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: advancing clinical and post-market surveillance of drugs and biologics, advancing clinical and post-market surveillance of medical devices and diagnostic tests, development and application of novel analytics, and fostering patient-centered decision making.

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

Current Projects

Advancing Clinical and Post-Market Surveillance of Drugs and Biologics

Characterizing use, safety and efficacy of brand-name and generic drugs used to treat hypothyroidism

FDA Priority Area/Regulatory Science Challenge: 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 may have differing effects. One example of this is the clinical preference to prescribe brand-name L-thyroxine as opposed to the generic formulation of the drug. This disconnect between FDA and expert recommendations is likely to cause confusion among patients and may be resolved through comparative effectiveness and safety research among currently available L-thyroxine products.

This project uses Real World Evidence via structured electronic health record and administrative claims data to better understand brand and generic use of FDA-regulated drugs to treat hypothyroidism and examine comparative effectiveness and safety. The proposed project will advance our knowledge of the effectiveness and safety of generic drugs.

Project Description: 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 new users.

Accomplishments: Click here for publication

Use of instrumental variable approaches to assess the safety and efficacy of brand-name and generic drugs used to treat hypothyroidism

FDA Priority Area/Regulatory Science Challenge: 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 may have differing effects. One example of this is the clinical preference to prescribe brand-name L-thyroxine as opposed to the generic formulation of the drug. This disconnect between FDA and expert recommendations is likely to cause confusion among patients and may be resolved through comparative effectiveness and safety research among currently available L-thyroxine products.

This project uses Real World Evidence via structured electronic health record and administrative claims data to better understand brand and generic use of FDA-regulated drugs to treat hypothyroidism and create ways to aid in the implementation of instrumental variable methods for future research. The proposed project will advance our knowledge of methods that can be used to compare the safety and effectiveness of generic drugs. It will also provide a toolkit for the use of instrumental variable approaches for future research.

Project Description: 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 instrumental variable methods for future research. Collaborators from the University of Washington are involved in this project.

Linking data sources to elucidate non-fatal and fatal opioid-related overdose epidemiology and the role of FDA-regulated products

FDA Priority Area/Regulatory Science Challenge: 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 studying opioid use is that, while data may show 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, those challenges will be overcome by 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. This foundational work will ultimately allow for identification of specific FDA-regulated products (e.g. long and short acting opioid analgesics, abuse-deterrent formulations, benzodiazepines) and individuals who use them at highest risk for overdose and the development of multi-system prevention efforts.

Project Description: The goal of this project is to characterize the role of FDA-regulated controlled substances in overdose epidemiology. This will first be addressed by gaining access to data from several independently maintained state databases. Next the project will link data from these databases to characterize morbidity and mortality risks associated with opioid products. Collaborators from the University of Connecticut are involved in this project.

Characterization and analysis of high incidence of potentially unsafe prescribing of some Extended-Release (ER) opioid analgesics using Natural Language Processing (NLP) of Electronic Health Record (EHR) clinical notes

FDA Priority Area/Regulatory Science Challenge: 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. Two recent studies conducted by the FDA, one using the Medicare database and the other 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. Because prior analyses were performed using administrative claims data only, there is interest in determining whether information from EHRs could provide additional evidence of opioid tolerance in patients whose claims data lacks that evidence.

This project uses Real World Evidence to determine whether additional data from EHRs would enable enhanced analysis of the causes, potential clinical drivers, and verification and adjudication of the reasons for apparent widespread unsafe practice of prescribing ER opioid analgesics that require prior opioid tolerance to patients who are opioid-non-tolerant.

Project Description: 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 NLP techniques. Collaborators from OptumLabs are involved in this project.

Understanding the contribution of laboratory data linked to administrative claims: a case study looking at renal function and oral anticoagulant performance in patients with atrial fibrillation

FDA Priority Area/Regulatory Science Challenge: 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. Lifelong oral anticoagulation is recommended in most patients with atrial fibrillation (AF) to prevent stroke. However, treatment decisions can be complicated by the presence of chronic kidney disease, 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. By using Real World Evidence to evaluate the performance of oral anticoagulants after FDA approval in patients with AF, the project will improve understanding of oral anticoagulant safety and effectiveness in relation to renal function as well as help providers and patients make informed decisions and achieve better outcomes.

Project Description: We will conduct a case study looking at renal function and the performance of oral anticoagulant drugs in patients with AF. This project proposes to answer two 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, 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.

