2019 Annual Report: Welcome from the Interim Executive Director

I am delighted to share this year’s DCRI annual report, an especially meaningful edition for me as I complete more than a year as interim executive director. The stories and studies here reflect fully how the DCRI is advancing clinical research through groundbreaking studies led by exceptional teams of faculty and operational staff. Under the leadership of our research teams, we continue to introduce new methods and pursue innovative approaches in clinical research for the most pressing issues in patient care.

The past year has brought challenges and change, and also a deeply renewed commitment to our mission throughout the DCRI and across the Duke system. We completed a comprehensive strategic planning effort and launched transformation initiatives aimed at revitalizing our core offerings and advancing new ways of conducting clinical research. The DCRI is an undisputed leader in the areas of using pragmatic approaches and real-world data to improve clinical trial design—the future of clinical research. Our overarching goal is to ensure the DCRI remains distinct in its academic and operational expertise and its approaches—for the benefit of our sponsors, partners, collaborators, and the patients we serve. As expected, we opened a search for a new executive director. In the coming year, I am excited to be able to hand the reins of an invigorated and future-focused organization to a new leader.

I am honored to have led the DCRI. The progress we have made is due to so many—a sincere commitment to improvement from the DCRI operational teams and support from the Duke School of Medicine and my colleagues, both at the DCRI and the Department of Population Health Sciences.

I look forward to an exciting year ahead and many years of growth and contribution as the DCRI continues to achieve its mission to improve the care of patients around the world through innovative clinical research.

Lesley H. Curtis, PhD
Chair and Professor
Department of Population Health Sciences
Interim Executive Director, Duke Clinical Research Institute
Duke University School of Medicine

 


This article originally appeared in the DCRI’s 2018-2019 Annual Report. View more articles from this publication.

2019 Annual Report: Reflection from the Head of Research Operations

As a member of the DCRI team for more than 12 years, including as head of research operations now, I am especially proud to represent our operational teams in this year’s annual report. Inside, you will find a sampling of the hundreds of projects led by our dedicated clinical research project teams. Nowhere else will you find an academic research organization with the depth of operational expertise to translate thought leadership from the pages of a protocol through study start-up, recruitment, retention, and returning results to our valued study participants.

Whether it’s a traditional interventional trial such as ARISTOTLE, a DCRI-led study that was recognized by the New England Journal of Medicine as one of a dozen studies that have changed clinical practice, or DCRI-led ADAPTABLE, one of the first truly pragmatic clinical trials with direct-to-patient engagement, our operational teams approach their work each day with the following shared goals in mind:

  • Developing new ways to engage patients;
  • Delivering quality data;
  • Fostering new ideas and innovative approaches;
  • Improving efficiency;
  • Sharing learning; and
  • Partnering with DCRI faculty and collaborators.

We achieve these goals in collaboration with our many long-standing partnerships that extend our expertise around the world. In this annual report, I chat with one of our partners, Tracy Temple, Canadian VIGOUR Centre’s associate director of clinical trials, about how collaborations unite the best minds in thought leadership and operational expertise to advance clinical research around the world.

Whether working together with our partners or teaming up here at the DCRI, we all are motivated by our mission to improve care for patients. I know this is true for me, and I witness it in the work of so many colleagues. I am honored to lead the DCRI’s operational teams, and I’m looking forward to what comes next.

Ty Rorick
Head, Research Operations

 


This letter originally appeared in the DCRI’s 2018-2019 Annual Report. View more articles from this publication.

2019 Annual Report: Traditional Trials

AR19-Traditional-Trials-Header

Michael Felker, MD, MHS, a cardiologist at the DCRI, invited his colleague, fellow DCRI cardiologist Adrian Hernandez, MD, MHS, to discuss the evolving clinical research landscape and how the DCRI is responding by continuing to deliver traditional clinical trials while also incorporating pragmatic approaches.

MICHAEL: It seems like we’re at an inflection point in clinical research, from an era where we were focused on randomized clinical trials as the foundational way of generating evidence to now, where there are all these additional aspects of evidence generation, such as real-world data, pragmatic trials, and registry-based trials. I think it’s created interesting opportunities, as well as interesting challenges. Adrian, how do you view the current landscape?

ADRIAN: There are two things at play. The complexity and cost of trials have increased. On the other hand, we need more answers to important questions. Alongside that, we’ve seen a transformation in health care systems in terms of greater access to data and greater engagement of patients, which gives us the ability to take advantage of that for research purposes and hopefully make research simpler.

