Speaker

Roxana Mehran, MD, FACC, FAHA, MSCAI, FESC
Mount Sinai Endowed Professor of Cardiovascular Clinical Research and Outcomes
Professor of Medicine (Cardiology), and Population Health Science and Policy
Director, Interventional Cardiovascular Research and Clinical Trials
Director, Women’s Heart and Vascular Center at Mount Sinai Heart
Icahn School of Medicine at Mount Sinai 

Slides

Keywords

Cardiovascular Health, Interventional Cardiovascular Research, Diversity, Health Disparities

Key Points

• There are important gender disparities in cardiovascular health. Globally cardiovascular disease (CVD) prevalence had decreased between 1990 and 2010, but it has slightly increased since 2010.

• CVD is the leading cause of mortality in women. The median survival time after a first myocardial infarction (MI) for adults 45 years and older is 8.2 years for males and 5.5 years for females at age 45 years or older. Of those who have had a first MI, the percentage with a recurrent MI or fatal CHD within 5 years is 17% of males and 21% of females age 45 years or older. Hospital admissions with acute coronary syndrome in women younger than 55 years increased 21% in 1995-1999 and 31% in 2010-2014.

• There are also ethnic and racial disparities. By 2045, more than 50% of the population in the U.S. is expected to be other than non-Hispanic white. It is important to note when you look at the ARIC surveillance data, there is consistently higher risk in black females and males across all ages.

• Yet enrollment of ethnic minorities in NIH clinical trials and for trials studying approved devices and drugs remains low.

• Diversity in clinical trials is important for generalizability of results, to provide equal opportunities, practice precision medicine, tailor practical guidelines, improve public health outcomes, detect potential differences in safety and efficacy, and to address health disparities.

• There are several strategies that will help increase diversity in CVD trials. First, increasing diversity among trial participants must be a top priority in order to address health disparities and allow for optimal diagnosis and management of CVD in all.

• Increasing diversity in trial leadership is one of the most important strategies to increase diversity among RCT participants.

• Further efforts are urgently needed to increase diversity in the cardiology workforce, which will improve clinical trial diversity and cardiovascular health for all.

• Approaches from the whole scientific community to tackle the inequality in workforce, trial leadership and trial participants have to be developed.

 

Discussion Themes

-What have been the challenges in developing and identifying people interested in working in clinical trials? Many high school and college students are moving away from the sciences. We have to change how we are practicing. The biggest barrier for CV medicine is the lack of women leaders in CV. Trainees don’t see a way forward. Work-life balance is a very important issue and will be for the next generation of women and men.

-Have you looked at the differential recruitment approaches between provider led and EHR-led approaches? Can we use blinded HER-led recruitment efforts to identify participants? This is a really important point because we know that providers have bias, and it is the providers who do not approach women for enrollment.

–What about women who are excluded because they are pregnant or planning to be pregnant? It is an excellent question. With the current ability to become pregnant this can affect women up to the age of 50. These are really excluding many women on the basis of this exclusion. We need to think about removing some of this and evaluate.

Tags

#pctGR, @Collaboratory1

Speakers

Ruth Engelberg, PhD
Research Professor of Medicine
Division of Pulmonary, Critical Care and Sleep Medicine
University of Washington

Erin Kross, MD
Associate Professor of Medicine,
Division of Pulmonary, Critical Care and Sleep Medicine
University of Washington

Robert Lee, MD, MS
Assistant Professor of Medicine
Division of Pulmonary, Critical Care and Sleep Medicine
University of Washington

Slides

Keywords

Jumpstart, Advanced Planning, Goals of Care

Key Points

• Researchers know that goals of care discussions between patients and clinicians are associated with important patient and family outcomes. And yet goals of care discussions and their documentation remain a shortcoming in many health systems.

• The Jumpstart intervention is a communication-priming intervention. It has been studied in prior contexts in a PCORI trial and a pilot inpatient trial where the intervention increased goals of care discussions from 8% to 21%.