Non-Federal Entity Collaborators: Brahmajee Nallamothu, MD, MPH, and Rajiv Saran, MD, MS, MBBS- Co-Investigators (University of Michigan)

Characterizing safety and efficacy of brand-name and generic drugs used to treat hypothyroidism among patients who switch therapy formulation

FDA Priority Area/Regulatory Science Challenge: 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 may have differing effects. One example of this is the clinical preference to prescribe brand-name L-thyroxine as opposed to the generic formulation of the drug. This disconnect between FDA and expert recommendations is likely to cause confusion among patients and may be resolved through comparative effectiveness and safety research among currently available L-thyroxine products.

This project uses Real World Evidence via structured electronic health record and administrative claims data to better understand the safety and effectiveness among patients who switch from brand to generic formulations to treat hypothyroidism. Additionally, this project will use novel measures to examine relevant outcomes among all patients.

Project Description: Using a large administrative claims data source that includes information on privately insured and Medicare Advantage enrollees of all ages, we will examine the effectiveness and safety of generic and brand-name L-thyroxine among adult patients. We will first examine the effectiveness and safety among patients who switch from brand to generic L-thyroxine within 1 year of treatment initiation and then among all patients who switched from brand to generic formulations regardless of treatment duration. Lastly, we will use a novel measure to estimate the overall time spent on generic and brand L-thyroxine for those patients on treatment for at least one year to examine relevant clinical outcomes.

Real-world data to assess variation in opioid prescribing and use for acute pain in diverse populations

FDA Priority Area/Regulatory Science Challenge: Many studies have described the differences reported by patients between the amount of opioid analgesic prescribed and the amount that they actually used to manage acute pain. However, these studies have generally assessed use after a limited number of surgical procedures, used small groups of patients at single institutions, and have not considered diverse populations that may have different demographics and social or cultural norms regarding opioid analgesic use. To better inform prescribing guidelines and public health measures, data are needed from diverse populations of patients on their use of opioid analgesics to manage acute pain, trajectories of pain experienced and response to opioids, and how patients dispose of these medications when no longer needed. Additionally, these data can help identify patient factors that predict variation in opioid analgesic use to incorporate into prescribing guidelines.

The congressional SUPPORT Act (Section 3002) has tasked FDA with developing evidence-based opioid analgesic prescribing guidelines for treating specific acute pain diagnoses where such guidelines do not exist. As part of this task, FDA will support development of real-world data on patient-reported opioid analgesic use to manage acute pain to help develop evidence-based recommendations for opioid analgesic-prescribing for specific conditions or procedures commonly associated with acute pain.

Project Description: Using a novel patient-centered health data-sharing platform (Hugo), the Yale-Mayo Clinic CERSI will enroll 1,200 patients who have been prescribed short-acting opioid analgesics after receiving care for new onset pain in the emergency department, primary care, or dental care offices at one of four diverse hospital systems. Patients will be followed for 180 days to collect information on pain control and opioid use through survey questionnaires sent via the Hugo platform. Additionally, patients will connect their electronic medical records and pharmacy data, when available, to the Hugo platform, using their patient portal accounts. Patients will also use wearable devices to gather additional insights into their activity and sleep patterns during the study period. At the end of follow-up, the CERSI will assess what patients did with their unused opioids. Non-federal collaborators from Rapid City Regional Hospital and University of Alabama Birmingham are involved in this project.

Quantifying the relationship between inappropriate prescribing of opioid-tolerant-only medications to patients without prior opioid tolerance and opioid-related harms

FDA Priority Area/Regulatory Science Challenge: 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 opioid analgesics. A recently completed project using the OptumLabs claims data looked at prescribing of opioids that are only intended for use in people who are opioid tolerant. The project found that more than half of patients starting these drugs had no evidence of opioid tolerance. Such use is inconsistent with the labelling of these drugs and may create safety risks; however, the magnitude of this risk and prevalence of harms are not well described.

Project Description: This project will use claims data to measure the risk of opioid-related harms associated with the use of opioid-tolerant-only formulations by opioid naïve patients, and identify risk factors associated with these harms. Ultimately the findings from this study will help to meet the goals of the ongoing REMS for Opioid Analgesics efforts to reduce the risk of abuse, misuse, addiction, overdose, and deaths due to prescription opioid analgesics by understanding the outcomes of such medications.

Advancing Clinical and Post-Market Surveillance of Medical Devices and Diagnostic Tests

Post-market surveillance with a novel mHealth platform

FDA Priority Area/Regulatory Science Challenge: 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, none of these mechanisms can capture longitudinal patient-reported outcomes nor can they integrate data from multiple sources.

This project aims to explore whether a new mobile health technology could aid in the FDA's post-market surveillance of medical devices by obtaining health data from medical records allowing for better insights into patients' outcomes, as well as information on patient-reported symptoms and experiences. The proposed project will inform how the FDA may use novel and emerging technologies as it increasingly adopts a life-cycle evaluation approach to medical device regulation.

