Race and Ethnicity in Heart Failure: JACC Focus Seminar 8/9

Multi-year population-based analysis of Asian patients with acute decompensated heart failure and advanced chronic kidney disease

BACKGROUND

Data on disparities in outcomes and risk factors in Asian patients with advanced chronic kidney disease admitted for heart failure are scare.

METHODS

This was a retrospective cohort study that utilized data from the National Inpatient Sample between January 2016 and December 2019. Patients who had a primary diagnosis of acute decompensated heart failure and a concomitant diagnosis of advanced CKD were included. The primary outcome of interest was in-hospital mortality. Secondary outcomes include hospital cost, length of stay, and other clinical outcomes. Weighted multivariable logistic regression was used to adjust for comorbidities.

RESULTS

There were 251,578 cases of ADHF with advanced CKD, out of which 2.6 % were from individuals of Asian ethnicity. Asian patients exhibited a higher burden of comorbidities in comparison to other UREM patients, but a lower burden than White patients. Regardless of differences in comorbidity burden, Asian patients exhibited a higher likelihood of experiencing severe consequences. After adjusting for comorbidies, White (OR:1.11; 95 % CI 1.03-1.20;0.009) patients had higher odds of mortality than Asian patients. However, Blacks (OR: 0.58; 95 % CI 0.53 to 0.63; p < 0.001) and Hispanics (OR: 0.69; 95 % CI 0.62 to 0.78; p < 0.001) had lower odds of mortality.

CONCLUSION

This first population-based studies shows that Asian patients with advanced CKD admitted for ADHF have greater comorbidity burden and poorer outcomes Black and Hispanic patients. This data underscores the importance of comprehensive approaches in phenotyping, and ethnic specific interventions.

Paving a Path to Equity in Cardiorenal Care

Cardiorenal syndrome encompasses a dynamic interplay between cardiovascular and kidney disease, and its prevention requires careful examination of multiple predisposing underlying conditions. The unequal distribution of diabetes, heart failure, hypertension, and kidney disease requires special attention because of the influence of these conditions on cardiorenal disease. Despite growing evidence regarding the benefits of disease-modifying agents (e.g., sodium-glucose cotransporter 2 inhibitors) for cardiovascular, kidney, and metabolic (CKM) disease, significant disparities remain in access to and utilization of these essential therapeutics. Multilevel barriers impeding their use require multisector interventions that address patient, provider, and health system-tailored strategies. Burgeoning literature also describes the critical role of unequal social determinants of health, or the sociopolitical contexts in which people live and work, in cardiorenal risk factors, including heart failure, diabetes, and chronic kidney disease. This review outlines (i) inequality in the burden and treatment of hypertension, type 2 diabetes, and heart failure; (ii) disparities in the use of key disease-modifying therapies for CKM diseases; and (iii) multilevel barriers and solutions to achieve greater pharmacoequity in the use of disease-modifying therapies. In addition, this review provides summative evidence regarding the role of unequal social determinants of health in cardiorenal health disparities, further outlining potential considerations for future research and intervention. As proposed in the 2023 American Heart Association presidential advisory on CKM health, a paradigm shift will be needed to achieve cardiorenal health equity. Through a deeper understanding of CKM health and a commitment to equity in the prevention, detection, and treatment of CKM disease, we can achieve this critical goal. Semin Nephrol 36:x-xx © 20XX Elsevier Inc. All rights reserved.

Healthcare Disparities in Cardiovascular Medicine

There are significant healthcare disparities in cardiovascular medicine that represent a challenge for cardiologists and healthcare policy-makers who wish to provide equitable care. Disparities exist in both the management and outcomes of hypertension, coronary artery disease and its sequelae, and heart failure. These disparities are present along the lines of race, gender, and socioeconomic status. Despite recent efforts to reduce disparity, there are knowledge and research gaps among cardiologists with regards to both the scope of the problem and how to solve it. Solutions include increasing awareness of disparities in cardiovascular health, increasing research for optimal treatment of underserved communities, and public policy changes that reduce disparities in social determinants of health.

