Yale University-Mayo Clinic CERSI

Center of Excellence in Regulatory Science and Innovation

The Yale-Mayo CERSI conducts high-quality, high-impact collaborative research to support several areas of focus in the FDA strategic plan for regulatory science. Research topic areas include: adoption/de-adoption of FDA-approved medical products, postmarket surveillance, development and application of novel analytics, and patient-centered regulatory decision-making.

For comments or questions pertaining to our CERSI research projects, contact the Yale-Mayo Clinic CERSI at CERSI@yale.edu.

Current Projects

Generic drugs are approved based on bioequivalence to the brand name agents. However, there are sometimes concerns among patients and clinicians that generic and brand name drugs are not equivalent and have differing effects. Using a large administrative claims data source that includes information on privately insured and Medicare Advantage enrollees of all ages, we will characterize patterns of use of generic and brand-name L-thyroxine products and then compare the effectiveness and safety of generic and brand-name L-thyroxine among both new users and recent switchers.

Generic drugs are approved based on bioequivalence to the brand name agents. However, there are sometimes concerns among patients and clinicians that generic and brand name drugs are not equivalent and have differing effects. Building on our ongoing research, we will test different methods to compare the effectiveness and safety of generic and brand name drugs using a large administrative claims data source that includes information on privately insured and Medicare Advantage enrollees of all ages. Further, we will create packages that will ease the implementation of these methods for future research.

Non-Federal Entity Collaborators: Anirban Basu, PhD, Co-Investigator (University of Washington)

Overdose, or the syndrome where a drug causes loss of consciousness/coma, is the leading cause of accidental death in the U.S. It is clear that medications- not just illegal drugs- are increasingly involved in overdoses. As such, the FDA has pledged to re-examine the role of medications, particularly opioids and benzodiazepines, in the dramatic increase in overdoses. One of the main challenges in this work is that while we may know who has experienced an overdose, it has been very difficult to assess what medications they may have had access to or what they were taking. The primary reason for this difficulty is that there are many different sources of data on medication receipt and it is challenging to link these datasets. In this project, we will overcome those challenges, linking a variety of datasets that will generate a more accurate assessment of which medications at which doses put patients at risk for- or protect them from- overdose. Collaborators from the University of Connecticut are involved in this project.
In 2012, the FDA approved a Risk Evaluation and Mitigation Strategy (REMS) to provide prescriber education to help reduce adverse outcomes resulting from misuse and abuse of extended-release (ER) opioid analgesics. Recent studies, one conducted by FDA using the Medicare database, and one conducted by FDA using the Sentinel database, focused on inappropriate prescribing of ER opioid analgesics that require prior opioid tolerance to patients who do not appear (based on prescription dispensing records) to be opioid-tolerant. Those studies suggest a high incidence of potentially unsafe prescribing behavior for ER opioid analgesics to opioid non-tolerant patients, but recommend further validation in other datasets. The objective of this new study is twofold: (1) examine ER opioid analgesic prescribing patterns in the OptumLabs claims database of commercially insured and Medicare Advantage patients using the Willy and LaRochelle approach, and (2) provide further insight to understand the context of prescribing behavior using clinical data from Electronic Health Records (cdEHR) including data extracted from raw provider notes using advanced Natural Language Processing (NLP) techniques. Collaborators from OptumLabs are involved in this project.
Medical devices play an important role in advancing patient care and reducing morbidity and mortality. All devices must receive FDA approval before they can be marketed. Once medical devices are marketed, it is necessary and important to monitor their safety and effectiveness in real-world clinical practice. Safety concerns may emerge when these devices are used in significantly more patients than were studied before marketing and when longer duration of follow-up is available. FDA therefore requires post-market surveillance of medical devices to ascertain if devices perform as intended and detect any unexpected or serious adverse effects. While the FDA currently employs multiple post-market surveillance strategies, there is opportunity to strengthen this important area given its central role in FDA regulation and, therefore, it is an FDA regulatory science priority. An important component of post-market surveillance is obtaining health data from medical records and insurance claims; these data should also include longitudinal patient-reported outcomes since the goal of these devices is to help people live better and longer.

