Advanced Heart Failure Workshop: Trajectories and Triage for Ambulatory Stage Pre-D and D

The National Heart, Lung, and Blood Institute (NHLBI) convened the “Advanced Heart Failure (HF) Workshop: Trajectories and Triage for Ambulatory Stage Pre-D and D” to discuss gaps and opportunities to advance the care of ambulatory heart failure (HF) patients in late stages. Participants included healthcare practitioners, researchers, and policymakers with expertise in adult and pediatric cardiology, HF, heart transplant (HT), mechanical circulatory support (MCS), geriatrics, palliative care, health disparities, clinical trials, and outcomes research. The virtual workshop objectives were to:

• Evaluate critical knowledge gaps, challenges, priorities, and approaches to identify and manage advanced HF in the context of current diagnostic, therapeutic medical, and surgical modalities aligned with patient goals for enhanced quality and length of life
• Explore novel approaches to therapies and new information about social determinants of health (SDOH) and resource allocation

A concurrent Think Tank was held for fellows and junior faculty to propose observational studies and/or clinical trials to progress the advanced HF field. The workshop is responsive to NHLBI Strategic Vision Objectives 2-8.

Background

The American College of Cardiology, American Heart Association, Heart Failure Society of America, and European Society of Cardiology have each proposed advanced HF definitions for patients who appear to have progressed “beyond guideline-directed medical therapies (GDMT).” The lack of clear criteria to define these patients contributes to serious challenges in identifying them and ensuring equitable access to HT, durable MCS (dMCS), and alternative therapies. The prognosis varies substantially due to differential access to these recommended therapies. Much effort has been devoted to HT triage from intensive care units, but there is no clear path for ambulatory advanced HF patients. Compared to the HT waiting list, the ambulatory pre-D and D-stage population sizes are at least twenty times larger, with disease burden that has likely been underestimated.

Advanced therapy access and outcomes are compromised by SDOH (e.g., socioeconomic status, structural racism, interpersonal bias, discrimination, educational attainment, health literacy). Changes in donor heart preservation and HT allocation affect both MCS and ambulatory patients with HF on medical therapy alone. Improved dMCS outcomes have yet to be translated into equitable access and shared decision-making. Determining the role of current home inotropic infusion therapy and other potential options will be accelerated by recognizing the magnitude and priorities of ambulatory patients with pre-D and D stage HF with many continuing to seek survival with meaningful quality-of-life (QOL). Wide information gaps comparing HT, MCS, and alternative therapy outcomes exist. These data are required for shared decision-making with major implications for patients, caregivers, clinicians, and policymakers and links directly with SDOH. There is an urgent need to identify, characterize, and explore treatments for this large ambulatory advanced HF population.

Discussions

The workshop focused on six areas: 1) ambulatory advanced HF clinical presentation and phenotypes; 2) medical therapies before and after advanced HF definition; 3) HT organ supply, allocation, and outcomes; 4) temporary and durable MCS allocation and outcomes; 5) social environment and resource limitations; and 6) appropriate data collection and approaches for shared decision-making.

Overarching Goals: Participants identified three overall goals.

• Identify clinical phenotypes and disease trajectory of ambulatory advanced HF patients
• Focus on selecting the best therapies for each patient, which include specific medical and surgical therapies, but also enhanced access to multi-disciplinary care, rather than selecting the best patients for each high-profile therapy
• Improve data and processes for shared decision-making and equitable access to advanced therapies with a focus on SDOH and patient-centered outcomes

Gaps and Opportunities: The following gaps and opportunities were identified to guide future research.