Leveraging digital health technologies in heart failure self-care interventions to improve health equity

For patients with heart failure, self-care is crucial, but recommendations for self-care are often not adhered to. Digital health interventions have the potential to help patients improve their self-care and, ultimately, their health outcomes, but without careful examination, digital interventions may generate further inequality. In this study, we use a digital health equity framework to examine challenges and opportunities related to digital health interventions across many domains of influence that may affect health equity. We draw from our own experience in conducting a decentralized clinical trial with multiple sensors and apps to help patients with heart failure improve adherence to key self-care behaviors. Finally, we provide recommendations for leveraging digital health interventions in research and practice to address health equity.

Factors Impacting Physician Prognostic Accuracy in Heart Failure Patients With Reduced Left Ventricular Ejection Fraction

BACKGROUND

A recent study showed that the accuracy of heart failure (HF) cardiologists and family doctors to predict mortality in outpatients with HF proved suboptimal, performing less well than models.

OBJECTIVES

The authors sought to evaluate patient and physician factors associated with physician accuracy.

METHODS

The authors included outpatients with HF from 11 HF clinics. Family doctors and HF cardiologists estimated patient 1-year mortality. They calculated predicted mortality using the Seattle HF Model and followed patients for 1 year to record mortality (or urgent heart transplant or ventricular assist device implant as mortality-equivalent events). Using multivariable logistic regression, the authors evaluated associations among physician experience and confidence in estimates, duration of patient-physician relationship, patient-physician sex concordance, patient race, and predicted risk, with concordant results between physician and model predictions.

RESULTS

Among 1,643 patients, 1-year event rate was 10% (95% CI: 8%-12%). One-half of the estimates showed discrepant results between model and physician predictions, mainly owing to physician risk overestimation. Discrepancies were more frequent with increasing patient risk from 38% in low-risk to ∼75% in high-risk patients. When making predictions on male patients, female HF cardiologists were 26% more likely to have discrepant predictions (OR: 0.74; 95% CI: 0.58-0.94). HF cardiologist estimates in Black patients were 33% more likely to be discrepant (OR: 0.67; 95% CI: 0.45-0.99). Low confidence in predictions was associated with discrepancy. Analyses restricted to high-confidence estimates showed inferior calibration to the model, with risk overestimation across risk groups.

CONCLUSIONS

Discrepant physician and model predictions were more frequent in cases with perceived increased risk. Model predictions outperform physicians even when they are confident in their predictions. (Predicted Prognosis in Heart Failure [INTUITION]; NCT04009798).

Epidemiology and Definition of Heart Failure in Adult Congenital Heart Disease

Adults with congenital heart disease (ACHD) are facing lifelong complications, notably heart failure (HF). This review focuses on classifications, incidence, prevalence, and mortality of HF related to ACHD. Diagnosing HF in ACHD is intricate due to anatomic variations, necessitating comprehensive clinical evaluations. Hospitalizations and resource consumption for ACHD HF have significantly risen compared with non-ACHD HF patients. With more than 30% prevalence in complex cases, HF has become the leading cause of death in ACHD. These alarming trends underscore the insufficient understanding of ACHD-related HF manifestations and management challenges within the context of aging, complexity, and comorbidity.

Incorporating Diversity, Equity, and Inclusion Content Into Bioengineering Curricula: A Program-Level Approach

Diversity, equity, and inclusion (DEI) are interconnected with bioengineering, yet have historically been absent from accreditation standards and curricula. Toward educating DEI-competent bioengineers and meeting evolving accreditation requirements, we took a program-level approach to incorporate, catalog, and assess DEI content through the bioengineering undergraduate program. To support instructors in adding DEI content and inclusive pedagogy, our team developed a DEI planning worksheet and surveyed instructors pre- and post-course. Over the academic year, 74% of instructors provided a pre-term and/or post-term response. Of responding instructors, 91% described at least one DEI curricular content improvement, and 88% incorporated at least one new inclusive pedagogical approach. Based on the curricular adjustments reported by instructors, we grouped the bioengineering-related DEI content into five DEI competency categories: bioethics, inclusive design, inclusive scholarship, inclusive professionalism, and systemic inequality. To assess the DEI content incorporation, we employed direct assessment via course assignments, end-of-module student surveys, end-of-term course evaluations, and an end-of-year program review. When asked how much their experience in the program helped them develop specific DEI competencies, students reported a relatively high average of 3.79 (scale of 1 = "not at all" to 5 = "very much"). Additionally, based on student performance in course assignments and other student feedback, we found that instructors were able to effectively incorporate DEI content into a wide variety of courses. We offer this framework and lessons learned to be adopted by programs similarly motivated to train DEI-competent engineering professionals and provide an equitable, inclusive engineering education for all students.