• For the Jumpstart trial, a number of refinements were made to make it more pragmatic, including the creation of Jumpstart using EHR data rather than patient or family member surveys, delivering the intervention to clinicians only, automated population of Jumpstart guide fields, and automated Jumpstart delivery to clinicians by email.

• The research question Jumpstart set out to answer is can a patient-specific, clinician-facing communication priming intervention with discussion prompts effectively promote goals of care discussions between clinicians and hospitalized older adults with serious illness?

• Jumpstart is a pragmatic randomized trial of Jumpstart compared to usual care. It utilized a waiver of consent; all eligible patients were randomized, and randomization was stratified by hospital and history of dementia at 3 hospitals in University of Washington system.

• Patients who were eligible had been hospitalized at least 12 hours but no more than 96 hours, age 55 and older with at least one Dartmouth Atlas chronic condition, or they were age 80 or older. The Jumpstart Guide was delivered to members of the primary hospital team on day of randomization via a secure email with a reminder message via pager.

• The primary outcome was the proportion of patients with EHR-documented goals of care discussion within 30 days of randomization. Goals of care discussions were defined as discussions about overarching goals for medical care but going beyond “just code status” (e.g. DNR/DNI). The goals of care discussions were identified by a natural language processing (NLP) called BERT and screened human abstraction.

• The study team trained and validated the NLP model by adjudicating 2,500 notes, and compared human decisions with NLP decisions to come up with an assessment with how NLP preformed. In order to classify a patient with goals of care conversation, the study used the NLP to screen the records and pull out excerpts of EHR that had high probability to contain a goals of care conversation and then conducted a human review. EHR passages were adjudicated in pseudo-random order, blinded to patient ID.

• Jumpstart also obtained secondary outcomes from the EHR, including ICU admissions, ED visits, palliative care consultation, ICU and hospital-free days, death and hospital readmission within 7 days of discharge

• Among hospitalized older adults with serious illness, Jumpstart found that a pragmatic clinician-facing communication-priming intervention significantly improved documentation of goals of care discussions in the electronic health record, with a greater effect size in racially or ethnically minoritized patients.

• Jumpstart provides evidence that a low-touch intervention can nudge clinicians to change behavior. Overall prevalence of goals of care discussions is low suggestions opportunity for improvement. Jumpstart may be useful in enhancing equity in serious illness communication among racially or ethnically minoritized patients.

Learn more

Read more in JAMA and a JAMA editorial.

Discussion Themes

-Within individual physicians, did you see an increase in goals of care discussions? We have not looked within clinician practices for documentation. In some previous trials we have randomized at the clinician level. What’s hard is that patients have a number of different physicians. When we sent out the jumpstart we sent it to the entire team, from interns to attendings. We don’t have much data on the overall effect on individual physicians behavior.

-Were you to do this again would you use BERT again or go in a different direction? BERT was state of the art in 2018 and out preformed all other NLPs at the time. The same family of deep learning models is still considered state of the art. The key thing with these models is that they have gotten bigger to accommodate the language. We could possibly take out the training phase.

–What is the concern about prompt fatigue in these settings? It is something we are thinking about a lot in the outpatient trial. Clinicians could receive prompts on an almost daily basis. We are limiting it so clinicians can only receive prompts x times day.

Tags

#pctGR, @Collaboratory1

Speaker

Harlan M. Krumholz, MD, SM
Harold H. Hines, Jr. Professor of Medicine
Department of Internal Medicine
Section of Cardiovascular Medicine
Yale University School of Medicine
Director, Yale-New Haven Hospital Center for Outcomes Research and Evaluation

Slides

Keywords

Decentralized Trial, Digital Trial, Yale PaxLC Trial, Long COVID

Key Points

• The PaxLC Trial is a decentralized Phase 2, 1:1 randomized double-blind superiority placebo-controlled study on non-hospitalized high symptomatic adult participants with Long Covid to determine the efficacy, safety, and tolerability of 15 days of Nirmatrelvir/Ritonavir compared with a Placebo/Ritonavir.