MICHAEL: Agreed. This transformation, particularly in terms of data access, is interesting. As you know well, the way we collect data clinically is often not structured for the data to be used for research. But it seems like a real opportunity, especially in a big health system like Duke, to have a learning health system where we’re actually testing questions and learning from the way we conduct clinical practice.

ADRIAN: That’s exactly right. As health care systems get smarter about care delivery, how do we embed research within the health care systems to power clinical trials? For certain outcomes, what we need as researchers is already available in the clinical care setting.

But there may be other things that we need to do more in a traditional mode or directly with patients to collect certain information that isn’t part of routine care. That’s where things have to be hybridized, combining traditional aspects with more pragmatic approaches that leverage what’s being done as part of clinical care.

MICHAEL: A lot of these more pragmatic approaches have the potential to address some of the challenges we’ve seen in traditional trials—expense, as you mentioned, slow enrollment, and very long timelines. Do you think the traditional Phase 3 randomized clinical trial is on its way out, or do you think there’s always going to be a role for that kind of evidence generation?

ADRIAN: I think one of the things we’ve learned is that one size does not fit all. There is a spectrum of discovery as we learn about a condition or a novel therapy, and we want to know everything about it before it reaches the public. For clinical developments, we’re still going to need lots of information about what’s happening in the progression through Phase 1 to Phase 3 trials. But as we have more information, we can narrow down and take advantage of more pragmatic approaches, with a special focus on methods that will be more convenient for patients.

MICHAEL: I think we’ve realized it’s not a strict dichotomy between traditional and pragmatic. Even for trials that are traditionally structured, there are a lot of opportunities to leverage other data sources to identify patients, conduct follow-up, or otherwise streamline trials so we can get answers more quickly for our patients.

This is a place where the DCRI can really add value because we have expertise across not only traditional clinical trials, but also a lot of these new technologies like wearables, real-world data, and learning health systems. The ability to bring all of that together in one place will be critical as clinical research moves forward.


This article originally appeared in the DCRI's 2018-2019 Annual Report. View more articles from this publication.

DCRI cardiologists Michael Felker, MD, and Adrian Hernandez, MD, discuss different types of evidence generation, from traditional randomized trials to pragmatic clinical trials that embed research in routine patient care.

2019 Annual Report: Sharing Knowledge, Changing Practice

The DCRI’s mission is to translate knowledge gained from research into clinical practice in order to improve patient outcomes. Our long tradition of delivering on this mission was recognized this year when one of our studies was honored by the New England Journal of Medicine.

Prior to his retirement, Jeffrey Drazen, the journal’s former editor-in-chief, reflected on the studies the journal had published since his tenure began in 2000—more than 80,000 submissions and nearly 4,000 published studies. He selected what he called Drazen’s Dozen: 12 studies that were “practice-changing and lifesaving,” and the ARISTOTLE trial was included in the list. ARISTOTLE, published in 2011, was led by a team at the DCRI and the Uppsala Clinical Research Center in Sweden.

The ARISTOTLE study, spanning 39 countries and including more than 18,000 patients with atrial fibrillation, was a clinical trial that randomized patients to either apixaban or warfarin. Results showed that apixaban was superior to warfarin; not only was apixaban more effective at preventing stroke, but it also caused less bleeding and resulted in fewer deaths.

ARISTOTLE was honored as 1 of 12 studies that were "practice-changing and lifesaving".Although use of warfarin was prevalent prior to these findings, apixaban is now the most commonly initiated oral anticoagulant drug for patients with atrial fibrillation, said Christopher Granger, MD,(pictured left) the DCRI lead investigator on the study. Apixaban is easier to use because warfarin is associated with several food and drug interactions and requires monitoring.

“It is gratifying to be able to generate evidence that prevents strokes and saves lives,” Granger said. “The honor of being selected as one of the 12 most lifesaving studies from such an important journal aligns nicely with the DCRI’s mission to develop and share knowledge that improves patient care around the world.”

 


This article originally appeared in the DCRI’s 2018-2019 Annual Report. View more articles from this publication.

2019 Annual Report: Reducing Risk, Changing Paradigms

An ongoing DCRI clinical trial seeks to determine whether an experimental lipid treatment is effective in preventing a second cardiovascular event in patients who have already had a heart attack.

Patients who have had a heart attack are at highest risk for stroke or a second heart attack in the months following their initial heart attack, said John Alexander, MD, (pictured left) DCRI’s investigator on the AEGIS-II trial. The study centers on whether an experimental lipid therapy called CSL112 could help reduce this risk.