Project Description: A novel health data sharing platform (Hugo) 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 health record (EHR) 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' EHR 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, EHR 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 health data sharing platform), Johnson & Johnson (collaborator- provides input on project and funds to Me2Health for development of Hugo health data sharing platform) 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) and Sanket Dhruva, MD, MHS- Co-Investigator (University of California, San Francisco)

Real world short- and intermediate-term safety outcomes following atrial fibrillation ablation

FDA Priority Area/Regulatory Science Challenge: 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 (AEF) - 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 AEF is often missed and/or underreported, and that the true incidence of AEF in the "real world" is higher than the limited study data suggest. This research proposal aims to use Real World Evidence to understand the incidence of short- and intermediate-term complications of catheter AF ablation, with an emphasis on AEF.

Project Description: Using claims data from the OptumLabs database, we will first characterize the 30- and 90-day rates of acute care use (including ED visits and hospitalizations) for patients undergoing AF ablation from 2011-2017. Next, using an algorithm, we will characterize the 30- and 90-complications that are likely to be related to AEF.


Non-Federal Entity Collaborators: James Hummel, MD- Co-Investigator (University of Wisconsin School of Medicine) and Sanket Dhruva, MD, MHS- Co-Investigator (University of California, San Francisco)

Enhancing pediatric medical device innovation: creating a supportive marketplace

FDA Priority Area/Regulatory Science Challenge: Since its inception in 1976, the Center for Devices and Radiological Health (CDRH) at the FDA has been dedicated to the protection and promotion of public health. While the number of devices approved for adults continues to rise, little change has occurred in the number of devices developed for children. Over the years, stakeholders have raised concerns about a lack in the availability of devices designed and tested for children. To encourage the development of devices for underserved populations, Congress passed the Safe Medical Devices Act of 1990, and in 2007 Congress passed the Pediatric Medical Device Safety and Innovation Act (PMDSIA). Despite these actions, minimal improvement has been seen over the last 30 years in the number of devices developed for children. Pediatric device development is hindered by a number of factors including challenges in consent and enrollment in clinical trials, variations in anatomy and pathophysiology, and small, heterogeneous and geographically disparate populations.

Creating a marketplace that supports development of technologies which serve the complexities of children may accelerate medical technology innovation for all Americans. This project aims to both identify the current challenges and barriers for the development and approval of pediatric medical devices by interviewing key stakeholders as well as to prioritize the barriers to market that will inform regulatory decision-making and promote innovation.

Project Description: The goal of this project is to survey members of industry to better inform the growth of a marketplace that supports the development of devices for pediatric populations that are approved or cleared and labeled for use in pediatrics. The project will use both qualitative and quantitative methods to gain insight from key stakeholders from the device industry, research community, investors, and payers.


Non-Federal Entity Collaborators : Andrew Lo, PhD- Co-Investigator (Massachusetts Institute of Technology)

Evaluating mobile health tool use for capturing patient-centered outcome measures in heart failure patients

FDA Priority Area/Regulatory Science Challenge: Heart failure (HF) is a highly prevalent disease that also carries high morbidity and mortality. Improvements in mortality and healthcare utilization, including hospitalizations, remains the gold standard outcome for HF drug approvals. However, it is difficult to improve mortality as the only endpoint due to the variation in the age groups and comorbidities of the population and ineffectiveness to alter all-cause mortality. Considering these issues, there is a need for exploration of complimentary endpoints. The FDA recognizes the importance of developing patient-centric endpoints that are relevant to patients beyond mortality and hospitalizations. Patients with HF have substantially reduced functional capacity and quality of life (QoL) and it is imperative to explore interventions that impact endpoints that directly measure how a patient feels or functions on a daily basis.

Recently, new mobile health technologies have emerged as clinical tools and offer an opportunity to overcome the challenges in measuring functional capacity and recording symptoms. These technologies capture and integrate data from disparate sources reflecting patients' functional status and symptomatology and have the potential of serving as surrogate endpoints for new HF therapy approvals.

Project Description: The goal of this project is to test the feasibility and reliability of capturing quantifiable measures of functional capacity and QoL using a wearable sensor in HF patients for a period of 60 days. Acute Decompensated Heart Failure (ADHF) patients will be recruited post-discharge from National Heart Centre and National University Hospital in Singapore. Patients will be monitored at home using the Biofourmis' BiovitalsHF™ platform which will capture biosensor data from two wearable devices: Everion® and Apple Watch Series 4. Patients will also use the BiovitalsHF™ smartphone application to capture electronic patient reported outcomes (ePROs) such as medication adherence, symptoms, the Kansas City Cardiomyopathy Questionnaire (KCCQ) responses, and perform the guided mobile-based 2-minute-step-test.