Identifying racial disparities in the management of heart failure with reduced ejection fraction

OBJECTIVE(S)

Heart failure (HF) is chronic and progressive. Individuals with a left ventricular ejection fraction (LVEF or EF) < 40% are classified as having heart failure with reduced ejection fraction (HFrEF). Black patients have the highest incidence of HF and are more likely to suffer serious consequences from the disease. Identifying and addressing racial disparities in care is vital to ensuring health equity. The primary objective was to determine the association of race with 1-year heart HF admission rates for white and black patients, when adjusted for EF and age. The secondary objective was to determine the proportion of patients not on guideline-directed medication therapy (GDMT).

DESIGN

This study was a retrospective chart review conducted between 10/22/2021 and 11/22/2022 of Veteran patients with HFrEF who were identified via the VA Heart Failure Dashboard. Only White and Black patients were included. A multivariable logistic regression was used to determine odds of admission due to HF. Pharmacotherapy was analyzed to identify gaps in GDMT and if racial disparities existed.

SETTING AND PARTICIPANTS

Veterans within the Veterans Affairs Western New York Healthcare System.

OUTCOME MEASURES

One-year HF admission rates for white and black patients, when adjusted for EF and age. Proportion of patients not on GDMT.

RESULTS

Of the 345 patients with HF originally identified, 172 were included; 22% were admitted within one year. Black patients were 2.9 times more likely to be admitted. (P = 0.031). A median of two drugs (interquartile range [IQR] 1-3) could be added and one dose could be optimized (IQR 1-4) to reach GDMT goals. No differences were found in the prescribing of GDMT or in proportion of patients not on GDMT at recommended doses between white and black patients.

CONCLUSION

Black patients were more likely to be admitted for HF than white patients. Pharmacists can play an important role in identifying the need for optimizing GDMT. Future studies could focus on pharmacist-led prospective interventions with an aim to close the gap in racial disparities.

Lifestyle Medicine: An Antidote to Cardiovascular Diseases

Despite numerous advances in basic understanding of cardiovascular disease pathophysiology, pharmacology, therapeutic procedures, and systems improvement, there hasn't been much decline in heart disease related mortality in the US since 2010. Hypertension and diet induced risk continue to be the leading causes of cardiovascular morbidity. Even with the excessive mortality associated with the COVID-19 pandemic, in 2020, heart disease remained the leading cause of death. Given the degree of disease burden, morbidity, and mortality, there is an urgent need to redirect medical professionals' focus towards prevention through simple and cost effective lifestyle strategies. However, current practice paradigm and financial compensation systems are mainly centered disease management and not health promotion. For example, the financial value placed on 3-10 min smoking cessation counseling (.24RVUs) is 47-fold lower than an elective PCI (11.21 RVUs). The medical community seems to be enamored with the latest and greatest technology, new devices, and surgical procedures. What if the greatest technology of all was simply the way we live every day? Perhaps when this notion is known by enough, we will switch to this lifestyle medicine technology to prevent disease in the first place.

Machine Learning and Bias in Medical Imaging: Opportunities and Challenges

Bias in health care has been well documented and results in disparate and worsened outcomes for at-risk groups. Medical imaging plays a critical role in facilitating patient diagnoses but involves multiple sources of bias including factors related to access to imaging modalities, acquisition of images, and assessment (ie, interpretation) of imaging data. Machine learning (ML) applied to diagnostic imaging has demonstrated the potential to improve the quality of imaging-based diagnosis and the precision of measuring imaging-based traits. Algorithms can leverage subtle information not visible to the human eye to detect underdiagnosed conditions or derive new disease phenotypes by linking imaging features with clinical outcomes, all while mitigating cognitive bias in interpretation. Importantly, however, the application of ML to diagnostic imaging has the potential to either reduce or propagate bias. Understanding the potential gain as well as the potential risks requires an understanding of how and what ML models learn. Common risks of propagating bias can arise from unbalanced training, suboptimal architecture design or selection, and uneven application of models. Notwithstanding these risks, ML may yet be applied to improve gain from imaging across all 3A's (access, acquisition, and assessment) for all patients. In this review, we present a framework for understanding the balance of opportunities and challenges for minimizing bias in medical imaging, how ML may improve current approaches to imaging, and what specific design considerations should be made as part of efforts to maximize the quality of health care for all.