• This talk describes the PaxLC Trial’s strategies to implement a digital, decentralized, and democratized approaches to multidisciplinary research that address the deficiencies of traditional research studies, which can tend to be hierarchical, siloed, slow, expensive, and inconvenient.

• A key question for the study was how do we put the interest of the participants first and foremost and produce knowledge rapidly? Research can improve by simultaneously leveraging advances in technology and culture. Studies must be convenient, meaningful, respectful, efficient, rapid, and fair.

• The research optimization requires partnership with participants, including involvement in study design and workflow, data collection and analysis, and data and results sharing. Participants should have access to investigators and the results of studies.

• PaxLC brings together many innovations including online screening, digital medical record review, e-consent, home-delivery of medications, local clinical blood draws, home-based biospecimen collection, online diaries and surveys, digital medical record outcomes, and participant-centricity, and return of results. During the presentation, members from across the research team shared how PaxLC implemented all of these innovations through the course of the trial.

Discussion Themes

-What have you experienced on the scalability of the approaches you have taken, such as regulations and IRB? Is this knowledge generalizable to other similar trials? Using the local Yale IRB was an asset. We had to have the right people in the room to initiate communication with collaborators and their IRB representatives. The Yale IRB and Trusted Medical helped facilitate the other states and anything we need to take in account for recruitment. We will have a repository with Trusted Medical for future studies. We had to operate the trial with an institution that would allow zero risk. We had to come up with solutions.

-Can you comment on the decision to make the PROMIS scale the primary outcome? Does it capture most what matters to patients? We spent a lot of time discussing what would be the best primary outcome. Pfizer provided feedback. We wanted to measure a lot of stuff. We were familiar with the PROMIS scale and  felt PROMIS would capture whether people were feeling better. We wanted to say if this is working, people’s general health should improve.

–How do you organize recruitment to ensure you have the diversity you want and how do you explain to people you are turning away? It’s a multipronged strategy. Be welcoming and be authentically committed to health equity and be worthy of the trust. One of the main reasons we turn people away is because they are taking a medication that does not work with Paxlovid. A lot of participants have worked with their physician to stop a drug so they can participate.

Tags

#pctGR, @Collaboratory1

Speakers

Rachael L. Fleurence, PhD, MSc 
Senior Advisor
National Institutes of Health

Joshua M. Sharfstein, MD
Vice Dean for Public Health Practice and Community Engagement
Director, Bloomberg American Health Initiative
Professor of the Practice in Health Policy and Management

Slides

Keywords

Hepatitis C, NIH, PCORnet

Key Points

• The advances in Hepatitis C drugs is one of the greatest successes in clinical research in the last 20 years, yet Hepatitis C is a public health crisis in the U.S. with the rate of reported acute Hepatitis C cases increasing 400% during 2010-2020. Rates are the highest among 20-39-year-olds.

• Untreated, chronic Hepatitis C infection leads to liver damage, liver cancer, and death. There is now a cure for Hepatitis C, but many people are not able to access it. Many people do not know they have Hepatitis C (about 40% of patients). There is a lack of point-of-care diagnostics; it can take up to 3 steps to treatment initiation. There is high cost of treatment and insurance prior-authorization requirements. The treatment is not a routine part of primary care, and there is an underserved and hard-to-reach population.

• Even when diagnosed, only 1 in 3 adults are cured of Hepatitis C in the U.S. Pilots for elimination programs for Hepatitis C have been successful in individual states and other countries.

• To address this crisis, the NIH is embarking on a National Initiative on Hepatitis C. The initiative would bring to the U.S. point of care diagnostic tests, provide broad access to curative Hepatitis C medications with a national subscription model, with Medicare co-pay assistance and commercial insurance coverage.

• The initiative will also empower implementation efforts through a public awareness campaign, expansion of screening strategies and settings, especially for high-risk populations; expansion of the number of providers using innovative telehealth methods such as the ECHO program; and expansion of the number of community health workers who can link people to care.

• There are possible clinical research components that would include research on treatment during pregnancy, vaccine development, and implementation model research.