Ambulatory Advanced HF
Gap 1: Define a large ambulatory advanced HF population, which is not well-recognized

• Determine the ambulatory advanced HF population size (e.g., hospitalization screening) and whether advanced therapies were considered
• Depict population as a larger concentric circle funneling to smaller, actionable circles of those referred, evaluated, and accepted for HT or left ventricular assist device (LVAD) (Figure 1)
• Delineate advanced HF phenotypes (e.g., age/frailty, right HF, cardiorenal/hepatic dysfunction, resource limitation/SDOH)and how these influence prognoses

Gap 2: Learn about patients who are referred/evaluated but do not receive a LVAD or HT

• Collect uniform longitudinal data and objective functional capacity/limitation assessments to assess patient phenotypes, preferences, and reasons for declining therapies; provider reasons for not offering therapies; eligibility changes; reversible/non-reversible and SDOH factors and outcomes; and treatment options

Gap 3: Explore how to target therapies more precisely for the ambulatory HF patients

• Move away from considering HF therapy as a dichotomy as either full GDMT or the customary triad limited to HT, LVAD, or hospice and target therapies based on phenotypes
• Determine phenotypes, outcomes, and care goals of the advanced HF population size on home intravenous (IV) inotropic therapy
• Assess current and potential therapy efficacy, considering advanced HF GDMT data limits
• Add patient-reported outcomes and serial functional capacity assessment for survival quality rather than quantity

Triage for HT and LVAD
Gap 1: Identify patients who may be disadvantaged by the current allocation system when therapy based on physiological need confers lower priority

• Develop predictive biomarkers and obtain more robust patient phenotype data [including long-term follow-up, United Network for Organ Sharing (UNOS), electronic health record (EHR)], leveraging artificial intelligence (AI)/machine learning methods
• Understand LVAD/HT barriers [e.g., gather data on temporary MCS (tMCS) vs. Organ Procurement and Transplantation Network (OPTN) inotrope criteria], focusing on marginalized populations and perceived nonmedical barriers to post-LVAD/HT success

Gap 2: Improve dMCS use as a complement or alternative therapy to HT

• Identify potential HT patients with a myocardial recovery possibility enabled by dMCS use to delay or obviate HT need
• Model how different patient subset characteristics may affect dMCS outcomes to inform bridge-to-transplant or candidacy opportunities more appropriately, as well as re-enable patient and provider equipoise
• Assess how patients perceive current clinician communication, asking clinicians and patients what data and information helps decision-making.

Gap 3: Direct patients to advanced HF centers through earlier referrals

• Develop HF models to share care between community and quaternary heart centers
• Improve education for patients with HF symptoms and educate clinicians on high-risk HF patient identification, advanced HF treatment options, and management
• Track referrals as a clinician-healthcare system performance quality measure
• Define and extend HT/LVAD resources to previously ineligible patients; and identify specialty care barriers

Gap 4: Synergize LVAD and HT in the same patient to maximize QOL and survival benefit

• Evaluate complementary treatment strategies [e.g., bridge-to-myocardial recovery for increased GDMT (particularly for GDMT-naïve patients), extended bridge-to-transplant] to delay HT and extend the overall survival benefit of combined therapies
• Identify higher risk patients (e.g., AI, EHR) who may benefit from dMCS rather than HT to permit heart allocation to those with HT as the only option (e.g., pediatric patients) or when HT provides greater survival benefit

Improving Equitable Access to Therapies and Supporting Shared Decision-making
Gap 1: Ensure access to all patients with advanced therapy needs

• Collect standardized data (e.g., EHR, SDOH toolkit) across multiple HT and LVAD centers
• Develop pilot programs to demonstrate return on investment for community partnerships and incentivize health systems and clinicians
• Evaluate approaches to integrate community specialty care for earlier patient, general cardiologist, and advanced HF provider collaborations (e.g., prioritizing a multidisciplinary team approach with a SDOH focus)
• Undertake implementation and dissemination studies to better understand which models work
• Survey current and in-training staff (including those not in HF) to understand multi-disciplinary care opportunities and barriers for HT and LVAD-ineligible patients

Gap 2: Engage the community

• Evaluate use of community champions to build trust circles; address patient big data collection concerns, discrimination, and care differentiation; and promote collaborative care management
• Investigate use of remote monitoring, decentralize evaluations, and support innovative programs tailored to address patient barriers to care

Gap 3: Use the information to improve our evaluation process to support our patients