Racial and Ethnic Disparities in Perioperative Health Care Among Patients Undergoing Cardiac Surgery: JACC State-of-the-Art Review

There has been little progress in reducing health care disparities since the 2003 landmark Institute of Medicine's report Unequal Treatment. Despite the higher burden of cardiovascular disease in underrepresented racial and ethnic groups, they have less access to cardiologists and cardiothoracic surgeons, and have higher rates of morbidity and mortality with cardiac surgical interventions. This review summarizes existing literature and highlights disparities in cardiovascular perioperative health care. We propose actionable solutions utilizing multidisciplinary perspectives from cardiology, cardiac surgery, cardiothoracic anesthesiology, critical care, medical ethics, and health disparity experts. Applying a health equity lens to multipronged interventions is necessary to eliminate the disparities in perioperative health care among patients undergoing cardiac surgery.

Quantitative Prediction of Right Ventricular Size and Function From the ECG

BACKGROUND

Right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep learning-enabled ECG analysis for estimation of right ventricular (RV) size or function is unexplored.

METHODS AND RESULTS

We trained a deep learning-ECG model to predict RV dilation (RVEDV >120 mL/m2), RV dysfunction (RVEF ≤40%), and numerical RVEDV and RVEF from a 12-lead ECG paired with reference-standard cardiac magnetic resonance imaging volumetric measurements in UK Biobank (UKBB; n=42 938). We fine-tuned in a multicenter health system (MSHoriginal [Mount Sinai Hospital]; n=3019) with prospective validation over 4 months (MSHvalidation; n=115). We evaluated performance with area under the receiver operating characteristic curve for categorical and mean absolute error for continuous measures overall and in key subgroups. We assessed the association of RVEF prediction with transplant-free survival with Cox proportional hazards models. The prevalence of RV dysfunction for UKBB/MSHoriginal/MSHvalidation cohorts was 1.0%/18.0%/15.7%, respectively. RV dysfunction model area under the receiver operating characteristic curve for UKBB/MSHoriginal/MSHvalidation cohorts was 0.86/0.81/0.77, respectively. The prevalence of RV dilation for UKBB/MSHoriginal/MSHvalidation cohorts was 1.6%/10.6%/4.3%. RV dilation model area under the receiver operating characteristic curve for UKBB/MSHoriginal/MSHvalidation cohorts was 0.91/0.81/0.92, respectively. MSHoriginal mean absolute error was RVEF=7.8% and RVEDV=17.6 mL/m2. The performance of the RVEF model was similar in key subgroups including with and without left ventricular dysfunction. Over a median follow-up of 2.3 years, predicted RVEF was associated with adjusted transplant-free survival (hazard ratio, 1.40 for each 10% decrease; P=0.031).

CONCLUSIONS

Deep learning-ECG analysis can identify significant cardiac magnetic resonance imaging RV dysfunction and dilation with good performance. Predicted RVEF is associated with clinical outcome.

Health equity in heart failure

The treatment of heart failure (HF) with reduced ejection fraction (HFrEF) has substantially developed over the past decades. More than ever before, the application of appropriate evidence-based medical therapy for HFrEF is associated with remarkable improvements in survival, noteworthy increases in quality of life, and a marked reduction in symptomatic HF sufficient to warrant hospitalization. These enhanced clinical outcomes are driven by the "four pillars" of HF therapy: 1) evidence-based beta blockers, 2) Renin-angiotensin-aldosterone system inhibitors (angiotensin-converting enzyme inhibitors /angiotensin II receptor blockers or angiotensin receptor-neprilysin inhibitors, 3) mineralocorticoid receptor antagonists, and most recently, 4) sodium-glucose cotransporter-2 inhibitors. Despite robust evidence from well-conducted randomized clinical trials, guideline-directed medical therapies with established cardiovascular benefits remain significantly underutilized in clinical practice, particularly among under-represented minority populations. This phenomenon has led to class 1 level recommendations from the 2022 American Heart Association/American College of Cardiology/Heart Failure Society of America Guidelines to address HF disparities among vulnerable populations as follows. In this article, we highlight the difference between health equality and health equity and discuss the need to address equity in the treatment of heart failure, ensuring that the impressive progress made in the treatment of HFrEF is equally beneficial to all individuals. We discuss strategies to reduce and ultimately eliminate disparities in the determinants of health that particularly affect marginalized groups, including the socioeconomic determinants and racism as a threat to public health. Finally, we discuss and propose a combination of the four pillars of ethics with the four pillars of GDMT to optimize and personalize treatment of all patients with HFrEF, to achieve true equity in the treatment of HF.

