Prioritizing the Primary Prevention of Heart Failure: Measuring, Modifying and Monitoring Risk

Abstract

With the rising incidence of heart failure (HF) and increasing burden of morbidity, mortality, and healthcare expenditures, primary prevention of HF targeting individuals in at-risk HF (Stage A) and pre-HF (Stage B) Stages has become increasingly important with the goal to decrease progression to symptomatic (Stage C) HF. Identification of risk based on traditional risk factors (e.g., cardiovascular health which can be assessed with the American Heart Association’s Life’s Essential 8 framework), adverse social determinants of health, inherited risk of cardiomyopathies, and identification of risk-enhancing factors, such as patients with viral disease, exposure to cardiotoxic chemotherapy, and history of adverse pregnancy outcomes should be the first step in evaluation for HF risk. Next, use of guideline-endorsed risk prediction tools such as Pooled Cohort Equations to Prevent Heart Failure provide quantification of absolute risk of HF based in traditional risk factors. Risk reduction through counseling on traditional risk factors is a core focus of implementation of prevention and may include the use of novel therapeutics that target specific pathways to reduce risk of HF, such as mineralocorticoid receptor agonists (e.g., fineronone), angiotensin-receptor/neprolysin inhibitors, and sodium glucose co-transporter-2 inhibitors. These interventions may be limited in at-risk populations who experience adverse social determinants and/or individuals who reside in rural areas. Thus, strategies like telemedicine may improve access to preventive care. Gaps in the current knowledge base for risk-based prevention of HF are highlighted to outline future research that may target approaches for risk assessment and risk-based prevention with the use of artificial intelligence, genomics-enhanced strategies, and pragmatic trials to develop a guideline-directed medical therapy approach to reduce risk among individuals with Stage A and Stage B HF.

INTRODUCTION

Epidemiology and the Cost of Heart Failure(HF)

In 2017, an estimated 64.3 million people were living with HF worldwide.^{1, 2} In the United States (US), the number of individuals with HF increased from approximately 6.0 million in 2015–2018 to 6.7 million in 2017–2020 among those over 20 years of age, according to data from the National Health and Nutrition Examination Survey. ³ The prevalence of HF is projected to increase such that, by 2030, more than 8 million people in the US, or 1 in every 33 Americans (about 3.0% of the population), will have HF. ⁴ This increasing prevalence is multifactorial and may be due to: (1) patients with HF surviving longer with the development and use of improved therapeutics, (2) an aging population who are at higher risk for developing HF, and (3) increasing prevalence of risk factors for HF such as obesity, hypertension (HTN), and diabetes mellitus (DM).^4–7 Although we await further data on post- COVID-19 pandemic HF mortality rates, pandemic and pre-pandemic HF data have demonstrated increasing rates in HF mortality, with widening disparities among young adult and Black patients.^8–10 The greatest economic burden related to HF results from hospitalizations and rehospitalizations, and pre-pandemic data have shown steadily rising hospitalizations for HF.¹¹ Additionally, the expected cost attributable to HF in the US is expected to increase, with total direct medical costs of HF reaching $53 billion in 2030—from $21 billion in 2012—and total costs including indirect costs reaching $70 billion in 2030—from $31 billion in 2012.⁴

Definition and Classification

Prevalent HF or Stage C HF is defined as a clinical syndrome with symptoms and/or signs of congestion.¹² Additionally, the 2022 ACC/AHA guidelines subcategorize Stage C/D HF by left ventricular (LV) ejection fraction (LVEF) into: HFpEF (HF with preserved LVEF or LVEF > 50%), HFmrEF (HF with mildly reduced LVEF or LVEF 41–49%), HFrEF (HF with reduced LVEF or LVEF <40%) and HFimpEF (HF with improved LVEF or baseline LVEF value of ≤ 40% with a ≥ 10% increase from baseline LVEF, and a second measurement of LVEF of > 40%).¹³ In response to a need to define at-risk patients, the 2013 AHA/ACC guidelines created a new system for staging HF: (1) stage A: patients at risk of HF but without clinical signs or symptoms of HF (2) stage B: patients without HF but with abnormal cardiac structure and function (3) stage C: patients with signs or symptoms of HF, and (4) stage D: patients with severe advanced signs or symptoms of HF refractory to therapy. ¹⁴ Therefore, rightly, a new focus emerged to address Stage A and B HF, which were previously not recognized on the HF continuum.