• The economic benefits of a Hepatitis C elimination program would save lives and have enormous financial benefits to Medicare and Medicaid, paying for the program within 10 years.

• PCORnet has been an important resource by executing a query to identify the volume of HCV tests conducted by participating health systems and the number of co-infections with Hepatitis B virus. A manuscript is under development. PCORnet is engaging with sites to support the ITAP clinical study for de novo clearance of a qualitative POC HCV test-to-treat platform. Discussions are currently underway with 13 partner sites.

• Unless we take action, our system will be spending tens of billions of dollars for Hepatitis C care over the coming decades for people already infected. The current trends of Hepatitis C epidemiology in the U.S. show that a cure is not sufficient to guarantee disease elimination.

Discussion Themes

–When did NIH begin to think a program like this was possible? In early 2022 Dr. Collins was asked to serve as Biden’s acting science advisor, and Dr. Collins wanted to use his position at the White House to help advance health and medical space. He came to the conclusion that Hepatitis C has the most untapped potential to benefit from White House support. We spent 15 months working with the White House to get the program in the president’s budget and now our focus is on the Hill.

-What are the policy considerations to get to easier testing? Our review of the data from other countries is that having same-day tests available for certain settings and populations is a helpful strategy. The hope is that we can have coherent and national organization to roll out this program and get the point of care tests in places where it really matters to have these tests.

Tags

#pctGR, @Collaboratory1

Speaker

Keith Marsolo, PhD
Associate Professor
Department of Population Health Sciences
Duke University School of Medicine

Slides

Keywords

PCORnet, Common Data Model, EHR, Social Determinants of Health

Key Points

• There are many different definitions of social determinants of health. The World Health Organization defines social determinants of health as non-medical factors that influence health outcomes and conditions in which people are born, grow, work, live, and age, and the wider set of forces and systems shaping the conditions of daily life.

• The PCORnet Common Data Model (CDM) includes data available from Clinical Research Networks. Some data, such as basic clinical data and demographics, are ready for research. Other data, such as immunizations, social determinants of health (SDOH), patient-generated data, and others, may or may not be in the PCORnet Common Data Model and require additional work for use in research.

• In 2021-2022, PCORI contracted with NORC at the University of Chicago to undertake a series of convenings to consider data infrastructure enhancements to PCORnet. A social determinants of health convening built upon efforts of prior PCORnet SDOH workgroup and included survey development, key informant interviews, and public webinars.

• When we talk about patient-level SDOH measures, the CDM has some general purpose tables that can store that data. Adding these data to the CDM generally involves several steps. Identifying whether there are codes to represent the measures in standard terminologies. Partners then must find the relevant measures within their EHRs and harmonizing them to the appropriate code. In many EHRs, data may be captured using various workflows over time, which can also affect the overall data completeness.

• For example, consider food security. 22 sites within the network were able to load some record of food security. There was a wide spread of information that was available. Insurance status is captured in an encounter level with the payor name. It is often that you have to take the raw values and harmonize to a particular insurance type (source of payment typology). Partners within the network have to take raw names and try to harmonize those to a specific type of insurance. It can be complicated to tease out by the insurance name.

• PCORnet has demonstrated that patient-level SDOH data can be incorporated to the CDM. Data availability is dependent on adoption and utilization by health systems. It may be suitable for studies on targeted populations but will depend on collection practices at a given health system.

• Area-level measures can provide population-level SDOH insights. 5-digit zip and county can be included in Limited Data Sets and are more easily used in distributed analytics. Capabilities for geocoding exist at many institutions but will require involvement of local personnel to generate values based on census tract or latitude/longitude. May be be best suited for specific studies.

 

Discussion Themes

-Have the data been used by PCORnet studies? It is new and has not been used widely across studies.

–What are you measuring with insurance status and can you capture in a model individual effect and social effect? We are working to get the best information we can. Having no insurance information about the patient can be problematic. We are trying to work with sites to find the right level of granularity when describing insurance. Insurance status does not highlight whether a patient lives in a food desert or in unsafe housing. It is important to look at what is available to people, what interventions are able to be done by the cluster data – by housing authorities, federal groups, and the health system. First steps are getting the data in such a state that would allow us to learn about some of those social determinants of health.