CSL112, developed by CSL Behring, consists of apoA-1, a naturally occurring human protein that is part of high-density lipoprotein, also known as “good cholesterol” that removes cholesterol from plaques. In a 2016 DCRI coordinated study that proved the drug’s safety, CSL112 was found to reduce cholesterol buildup. AEGIS-II will enroll over 17,000 patients at about 1,000 sites worldwide and will examine the drug’s efficacy by determining whether this reduction in buildup also results in fewer recurrent cardiovascular events.

AEGIS-II will enroll over 17,000 patients at about 1,000 sites worldwide.Participants in the study will receive four weekly infusions of CSL112 and will be followed for one year, the timeframe during which investigators expect to see maximum benefit. Patients will then be followed for a year to determine whether these effects are sustained.

The study is the next step in a partnership with CSL Behring and other academic collaborators that has been ongoing for almost a decade. Since initially discussing CSL Behring’s development program for the drug in 2010, the DCRI has partnered with them on three trials, all of which led to new discoveries that can improve clinical care.

 


This article originally appeared in the DCRI’s 2018-2019 Annual Report. View more articles from this publication.

2019 Annual Report: Pragmatic Clinical Trials & Real-World Evidence

The DCRI’s Robert Mentz, MD, is the co-principal investigator for a pragmatic clinical trial on heart failure called TRANSFORM-HF, which is funded by the National Heart, Lung, and Blood Institute (NHLBI). Mentz discussed features of the trial with Patrice Desvigne-Nickens, MD, a medical officer in the Heart Failure and Arrhythmias Branch of the NHLBI and project officer for TRANSFORM-HF.

ROB: Patrice, we met while working with the Heart Failure Research Network funded by the NHLBI, which was formed to run many small trials to answer key questions in heart failure. Many of the lessons learned from the network helped us fine-tune our approach to TRANSFORM-HF. TRANSFORM is a real-world comparative effectiveness study that has many elements of pragmatism, including a broader population, streamlined trial conduct, and fewer requirements for patients in the follow-up period.

PATRICE: Yes, TRANSFORM looks at two active drugs to determine if one is more effective than the other in improving outcomes in a general population of patients with heart failure. Because it’s answering an important question using available drugs and tools, a pragmatic design is possible and preferred. Determining the kind of trial needed often depends on context, whether it’s a proof-of-concept trial or whether it’s a definitive trial seeking clinical outcomes that could change practice. Pragmatic trials are not new, although new emphasis may be placed on pursuing more pragmatic methods. In fact, the NHLBI has supported trials in the past that have involved a large number of patients with heart failure, clinically important outcomes, and minimal case report forms—all features of a pragmatic trial.

ROB: Agreed. As both the DCRI and the NHLBI look toward the future of clinical research, TRANSFORM is a good step forward in an environment in which it’s become harder to recruit patients with heart failure. We’re learning how to do trials more efficiently and make the experience better for all stakeholders, although of course there is more work to be done to innovate direct-to-patient trials. Can you talk a bit about differences you’ve seen in TRANSFORM versus other heart failure trials you’ve worked on in your career?

Industry average recruitment rate for HF studies: .02 patients per site per month; TRANSFORM-HF recruitment rate: >2 patients per site per month.PATRICE: One of the fundamental differences in running a pragmatic trial is that it requires broader eligibility criteria, which hopefully allows all patients with the condition to be included. Traditionally we must interview many patients before finding one who is eligible. A pragmatic trial like TRANSFORM is a tremendously different paradigm where we’re eager to enroll everyone affected by the disease we’re studying.

ROB: We’ve seen this difference impact TRANSFORM’s enrollment rates. Overall in the U.S., heart failure studies recruit about 0.2 patients per site per month. Our goal was to get that to three to five patients. Our average right now is just over two patients per site per month, which is higher than average but still not yet where we need it to be. Broadening the eligibility criteria has also resulted in the important inclusion of women and minorities. Patrice, can you comment on what you see as the future for pragmatic trials and our work together?

PATRICE: As we improve patient outcomes and they live longer, they have other health issues that need more study, which leads to additional research questions. To address this paradox with limited resources, we’ll need to conduct trials with large numbers of patients, which is where pragmatic design is a good option. At the DCRI, which is a research tour de force in cardiology clinical trials, and at the NHLBI, we’re working toward bringing clinical research into the 21st century by embracing information technology to facilitate collection and analyses of traditional patient-reported outcomes and, when appropriate, administrative databases, which will enhance efficiency and lower costs. Because we share these goals, it makes sense to align forces to work on projects like TRANSFORM-HF.