Exploring Heart Failure Mortality Trends and Disparities in Women: A Retrospective Cohort Analysis

Heart failure (HF) remains a significant cause of morbidity and mortality in women. Population-level analyses shed light on existing disparities and promote targeted interventions. We evaluated HF-related mortality data in women in the United States to identify disparities based on race/ethnicity, urbanization level, and geographic region. We conducted a retrospective cohort analysis utilizing the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research database to identify HF-related mortality in the death files from 1999 to 2020. Age-adjusted HF mortality rates were standardized to the 2000 US population. We fit log-linear regression models to analyze mortality trends. Age-adjusted HF mortality rates in women have decreased significantly over time, from 97.95 in 1999 to 89.19 in 2020. Mortality mainly downtrended from 1999 to 2012, followed by a significant increase from 2012 to 2020. Our findings revealed disparities in mortality rates based on race and ethnicity, with the most affected population being non-Hispanic Black (age-adjusted mortality rates [AAMR] 90.36), followed by non-Hispanic White (AAMR 83.25), American Indian/Alaska Native (AAMR 64.27), and Asian/Pacific Islander populations (AAMR 37.46). We also observed that nonmetropolitan (AAMR 103.36) and Midwestern (AAMR 90.45) regions had higher age-adjusted mortality rates compared with metropolitan (AAMR 78.43) regions and other US census regions. In conclusion, significant differences in HF mortality rates were observed based on race/ethnicity, urbanization level, and geographic region. Disparities in HF outcomes persist and efforts to reduce HF-related mortality rates should focus on targeted interventions that address social determinants of health, including access to care and socioeconomic status.

Eliminating Health Disparities in Atrial Fibrillation, Heart Failure, and Dyslipidemia: A Path Toward Achieving Pharmacoequity

PURPOSE OF REVIEW

Pharmacoequity refers to the goal of ensuring that all patients have access to high-quality medications, regardless of their race, ethnicity, gender, or other characteristics. The goal of this article is to review current evidence on disparities in access to cardiovascular drug therapies across sociodemographic subgroups, with a focus on heart failure, atrial fibrillation, and dyslipidemia.

RECENT FINDINGS

Considerable and consistent disparities to life-prolonging heart failure, atrial fibrillation, and dyslipidemia medications exist in clinical trial representation, access to specialist care, prescription of guideline-based therapy, drug affordability, and pharmacy accessibility across racial, ethnic, gender, and other sociodemographic subgroups. Researchers, health systems, and policy makers can take steps to improve pharmacoequity by diversifying clinical trial enrollment, increasing access to inpatient and outpatient cardiology care, nudging clinicians to increase prescription of guideline-directed medical therapy, and pursuing system-level reforms to improve drug access and affordability.

Achieving Health Equity in the Care of Patients with Heart Failure

PURPOSE OF REVIEW

To discuss the prevailing racial and ethnic disparities in heart failure (HF) care by identifying barriers to equitable care and proposing solutions for achieving equitable outcomes.

RECENT FINDINGS

Throughout the entire spectrum of HF care, from prevention to implementation of guideline-directed medical therapy and advanced interventions, racial and ethnic disparities exist. Factors such as differential distribution of risk factors, poor access to care, inadequate representation in clinical trials, and discrimination from healthcare clinicians, among others, contribute to these disparities. Recent data suggests that despite improvements, disparities prevail in several aspects of HF care, hindering our progress towards equity in HF care. This review highlights the urgent need to address racial and ethnic disparities in HF care, emphasizing the importance of a multifaceted approach involving policy changes, quality improvement strategies, targeted interventions, and intentional community engagement. Our proposed framework was derived from existing research and emphasizes integrating equity into routine quality improvement efforts, tailoring interventions to specific populations, and advocating for policy transformation. By acknowledging these disparities, implementing evidence-based strategies, and fostering collaborative efforts, the HF community can strive to reduce disparities and achieve equity in HF care.