A novel sync-for-science mobile application has been developed that unobtrusively enables patients to provide their own outcomes (through short questionnaires and through synchronizing data from mobile health trackers) to the FDA after they have received a procedure that utilizes medical devices. In addition, with user permission, this application draws data from the electronic medical record to complement patient-reported data. In this project, we will conduct a pilot study testing this mobile health application to enable the FDA to conduct post-market surveillance of two procedures that use medical devices: the multiple devices (including sutures and stapler) used to perform bariatric surgeries (either sleeve gastrectomy or gastric bypass) in patients seeking weight loss and an ablation catheter when used in patients with atrial fibrillation seeking a return to sinus rhythm. Patients will be enrolled before receiving each of the devices and then will be asked to report specific symptoms related to their need for the procedure and those that may be expected at baseline (enrollment, which is pre-procedure), and 1, 4, and 8 weeks post-procedure. Additionally, patients will be asked 2-3 short questions every 3-4 days for the first 30 days post-procedure related to post-procedure symptoms. We will also test if these patients’ electronic health record data from multiple health systems where they receive care can be synchronized into a research-ready database. Patients will also be provided with syncable devices to provide additional insights into their health and health outcomes. Finally, we will test the feasibility of obtaining medication data from pharmacies or the current needs to create a functional system that can integrate pharmacy data into the mobile application. Integration of these multiple data sources (patient-reported outcomes, wearable/mobile device data, electronic health record data, and pharmacy data) have the potential to ultimately enable a more robust and thorough post-marketing surveillance strategy by leveraging the potential of digital health technologies. Non-Federal entities involved in this project include Me2Health (the developers of the Hugo mHealth application), Johnson & Johnson (collaborator- provides input on project and funds to Me2Health for development of Hugo mHealth application) and AliveCor (donated Kardia Mobile devices for this project).

ClinicalTrials.gov Identifier: NCT03436082

Non-Federal Entity Collaborators: Karla Childers, MSJ, Paul Coplan, ScD, MBA, and Stephen Johnston, MSc (Johnson and Johnson)
The FDA has long relied on electronic healthcare data (e.g., data from claims and EHRs) to examine the safety of drugs in the post-market setting as part of its pharmacoepidemiology contracts and Sentinel Initiative. The passing of the 21st Century Cures Act and the proposed commitments included in the reauthorization of the Prescription Drug User Fee Act require FDA to give further attention and issue guidance on this topic. Recently, linking multiple types of electronic healthcare data is getting more attention, due to its ability to improve the measurement of exposures, outcomes and confounders.

This project seeks to understand how linking laboratory data to insurance claims can help examine a drug’s performance after approval. We will conduct a case study looking at renal function and the performance of oral anticoagulant drugs in patients with atrial fibrillation (AF). The 30 million patients with AF are at nearly a five-fold risk of stroke. Lifelong oral anticoagulation is recommended in most patients with AF to prevent stroke. However, treatment decisions can be complicated by the presence of chronic kidney disease (CKD), as poor renal function increases the risks of both stroke and bleeding ( a major complication of oral anticoagulation treatment), and may change the risk-benefit ratio of different treatment options. This project proposes to answer three important questions pertaining to the impact of renal function on oral anticoagulation treatment in patients with AF. First, in patients with severe-to-no renal impairment, we will assess the comparative effectiveness and safety of different oral anticoagulant drugs across the range of renal function. Second, in patients on dialysis who have substantial risks of both stroke and bleeding, we will compare different potential treatment options, including warfarin, non-vitamin K antagonist oral anticoagulants (NOACs), and no treatment. Third, we will use novel analytic methods to identify patient characteristics that contribute to the heterogeneity in treatment effects.

We will answer these questions by leveraging the power of a large observational database, OptumLabs Data Warehouse, which contains over 160 million privately insured and Medicare Advantage enrollees of all ages, races, and from 50 states and the USRDS data. The proposed work could provide important new evidence on the safety and effectiveness of oral anticoagulants in relation to renal function and will help physicians and patients make a choice among different treatment options.