Why and How to Define the Spectrum of Prevention

Prevention in public health has been traditionally categorized into four levels: (1) Primordial prevention- preventing the development of risk factors, (2) Primary prevention- preventing the onset of disease, (3) Secondary prevention- preventing recurrence of disease-related events, and (4) Tertiary prevention- preventing the progression of clinical disease. ¹⁵ These categories line up well with the stages of HF with Stage A/B identified for primary prevention, Stage C focusing on secondary prevention, and Stage D on tertiary prevention.¹⁶ Current management efforts focus on secondary and tertiary prevention among patients with Stage C and D HF, where ultimately the target is not curative but rather remission, stabilization, or referral for advanced heart replacement therapies.¹⁶ Hence prioritizing the primary prevention of HF is needed for the vast population who qualify as Stage A or B HF.

There has been relative success in the wide adoption of risk-based prevention for atherosclerotic cardiovascular (CV) disease (CVD) that has yet to be standardized and adopted for HF in clinical practice.¹⁷ This paradigm has analogues in the process of risk management used widely in industrial and corporate fields that can inform future iterations of CVD prevention.^{18, 19} Although traditionally risk management is a separate entity from healthcare, there are shared goals around the central concept of diminishing the impact of an event versus avoiding those.^{18, 20} We will innovatively review how a risk management framework (Figure 1) can inform contemporary approaches to prevention in HF: (1) Identification of HF Risk Factors (factors and identifying high risk populations) (2) Assessment of HF Risk (quantification and stratification) (3) Decision-Making to Reduce HF Risk (treatments and interventions) (4) Implementation of guideline-directed medical therapy (GDMT) (healthcare access and delivery) (5) Monitoring of HF Risk (continual re-assessment of risk factors, qualification for therapies, and meeting guideline-directed benchmarks for risk factor management ).¹⁹

Figure 1.

An overall framework to approach prevention in HF is displayed and includes: (1) Identification of HF risk factors (traditional cardiovascular factors and risk-enhancing factors) (2) Assessment of HF Risk (absolute risk assessment) (3) Decision-making to reduce HF risk (treatments and interventions) (4) Implementation strategies of guideline-directed medical therapy (GDMT) for the prevention of HF (healthcare access and delivery) (5) Monitoring of HF risk (continual re-assessment of risk factor levels, eligibility for therapies, and meeting guideline-directed benchmarks for risk factor management).

RISK QUANTIFICATION AND STRATIFICATION

Risk Prediction

The heterogeneity of HF, lack of standardized definitions, and limited datasets with adjudicated HF outcome data has made an accurate risk prediction model difficult to develop and implement.²¹ Ultimately, most approaches at risk quantification have looked at biomarkers, electrocardiogram (ECG) findings, imaging, presence of traditional risk factors, demographics/social determinants of health (SDOH) and, less so, genetics (Figure 2). The initial approach was to focus on more traditional CVD risk factors and more readily available objective data.^{21, 22} One of the earliest attempts at a HF risk prediction score was the Framingham Heart Study (FHS) HF risk score, a 10-year risk model that used age, vital signs, history of DM or coronary artery disease (CAD), left ventricular hypertrophy (LVH) on ECG, and significant valvular disease on auscultation; however, the FHS HF risk score did not perform well in various other populations.^{21, 23} Other prediction scores similarly had variable performances in varied populations and led to conclusions that were already well-established with clear therapeutic implications, such as the association of CAD with high risk of HF.^{16, 21, 24} More recently, risk prediction models have been developed for the elderly, patients with HTN, patients with DM, and patients with chronic kidney disease (CKD).^{8, 25} One such score, the WATCH-DM score was developed to predict 5-year risk of incident HF among patients with type 2 diabetes mellitus (T2DM), developed using data from the ACCORD trial and externally validated ALLHAT group and other pooled DM community cohorts. The score includes body mass index (BMI), age, HTN, creatinine, HDL cholesterol, fasting plasma glucose (FPG), HbA1c, QRS duration, and the presence/absence of previous myocardial infarction (MI) or coronary artery bypass grafting (CABG). Segar et al studied the validity of the score among patients with known T2DM and no known history of HF based in trial populations such as, TECOS and Look AHEAD and an electronic health record (EHR) data from a single large health care system. They found that the WATCH-DM score was able to accurately predict HF development.²⁵