Telehealth and Health Equity in Older Adults With Heart Failure: A Scientific Statement From the American Heart Association

Enhancing access to care using telehealth is a priority for improving outcomes among older adults with heart failure, increasing quality of care, and decreasing costs. Telehealth has the potential to increase access to care for patients who live in underresourced geographic regions, have physical disabilities or poor access to transportation, and may not otherwise have access to cardiologists with expertise in heart failure. During the COVID-19 pandemic, access to telehealth expanded, and yet barriers to access, including broadband inequality, low digital literacy, and structural barriers, prevented many of the disadvantaged patients from getting equitable access. Using a health equity lens, this scientific statement reviews the literature on telehealth for older adults with heart failure; provides an overview of structural, organizational, and personal barriers to telehealth; and presents novel interventions that pair telemedicine with in-person services to mitigate existing barriers and structural inequities.

Prediction models for heart failure in the community: A systematic review and meta-analysis

AIMS

Multivariable prediction models can be used to estimate risk of incident heart failure (HF) in the general population. A systematic review and meta-analysis was performed to determine the performance of models.

METHODS AND RESULTS

From inception to 3 November 2022 MEDLINE and EMBASE databases were searched for studies of multivariable models derived, validated and/or augmented for HF prediction in community-based cohorts. Discrimination measures for models with c-statistic data from ≥3 cohorts were pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using PROBAST. We included 36 studies with 59 prediction models. In meta-analysis, the Atherosclerosis Risk in Communities (ARIC) risk score (summary c-statistic 0.802, 95% confidence interval [CI] 0.707-0.883), GRaph-based Attention Model (GRAM; 0.791, 95% CI 0.677-0.885), Pooled Cohort equations to Prevent Heart Failure (PCP-HF) white men model (0.820, 95% CI 0.792-0.843), PCP-HF white women model (0.852, 95% CI 0.804-0.895), and REverse Time AttentIoN model (RETAIN; 0.839, 95% CI 0.748-0.916) had a statistically significant 95% PI and excellent discrimination performance. The ARIC risk score and PCP-HF models had significant summary discrimination among cohorts with a uniform prediction window. 77% of model results were at high risk of bias, certainty of evidence was low, and no model had a clinical impact study.

CONCLUSIONS

Prediction models for estimating risk of incident HF in the community demonstrate excellent discrimination performance. Their usefulness remains uncertain due to high risk of bias, low certainty of evidence, and absence of clinical effectiveness research.

The AHA/ACC/HFSA 2022 Heart Failure Guidelines: Changing the Focus to Heart Failure Prevention

The prevalence of heart failure (HF) in the United States (U.S.) is estimated at over 6 million adults, with the incidence continuing to increase. A large proportion of the U.S. population is also at risk of HF due to the high prevalence of established HF risk factors, such as hypertension, diabetes, and obesity. Many individuals have multiple risk factors, placing them at even higher risk. In addition, these risk factors disproportionately impact various racial and ethnic groups. Recognizing the rising health and economic burden of HF in the U.S., the 2022 American Heart Association / American College of Cardiology / Heart Failure Society of America (AHA/ACC/HFSA) Heart Failure Guideline placed a strong emphasis on prevention of HF. The purpose of this review is to highlight the role of both primary and secondary prevention in HF, as outlined by the recent guideline, and address the role of the preventive cardiology community in reducing the prevalence of HF in at-risk individuals.

Allostatic load and incident heart failure in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study

BACKGROUND

Allostatic load (AL) is the physiologic "wear and tear" on the body from stress. Yet, despite stress being implicated in the development heart failure (HF), it is unknown whether AL is associated with incident HF events.

METHODS

We examined 16,765 participants without HF at baseline from the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort. The main exposure was AL score quartile. AL was determined according to 11 physiologic parameters, whereby each parameter was assigned points (0-3) based on quartiles within the sample, and points were summed to create a total AL score ranging from 0-33. The outcome was incident HF event. We examined the association between AL quartile (Q1-Q4) and incident HF events using Cox proportional hazards models, adjusted for demographics, socioeconomic factors, and lifestyle.