This article originally appeared in the DCRI’s 2018-2019 Annual Report. View more articles from this publication.

2019 Annual Report: Partnering to Build a Research Network

The DCRI is partnering with Cerner, the world’s largest electronic health record (EHR) company, to conduct clinical research using Cerner’s cloud-based platform.

The platform, called HealtheIntent, was originally developed for use in population health management. When DCRI researchers Ann Marie Navar, MD, PhD, (pictured left) and Eric Peterson, MD, MPH, (pictured right) learned about the platform, they saw an opportunity for clinical research. Although maintained by Cerner, the platform can incorporate data from a variety of EHR vendors and has the ability to link to other data sources such as health care claims and mortality indices. Together with Cerner, the DCRI is now among the first to pilot this platform for clinical research.

The DCRI has a strong history in leveraging real-world data from the EHR to power future research. To further the collaboration with Cerner, Navar and Peterson identified academic collaborators at both the University of Texas Dell Medical School and the University of Missouri to pilot the use of HealtheIntent to run an EHR-powered clinical registry. Funded by Janssen, the pilot project will explore treatment patterns for patients with cardiovascular disease while helping to identify the benefits and limitations of Cerner’s platform for clinical research. Evaluating and maximizing data quality is also of paramount importance; as part of the project, large numbers of chart reviews are being performed to verify the accuracy of the EHR-generated data and refine how key clinical conditions are defined.

The DCRI and Cerner hope to expand this partnership by recruiting other health systems to join their network of sites dedicated to using the EHR to power research in what they are calling the Learning Health Network. Through expansion of the network, Navar and Peterson hope to create a network that can be leveraged for other research purposes, from larger observational registries to pragmatic clinical trials, as well as studies on how to best drive the adoption of evidence into clinical practice.


This article originally appeared in the DCRI’s 2018-2019 Annual Report. View more articles from this publication.

2019 Annual Report: A Pragmatic Approach to Benefit an Understudied Population

A new DCRI-led pragmatic clinical trial conducted in partnership with the Wake Forest School of Medicine will assess the effectiveness of statins in patients aged 75 or older.

Statins are known to lower cholesterol and reduce the risk of cardiovascular events for secondary prevention, or patients with known coronary artery disease (CAD), as well as primary prevention, or those without CAD who are at high risk of future cardiovascular events. However, few statin studies in primary prevention populations have included older adults.

PREVENTABLE is the largest pragmatic clinical trial with placebo-controlled drug assignment to date. It is also the first statin trial with a non-cardiovascular primary outcome. Instead, investigators will study whether statins could prolong survival free of new dementia or physical disability—a critical consideration for older adults looking to maintain independence. The study’s secondary outcome will include cardiovascular events as well as mild cognitive impairment.

PREVENTABLE will partner with PCORnet®, the National Patient-Centered Clinical Research Network, and the National VA Network to identify and recruit 20,000 participants aged 75 years or older and without CAD at 100 U.S. sites. Utilization of these two national resources is expected to enable investigators to enroll participants and collect health data faster and more efficiently than a traditional trial. Investigators will also use electronic health records (EHRs) to help ascertain patient outcomes.

The study will be funded over seven years from the National Institute on Aging in partnership with the National Heart, Lung, and Blood Institute and will be led by the DCRI’s Karen Alexander, MD (pictured above).

Karen Alexander, MD, principal investigator for PREVENTABLE, talks through pragmatic elements of the trial that will help investigators answer an important question more efficiently.

PREVENTABLE will use pragmatic elements throughout, including:

  • Embedding research in the health care system by enrolling patients in their usual care settings and in partnership with their primary care clinicians
  • Engaging potential participants during screening and recruitment by using informational videos, panel discussions with research participants, and an e-consent platform;
  • Pairing EHR data with other forms of follow-up, including calls and in-person visits for cognitive and functional assessments, to ensure complete collection of outcomes; and
  • Simplifying study drug delivery by shipping directly to patients.

This article originally appeared in the DCRI's 2018-2019 Annual Report. View more articles from this publication.

2019 Annual Report: Data in Clinical Research

Laine Thomas, PhD, associate director of DCRI Biostatistics, sat down with David Page, PhD, chair of Duke Biostatistics & Bioinformatics, who is new to his role as of this year, for a discussion on the future of biomedical data science, as well as how machine learning techniques will be incorporated in this work.