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Speakers

Speaker: David M. Murray, PhD
NIH Associate Director for Prevention and Director, NIH Office of Disease Prevention

Moderator: Jonathan C. Moyer, PhD
Statistician, NIH Office of Disease Prevention

Slides

Keywords

Implementation; Study design; Hybrid; Clustered; DECIPHeR

Key Points

• People often contest that hybrid designs are not as rigorous as they should be. The use of the term “hybrid design” is unfortunate, as it suggests that implementation research has different methods than other research and might not be held to the same standards. Instead, we should use the same rigorous methods for implementation research that we use for other research and simply change the focus.

• The Disparities Elimination through Coordinated Interventions to Prevent and Control Heart and Lung Disease Risk (DECIPHeR) initiative at the National Heart, Lung, and Blood Institute (NHLBI) is their first major effort to conduct implementation research. Through this initiative, 7 Clinical Centers are expected to test an evidence-based, multi-level intervention designed to reduce or eliminate cardiovascular and/or pulmonary health disparities. One of the key features was that implementation measures were to be used as primary outcomes.

• The NHLBI created the Technical Assistance Workgroup, building on the model established by the NIH Pragmatic Trials Collaboratory, with the goal of helping the Clinical Centers create the strongest possible application for the UH3 phase. The group considered the project aims, study design, statistical analysis plan, and power analysis for each center until all aspects were aligned. They also helped review each Clinical Center’s protocol before it went to the DSMB and NHLBI for transition to the UH3 phase.

• While working across the 7 projects, the Technical Assistance Workgroup encountered several design and analytic issues, including: ensuring emphasis on implementation outcomes, research designs for Type I, II, and III Hybrid Studies, intervention versus implementation strategies, the need to address clustering, cross-classification and multiple membership, time-varying intervention effects, data based parameter estimates, blinding, and adaptations of intervention and implementation strategies.

• Throughout the time the Technical Assistance Workgroup worked with the 7 Clinical Centers, they learned that implementation research has its own practices in many design and analysis areas. They also learned that consensus is lacking in many areas, such as blinding and adaptation, even among the implementation research community. They learned that researchers outside of the implementation research community often do not understand the features common to implementation research, and there’s a benefit to bringing the two communities together for review of proposed studies. They found that involving methodologists familiar with clustered designs and their analytic and power issues was a key factor in their success.

• The results of the Technical Assistance Workgroup’s involvement in the Clinical Centers’ development and proposal process was a much stronger set of proposals for the UH3 phase of DECIPHeR.

Learn more

Visit the DECIPHeR website.

Discussion Themes

-You previously mentioned that the practice of blinding is extremely common in clinical trials and less so in implementation studies. In implementation studies, how can you blind outcome assessor? Is independent adjudication a possible solution? Yes, that’s one solution. It’s relatively easy to blind outcome assessors. It’s not bulletproof, but first, you have intervention and implementation staff that are completely independent from measurement staff. Second, you shouldn’t tell the staff collecting the outcome data which arm the various sites are in. To the extent that you can use data from electronic health records, that will help with keeping the staff blinded. It’s important to note that it’s more difficult to keep the centers, or actual clusters, blinded. It’s important for them to know that they’re getting an intervention, even if they’re not aware which one.

-Could you expand more on what implementation outcomes are? Examples of implementation measures include acceptability, adoption, appropriateness, affordability, cost, feasibility, fidelity, reach, etc. In most clinical trials, we might measure these as process outcomes. However, in an implementation trial, these are the most interesting measures. An implementation study generally is used when you have an intervention that has already been shown to be effective on health outcomes, so it’s less important to be concerned about that. The real interest is in finding out about how to improve acceptability, adoption, fidelity, etc. so that people will use the research we’ve done.