RESULTS

The mean age was 64 ± 9.6 years, 61.5% were women, and 38.7% were Black participants. Over a median follow up of 11.4 years, we observed 750 incident HF events (635 HF hospitalizations and 115 HF deaths). Compared to the lowest AL quartile (Q1), the fully adjusted hazards of an incident HF event increased in a graded fashion: Q2 HR 1.49 95% CI 1.12-1.98; Q3 HR 2.47 95% CI 1.89-3.23; Q4 HR 4.28 95% CI 3.28-5.59. The HRs for incident HF event in the fully adjusted model that also adjusted for CAD were attenuated, but remained significant and increased in a similar, graded fashion by AL quartile. There was a significant age interaction (p-for-interaction < 0.001), whereby the associations were observed across each age stratum, but the HRs were highest among those aged < 65 years.

CONCLUSION

AL was associated with incident HF events, suggesting that AL could be an important risk factor and potential target for future interventions to prevent HF.

Disparities in Postdischarge Ambulatory Care Follow-Up Among Medicaid Beneficiaries With Diabetes, Hospitalized for Heart Failure

Background Ambulatory follow-up for all patients with heart failure (HF) is recommended within 7 to 14 days after hospital discharge to improve HF outcomes. We examined postdischarge ambulatory follow-up of patients with comorbid diabetes and HF from a low-income population in primary and specialty care. Methods and Results Adults with diabetes and first hospitalizations for HF, covered by Alabama Medicaid in 2010 to 2019, were included and the claims analyzed for ambulatory care use (any, primary care, cardiology, or endocrinology) within 60 days after discharge using restricted mean survival time regression and negative binomial regression. Among 9859 Medicaid-covered adults with diabetes and first hospitalization for HF (mean age, 53.7 years; SD, 9.2 years; 47.3% Black; 41.8% non-Hispanic White; 10.9% Hispanic/Other [Other included non-White Hispanic, American Indian, Pacific Islander and Asian adults]; 65.4% women, 34.6% men), 26.7% had an ambulatory visit within 0 to 7 days, 15.2% within 8 to 14 days, 31.3% within 15 to 60 days, and 26.8% had no visit; 71% saw a primary care physician and 12% a cardiology physician. Black and Hispanic/Other adults were less likely to have any postdischarge ambulatory visit (P<0.0001) or the visit was delayed (by 1.8 days, P=0.0006 and by 2.8 days, P=0.0016, respectively) and were less likely to see a primary care physician than non-Hispanic White adults (adjusted incidence rate ratio, 0.96 [95% CI, 0.91-1.00] and 0.91 [95% CI, 0.89-0.98]; respectively). Conclusions More than half of Medicaid-covered adults with diabetes and HF in Alabama did not receive guideline-concordant postdischarge care. Black and Hispanic/Other adults were less likely to receive recommended postdischarge care for comorbid diabetes and HF.

Diversity in U.S. Cardiovascular Trainees and Leadership Where we are and What the Future Holds

Cardiovascular (CV) outcomes can be improved with commonality between provider and patient regarding gender and race/ethnicity. Slow growth in CV care provider diversity is an obstacle for women and underrepresented groups. The hope for more equitable outcomes is unlikely to be realized unless trends change in selection of CV fellows and program directors (PDs). We investigate longitudinal trends of gender and racial/ethnic composition of CV FITs. De-identified demographic data were compiled in a descriptive cross-sectional study from AAMC of internal medicine (IM) residents and CV FITs from 2011 through 2021 to evaluate gender and race/ethnicity trends among CV trainees. Trends of CV fellows who later became program directors were analyzed. In the US between 2011 and 2021, 53% of IM residents were male while 40% female (7% unreported). Among CV FITs, 78% were male and 21% female. Races/ethnicities among CV FITs consisted of 36% non-Hispanic white, 28% non-Hispanic Asian, 5% Hispanic, 4%Black, and 25% were classified within other race/ethnicity categories. The proportion who became CV program directors followed similarly: 79% of PDs were male and 21% female. Demographic profiles for CV FITs have not significantly changed over the past decade despite increased diversity among IM residents. Efforts to improve diversity of CV FITs and PDs need to be analyzed. Slow growth of diversity in CV FITs is outpaced by rising patient diversity, leading to disparities in care and poorer CV outcomes for women and underrepresented minorities. Recruiting, training, and retaining diverse CV FITs is necessary.