LAINE: David, I’d like to talk about your vision for Duke Biostatistics & Bioinformatics in terms of where we’re going in some exciting new areas, like artificial intelligence and machine learning. Let’s start with a brief explanation of
artificial intelligence and machine learning.

DAVID: Artificial intelligence is automating human thought and actions. Machine learning is the adaptation piece of that. I view machine learning as the creation of algorithms that will analyze data and give back insights. Those insights could be predictors of the future, better explanations of past events, or indicators of cause-and-effect relationships. Although I’m trained as a computer scientist, I see machine learning and data science emerging just as much from statistics as from computer science and engineering.

LAINE: Can you talk us through your vision for collaboration among different groups on campus doing work in biomedical data science?

DAVID: A tremendous opportunity is to use cross-campus partnerships to recruit new faculty and staff. I think the DCRI and Duke Biostatistics & Bioinformatics can be critical players here because we offer outstanding data access, as well as translational opportunities for people who want to make a real difference in society.

LAINE: Speaking of intersections on campus, I want to segue a bit into other intersections—those between machine learning and causal inference, just because that’s the area my work is in. Have you worked on any problems that sit in this intersection?

DAVID: For the last 10 years, I’ve been working on using machine learning for discovery of adverse drug events. That project is primarily focused on using observational data such as EHR data or claims data. We’ve tried to build on previous statistical and artificial intelligence methods, and our best results have come from algorithms that combine insights from both traditions.

LAINE: I’m working on a project where we are developing new methods to analyze the comparison of hysterectomy and myomectomy in the COMPARE-UF registry of women with uterine fibroids. This DCRI registry emphasizes personalized medicine, where we’re trying to estimate individual treatment effects while accounting for differences between groups. Our approach also combines both traditions. We have a machine learning element, but it’s currently a tool that’s separate from the causal inference. We’re fitting models using machine learning methods, then the causal inference phase occurs. I can imagine some ways to make them more integrated. In my mind, the next step is to make them actually interact with each other instead of treating them as pre- and post-processing.

DAVID: That’s really exciting. I’d love to hear from you about what other areas you see for future intersection between the two fields as we continue to seek collaboration opportunities.

LAINE: In the DCRI Biostatistics group, I see a number of potential areas where machine learning could improve how we approach problems in causal inference. One area is precision medicine. As our purpose in causal inference changes—instead of trying to estimate averages, we’re now trying to estimate individual treatment effects—we need to figure out how to do that better, and machine learning could help. Another opportunity is hospital profiling. Because our work in this space is in demand from the patient perspective, I expect it will continue to expand and could integrate machine learning to improve our rating models.

DAVID: Thanks for those insights, Laine. Both of those sound like exciting opportunities to find the right hybrid approach that could improve how we do things across the board, and I look forward to working with you in these areas.


This article originally appeared in the DCRI’s 2018-2019 Annual Report. View more articles from this publication.

2019 Annual Report: Ensuring Data Quality

Data are easily accessible, but not all data are well-suited to answer a given clinical research question. A team that includes DCRI faculty is working to build frameworks to assess whether data can be used in research studies.

It is important to assess data, the DCRI’s Keith Marsolo, PhD, (pictured left) explained, because data come from many different sources. For example, within PCORnet®, a national patient-centered distributed research network for which the DCRI serves as a coordinating center, real-world data come from health systems and health plans across the country. Variation in source systems and data collection practices can lead to data heterogeneity that is problematic for research.

Alongside its work in defining PCORnet’s common data model, a team led by Marsolo has developed routines to assess data quality—what Marsolo called “a foundational set of data checks.” This material has been made publicly accessible on the PCORnet website, which is novel, Marsolo said, because many other groups working with real-world evidence (RWE) do not publicize their data quality processes.

35 data checks which translate into 1,200+ data check measuresThe data checks help determine whether available data is fit for purpose, or able to be used to answer the research question at hand. Although each study team will need to address study-specific questions, the checks developed by Marsolo’s team answer baseline questions of dataset fitness and shorten study start-up time.

Marsolo also leverages his expertise outside of PCORnet by collaborating with other organizations at Duke. He serves as a member of the data quality working group convened by the Duke-Margolis Center for Health Policy’s RWE Collaborative. By participating in this group, which includes researchers, industry representatives, patients, and other stakeholders, he helps translate complex data concepts so they can be viewed through a health policy lens. The group hopes to inform the Food and Drug Administration’s (FDA) work in the RWE space.


This article originally appeared in the DCRI’s 2018-2019 Annual Report. View more articles from this publication.