• Investigate lingering biases despite using apparently objective decision-making measures
• Identify the most important individual- and community-level SDOH elements in subgroups

Gap 4: Traverse different patient needs in accessing care and engaging in shared decision-making

• Survey what matters most to patients; hold iterative evaluations (e.g., interviews, surveys), anchoring on changing values over time; and link to phenotypes including SDOH
• Develop, test, and utilize patient and decision-support tools
• Estimate the reach and draw of direct-to-consumer marketing in targeted regions
• Incorporate mixed methods approaches to address the need for more study diversity

Gap 5: Improve decision support for clinicians/patients/social support systems

• Dedicate training for clinician-patient decision support tools that support quality decision-making and appropriate resource activation
• Test what information combinations are most helpful to improve comprehension and reduce uncertainty for patient decision-making through enhanced EHR and logic architecture use
• Develop and evaluate approaches to place information (e.g., EHR, lab results, diagnostic tests, available social support services) in patients’ hands and relay in a way that patients can understand with grounded expectations

Publication Plans

A white paper outlining workshop gaps and opportunities is in preparation.

Workshop Participants

Co-Chairs

• Alanna Morris, MD, MSc, Emory University
• Francis Pagani, MD, PhD, University of Michigan
• Lynne Warner Stevenson, MD, Vanderbilt University

Faculty Speakers

• Larry Allen, MD, MHS, University of Colorado
• Khadijah Breathett, MD, MS, Indiana University
• Rebecca Cogswell, University of Minnesota
• Monica Colvin, MD, MS, University of Michigan
• Jennifer Cowger, MD, MS, Henry Ford Health
• Stavros Drakos, MD, PhD, University of Utah
• Shannon Dunlay, MD, MS, Mayo Clinic
• Laura Gelfman, MD, MPH, Icahn School of Medicine at Mount Sinai
• Manreet Kanwar, MD, Allegheny Health Network
• Michael Kiernan, MD, MSc, Tufts Medical Center
• Michelle Kittleson, MD, PhD, Cedars-Sinai University of California Los Angeles
• Anuradha Lala-Trindade, MD, Icahn School of Medicine at Mount Sinai
• Eldrin Lewis, MD, MPH, Stanford University
• Colleen McIlvennan, PhD, DNP, ANP, University of Colorado
• Nader Moazami, MD, New York University
• Modele Ogunniyi, MD, MPH, Emory University
• Ambarish Pandey, MD, University of Texas Southwestern
• Sean Pinney, MD, Mount Sinai Morningside
• Joseph Rogers, MD, Texas Heart Institute
• Kurt Schumacher, MD, MS, University of Michigan
• Mark Slaughter, MD, University of Louisville
• Garrick Stewart, MD, MPH, Brigham and Women’s Hospital
• Ryan Tedford, MD, Medical University of South Carolina
• Jeffrey Teuteberg, MD, Stanford University
• Hannah Valantine, MD, MBBS, Stanford University

Think Tank Fellows and Junior Faculty

• Ersilia M. DeFilippis, MD, Columbia University
• Debra Dixon, MD, MS, Vanderbilt University
• Jessica Golbus, MD, MS, University of Michigan
• Gaurav Gulati, MD, MS, Tufts Medical Center
• Thomas Hanff, MD, MPH, University of Utah
• Stephanie Hsiao, MD, Stanford University
• Sabra Lewsey, MD, MPH, Johns Hopkins University
• Amanda McCormick, MD, University of Michigan
• Aditi Nayak, MD, MS, Brigham and Women’s Hospital

NIH Staff

• Renee Wong, PhD, NHLBI
• Kathleen Fenton, MD, NHLBI
• Catherine Burke, MA, NHLBI
• Maria Carranza, PhD, NIA (National Institute on Aging)
• Lisa Schwartz Longacre, PhD, NHLBI
• Marissa Miller, DVM, MPH, NHLBI
• Sujata Shanbhag, MD, MPH, NHLBI
• Wendy Taddei-Peters, PhD, NHLBI