Non-Federal Entity Collaborators: Brahmajee Nallamothu, MD, MPH, Rajiv Saran, MD, MS, MBBS, and Konstantinos Siontis, MD - Co-Investigators (University of Michigan)
Atrial fibrillation (AF) is the most common abnormal heart rhythm in humans and is a leading cause of stroke. Over the last two decades, catheter ablation has emerged as an effective modality for treating some patients with AF and its use has been rapidly increasing, especially with the introduction of new technology that allows more effective treatment. However, the “real-world” incidence of short- and intermediate- term complications following AF ablation is not well-characterized in the modern era. Specifically, the development of atrioesophageal fistula – an abnormal connection between the heart and the esophagus (food pipe) – is a rare but deadly complication of catheter ablation of AF. It is widely believed that the diagnosis of atrioesophageal fistula is often missed and/or underreported, and that the true incidence of atrioesophageal fistula in the “real -world” is higher than the limited study data suggest. This research proposal aims to understand the incidence of short- and intermediate-term complications of AF ablation, with an emphasis on atrioesophageal fistula.

Non-Federal Entity Collaborators: James Hummel, MD- Co-Investigator (University of Wisconsin School of Medicine)
When the heart muscle is severely and/or suddenly weakened, it is unable to deliver blood, oxygen, and nutrients to the body, a condition referred to as cardiogenic shock. There are many causes of cardiogenic shock, with the most common being a heart attack. However, a clinically meaningful classification system for cardiogenic shock has yet to be developed and we treat all patients who have an abnormally low blood pressure or oxygen requirement as having cardiogenic shock. It is highly likely, though, that there are distinct subgroups of patients experiencing cardiogenic shock that represent distinct combinations of risk factors and cardiac status. Because all patients with cardiogenic shock are lumped together, many of the clinical trials that have been conducted to investigate the impact of various interventions have failed to show benefit. This may derive from the fact that there is a mixture of different types of patients or different types of cardiogenic shock, some of which may respond quite favorably to an intervention while others may not have a favorable response. By refining our categorization of cardiogenic shock, we hope to be able to provide physicians and patients better prognostic information and strategies to improve clinical outcomes.

This project proposes to advance our understanding of cardiogenic shock with the ultimate aim of enabling patients and providers to estimate risk and develop optimal, individualized treatment plans. Specifically, we will use the NCDR CathPCI and ACTION-GWTG registries, two national registries of patients with acute myocardial infarction (ACTION-GWTG) and patients undergoing stent procedures (CathPCI). Given the substantial morbidity and mortality associated with cardiogenic shock, the proposed work will enable us to advance our understanding of this condition, develop better treatment approaches, and will enhance regulatory science by improving the safety and effectiveness of mechanical circulatory support devices through helping target them to patients who will benefit. Collaborators from Texas A&M University are involved in this project.

Related Projects

The ability to access population-scale data is essential for many emerging areas of biomedical research. Precision medicine, which focuses on identifying the optimal treatment pathway for very refined patient phenotypes, or those with specific genetic variation, require large sample sizes to identify differences in patient outcomes. Similarly, other biomedical research areas that have a limited number of events, such as drug development and pharmacovigilance surveillance, or projects that rely on high dimensional data, have an intrinsic need for very large sample sizes. Often, individual researchers and institutions are unable to identify a sufficient number of participants within local patient populations. However, data sharing and integration between clinical research data management systems (CDMs) and electronic health record (EHR) systems, which would allow systems to coordinate and collaborate to conduct research of this type, remains a challenging issue. To address this problem, the medical informatics community has made significant advances through the development of data and semantic interoperability standards to encourage multi-institutional collaborations and enable researchers to rapidly identify relevant patient cohorts. In this project, we will validate the accuracy and efficiency of these systems for drug safety surveillance, by implementing a universal CDM mapping approach and comparing it to gold standard data from the clinical data warehouse.