Real-World Disparities in Remote Follow-Up of Cardiac Implantable Electronic Devices and Impact of the COVID-19 Pandemic: A Single-Center Experience

Background Remote monitoring (RM) of cardiac implantable electronic devices has been shown to improve cardiovascular morbidity and mortality. To date, no studies have investigated disparities in use and delivery of RM. This study was performed to investigate if racial and socioeconomic disparities are present in cardiac implantable electronic device RM. Methods and Results This was a retrospective observational cohort study at a single tertiary care center in the United States. Patients who received a newly implanted cardiac implantable electronic device or device upgrade between January 2017 and December 2020 were included. Patients were classified as RM positive (RM+) when they underwent at least ≥2 remote interrogations per year during follow-up. Of all eligible patients, 2520 patients were included, and 34% were women. The mean follow-up was 25 months. Mean age was 71±14 years. Pacemakers constituted 66% of implanted devices, whereas 26% were implantable cardioverter-defibrillators, and 8% were cardiac resynchronization therapy with implantable cardioverter-defibrillators. Most patients (83%) were of European American ancestry. During follow-up, 66% of patients were classified as RM+. Patients who were younger, European American, college-educated, lived in a county with higher median household income, and were active on the hospital's patient portals were more frequently RM+. In an adjusted regression model, RM+ remained associated with the use of the online patient portal (odds ratio [OR], 2.889 [95% CI, 2.387-3.497]), presence of an implantable cardioverter-defibrillator (OR, 1.489 [95% CI, 1.207-1.835]), advanced college degree (OR, 1.244 [95% CI, 1.014-1.527]), and lastly with European American ancestry (P<0.05). During the years of the COVID-19 pandemic, the number of RM+ patients increased, whereas the association with ancestry and ethnicity decreased. Conclusions Despite being offered to all patients at implantation, significant disparities were present in cardiovascular implantable electronic device RM in this cohort. Disparities were partly reversed during COVID-19. Further studies are needed to examine health center- and patient-specific factors to overcome these barriers, and to facilitate equal opportunities to participate in RM.

Do PRO Measures Function the Same Way for all Individuals With Heart Failure?

Women diagnosed with heart failure report worse quality of life than men on patient-reported outcome (PRO) measures. An inherent assumption of PRO measures in heart failure is that women and men interpret questions about quality of life the same way. If this is not the case, the risk then becomes that the PRO scores cannot be used for valid comparison or to combine outcomes by subgroups of the population. Inability to compare subgroups validly is a broad issue and has implications for clinical trials, and it also has specific and important implications for identifying and beginning to address health inequities. We describe this threat to validity (the psychometric term is differential item functioning), why it is so important in heart-failure outcomes, the research that has been conducted thus far in this area, the gaps that remain, and what we can do to avoid this threat to validity. PROs bring unique information to clinical decision making, and the validity of PRO measures is key to interpreting differences in heart failure outcomes.

Augmented Intelligence to Identify Patients With Advanced Heart Failure in an Integrated Health System

BACKGROUND

Timely referral for specialist evaluation in patients with advanced heart failure (HF) is a Class 1 recommendation. However, the transition from stage C HF to advanced or stage D HF often goes undetected in routine care, resulting in delayed referral and higher mortality rates.

OBJECTIVES

The authors sought to develop an augmented intelligence-enabled workflow using machine learning to identify patients with stage D HF and streamline referral.

METHODS

We extracted data on HF patients with encounters from January 1, 2007, to November 30, 2020, from a HF registry within a regional, integrated health system. We created an ensemble machine learning model to predict stage C or stage D HF and integrated the results within the electronic health record.

RESULTS

In a retrospective data set of 14,846 patients, the model had a good positive predictive value (60%) and low sensitivity (25%) for identifying stage D HF in a 100-person, physician-reviewed, holdout test set. During prospective implementation of the workflow from April 1, 2021, to February 15, 2022, 416 patients were reviewed by a clinical coordinator, with agreement between the model and the coordinator in 50.3% of stage D predictions. Twenty-four patients have been scheduled for evaluation in a HF clinic, 4 patients started an evaluation for advanced therapies, and 1 patient received a left ventricular assist device.

CONCLUSIONS

An augmented intelligence-enabled workflow was integrated into clinical operations to identify patients with advanced HF. Endeavors such as this require a multidisciplinary team with experience in design thinking, informatics, quality improvement, operations, and health information technology, as well as dedicated resources to monitor and improve performance over time.