95 (0.81-1.12; vs. 1.03 (0.87-1.22) in CNODES) for acute pancreatitis and 0.91 (0.84-1.00; vs. 0.82 (0.67-1.00) in CNODES) for HF among patients without HF history. The CNODES's common protocol approach allows studies tailored to specific safety questions, whereas the Sentinel's common data model plus pretested program approach enables more rapid analysis. Despite these differences, it is possible to obtain comparable results using both approaches.

A comparison of two algorithms to identify sudden cardiac deaths in computerized databases

PURPOSE

Two previously validated algorithms to identify sudden cardiac death using administrative data showed high positive predictive value. We evaluated the agreement between the algorithms using data from a common source population.

METHODS

We conducted a cross-sectional study to assess the percent agreement between deaths identified by two sudden cardiac death algorithms using Tennessee Medicaid and death certificate data from 2007 through 2014. The source population included all deceased patients aged 18 to 64 years with Medicaid enrollment in the 6 months prior to death. To identify sudden cardiac deaths, algorithm 1 used only hospital/emergency department (ED) claims from encounters at the time of death, and algorithm 2 required death certificates and used claims data for specific exclusion criteria.

RESULTS

We identified 34 107 deaths in the source population over the study period. The two algorithms identified 4372 potential sudden cardiac deaths: Algorithm 1 identified 3117 (71.3%) and algorithm 2 identified 1715 (39.2%), with 460 (10.5%) deaths identified by both algorithms. Of the deaths identified by algorithm 1, 1943 (62.3%) had an underlying cause of death not specified in algorithm 2. Of the deaths identified by algorithm 2, 1053 (61.4%) had no record of a hospital or ED encounter at the time of death, and 202 (11.8%) had a discharge diagnosis code not specified in algorithm 1.

CONCLUSIONS

We found low agreement between the two algorithms for identification of sudden cardiac deaths because of differences in sudden cardiac death definitions and data sources.