Non-Federal Entity Collaborators: Kuldeep Singh Rajput, Trace Brookins, Rachel Chan (Biofourmis)

Development and Application of Novel Analytics

Utilization and adverse events associated with mechanical circulatory support devices among patients with acute myocardial infarction and cardiogenic shock undergoing PCI

FDA Priority Area/Regulatory Science Challenge: 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 all patients who have an abnormally low blood pressure or oxygen requirement are treated 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.

This project uses Real World Evidence from the National Cardiovascular Data Registry (NCDR) to identify subgroups of cardiogenic shock patients undergoing percutaneous coronary intervention and characterize the usage of different mechanical circulatory support devices. The proposed project will enhance regulatory science by improving understanding of cardiogenic shock, characterizing contemporary utilization patterns of mechanical circulatory support devices, and identifying differences in the utilization and adverse events associated with devices when used in patients with cardiogenic shock, which may inform benefit-risk decisions. Ultimately, this work is expected to inform efforts to improve health outcomes for patients with cardiogenic shock.

Project Description: 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 Chest Pain-MI registries, two national registries of patients with acute myocardial infarction (Chest Pain-MI) and patients undergoing stent procedures (CathPCI) to determine the utilization patterns of devices in cardiogenic shock. We will then use advanced analytic methods to identify distinct subgroups of patients and test for differences between subgroups. Collaborators from Texas A&M University are involved in this project.


Non-Federal Entity Collaborators: Jeptha Curtis, MD, Frederick Masoudi, MD, MSPH, and John Messenger, MD- Co-Investigators (American College of Cardiology), Sanket Dhruva, MD, MHS- Co-Investigator (University of California, San Francisco), and Saket Girotra, MBBS, MS- Co-Investigator (University of Iowa)

Understanding the use of existing real-world data for medical product evaluation

FDA Priority Area/Regulatory Science Challenge: Clinical trials are considered the gold standard for understanding the safety and efficacy for any clinical or health system intervention, and results from clinical trials nearly always form the basis of FDA regulatory evaluations. Likewise, clinical trial data has traditionally been prioritized when making clinical practice guidelines and treatment decisions. However, in recent years the potential to use observational research for medical product evaluation has improved due to availability of clinical data, as well as the increasing detail provided in clinical data. In addition, advances in computing and the development of statistical methods are increasingly making it possible to use large-scale, real-world data to inform our understanding of the safety and effectiveness of medical products.

Project Description: The goal of this proposed research is to better understand the potential advantages and limitations of applying observational research methods to the use of existing real-world data for medical product evaluation. Focusing on drugs that have been approved for use by the FDA, this proposed research will use OptumLabs claims data to predict the trial populations and results of ongoing clinical trials used for regulatory evaluations, particularly those focused on drug safety. This proposal addresses a key methodological gap: existing work applying observational research methods to real-world data has focused on replicating the results of completed clinical trials whose results are already known.


Non-Federal Entity Collaborators: William Crown, PhD, Co-investigator (OptumLabs), Sanket Dhruva, MD, Co-investigator (University of California, San Francisco)

Fostering Patient-Centered Decision Making

Qualitative analysis of gender differences in heart failure PROs

FDA Priority Area/Regulatory Science Challenge: Cardiovascular disease is the number one cause of death for women in the world. In recent years, approximately 1 in every 4 deaths in the United States results from heart disease, with similar rates among men and women. Heart failure (HF) patient-reported outcomes (PROs), like the Kansas City Cardiomyopathy Questionnaire (KCCQ), have been shown to correlate with the New York Heart Association classification system and to predict hospitalization and death. Instruments like the KCCQ are regularly used as endpoints in clinical studies and patient care for their utility. The differences in presentation and symptoms of the disease in female and male patients are well documented. However, because the instrument was developed and validated on a majority male population (69% male in the original study), there are large differences in scores between male and female patients with the same level of disease severity. Regulatory decisions being made upon these results may not adequately represent the symptoms and experiences of all patients.

An improved understanding of the KCCQ to address both male and female patients will advance clinical study design and analyses of HF trials. Furthermore, this project will also address improved health communication by initiating the work to make KCCQ clear and applicable to female patients, improving both patient care and regulatory decisions.

Project Description: This project will focus on the initial qualitative analysis methods to understand concept and item interpretation by patients. The qualitative work in this study will further our understanding in KCCQ response differences (in particular, potential for response bias) between genders. This project aims to further validate the use of the KCCQ among female HF patients and provide an example of how to account for gender differences in patient-reported outcomes.

Related Project

Universal Common Data Model Mapping – Implementation and Validation

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.