-This was an impactful collaboration between NIH investigators and study teams and methodologists. How can this kind of collaboration happen more widely? I wish that those of us involved had enough bandwidth to get involved with every major initiative that NHLBI launches, but that’s unfortunately not possible. This collaboration was a special case with a very good biostatistics and design working group. DECIPHeR benefitted greatly from having the Techinical Assistance Workgroup, and anytime you have a study with a coordinating center, a similar group could be an important function of the coordinating center. However, I don’t have a great general solution for that issue at this point.  

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#pctGR, @Collaboratory1

Speakers

Claire Snyder, PhD
Professor
Johns Hopkins Schools of Medicine and Public Health

Norah Crossnohere, PhD
Assistant Professor
Ohio State University College of Medicine

Anne Schuster, PhD
Research Scientist
Ohio State University College of Medicine

Slides

Keywords

Patient-Reported Outcomes, PROs, PROTEUS Consortium

Key Points

• The Patient-Reported Outcomes Tools: Engaging Users & Stakeholders (PROTEUS) Consortium initially focused on PROs in clinical trials and then expanded to use in clinical practice. The PROTEUS Consortium’s objective is to ensure that patients, clinicians, and other decision-makers have high-quality PRO data from clinical trials and clinical practice to make the best decisions they can about treatment options.

• The PROTEUS Consortium partners with key stakeholder groups to disseminate and implement tools that have been developed to optimize the use of PROs in clinical trials and practice.

• There are more than 50 organizations with participating in PROTEUS, including clinicians and patient advocates, research and methods organizations, clinical trials groups, funding and government agencies, and universities and health systems.

• Over the last decade, collection of guidance documents and resources have been developed to develop each of the steps in the clinical trial directory. The PROTEUS website has web tutorials, checklists, and a handbook on topics such as displaying data for patients and clinicians and researchers.

• The PROTEUS-Practice Guide offers support for designing, implementing, and managing PRO systems in clinical care. It collates and synthesizes foundational resources to create a unified, comprehensive resource. For each consideration, the Guide provides a range of options rather than one “right” way. In almost all cases, the options are not mutually exclusive, and it is advisable to adopt multiple approaches. The Guide is applicable to a broad range of health systems.

• The PROTEUS-Practice Learning Health Network includes 10 funded projects who come together with members across the PROTEUS Consortium for monthly meetings hosted by PROTEUS that provide a forum to share experiences and lessons learned.

• Building off the request for proposals process for the Learning Health Network, PROTEUS recognized that institutions caring for vulnerable and underserved populations may face unique challenges when aiming to implement PROs in routine care.

• PROTEUS and Pfizer partnered to form an Advisory Group that aimed to improve understanding of the facilitators and barriers of implementing routine PRO assessments in vulnerable and underserved populations and build capacity for PRO implementation to improve care for cancer patients who are vulnerable or underserved.

• The Advisory Group identified 47 different potential solutions to address the top barriers. PROTEUS leaders reviewed and categorized the solutions into 4 categories: education and engagement, information technology or technological resources, incentives, mandates, and marketing, and research.

 

Learn more

Visit the PROTEUS Consortium

Discussion Themes

-Can you explain why the error bars and P values are not on some of the graphs in the presentation? This question gets at the importance of tailoring presentations for the intended audience. In our research, we learned that patients actively do not want to see error bars and P values on graphs. They didn’t know what they meant and found them confusing, and it strongly interfered with their ability to engage with the information. For clinicians and researchers, there was value in showing them.

-Can you elaborate on the incentives and marketing recommendation from the Underserved Advisory Group? Some of the discussion included insurance coverage where PRO monitoring is included with a prescribed treatment plan. There was discussion about paying patients and patient advocates for the time they spend advocating for PROs. In terms of marketing, including patient preferences in marketing throughout.

–How is PROTEUS following up on the recommendations regarding underserved populations? With current funding, PROTEUS can address recommendations specifically regarding education materials. We are pursuing funding. We are an implementation and dissemination project so not all traditional funding sources are available to us

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#pctGR, @Collaboratory1