Figure 2.

Potential inputs for risk quantification and stratification are displayed based on current understanding of HF and existing risk prediction tools.

The 2022 ACC/AHA/HFSA Guideline for the Management of HF added a new Class 2a recommendation for consideration of validated multivariable scores such as the 2019 PCP-HF (Pooled Cohort equations to Prevent HF) score to estimate risk in general populations.¹³ The PCP-HF model provides race- and sex-specific 10-year risk equations from 7 community-based cohorts with at least 12 years of follow-up and subsequently validated in an EHR system and international cohorts.^{13, 24, 26, 27} Additionally, the first long-term risk prediction model, estimating 30-year risk of HF, was seen to have strong predictive ability in women and men with all C-statistics greater than 0.80. ²⁸

While it is possible that novel approaches to risk prediction with artificial intelligence (AI)-based machine learning algorithms could improve risk prediction, it is uncertain whether this approach is needed with limited risk factor variables as predictors where known associations are linear. Machine learning (ML) offers utility when input data needs to be (1) nonlinear (2) variable in quality and quantity between patients, and (3) adjusted for special populations.^{16, 22, 29, 30} One machine learning-derived model developed by Segar et al. included 39 candidate variables across demographic, anthropometric, medical history, laboratory, and ECG domains while incorporating race-specific contributors of HF and demonstrated superior performance to multiple traditional HF risk prediction models and overall showed a C-index of 0.8-.83 in Black individuals and 0.82 in White individuals.²⁹ Currently, most of the studies done in AI are not externally validated and, thus, are not yet endorsed in current guidelines. ^{13, 30}

Laboratory Biomarkers

In 2004, Wang et. al demonstrated that B-Type Natriuretic Peptide (BNP) and NT-proBNP >80th percentile predicted a higher risk of new-onset HF at 5 years in the general population.³¹ The positive results of trials like STOP-HF trial (>40 years with ≥ 1 risk factor or CVD comorbidity) and the PONTIAC trial (diabetic patients) using BNP- or NT-proBNP screening for HF led the 2022 ACC/AHA/HFSA HF guidelines to include a IIA recommendation of a natriuretic peptide (NP)-based screening in patients at risk for HF.^{13, 32–34} These data, among other studies, particularly in patients with diabetes suggest the use of NP-based screening in conjunction with risk factor-based to contextualize risk-based discussions as endorsed by the American Diabetes Association.^35–38 However, gaps remain in the guidelines on evidence-based therapeutic decisions following BNP screening in the absence of LV systolic dysfunction.

Studies have also demonstrated that higher troponin concentrations may be associated with future risk of HF, and serial measurements may further improve risk classification.³⁹ This association was also recently confirmed among a study of 2423 Black adults, which followed participants in the Jackson Heart Study between 2000–2004 without any history of CVD or HF before their follow-up visits from 2005–2008. Incident hs-cTnI elevation that had improved, stabilized, or worsened over time was associated with higher HF risk. Additionally, higher hs-cTnI levels at follow-up was associated with a higher burden of CVD risk factors, such as HTN, DM, and higher BMI.⁴⁰

Possibly more relevant with the data supporting CV benefits of finerenone that may be driven by improvement in HF risk, albuminuria has also been found in multiple trials, such as RENAAL (posthoc analysis), Framingham Heart Study, and Multi-Ethnic Study of Atherosclerosis (MESA), to be associated with incident HF in high-risk individuals.⁴¹ In an analysis of the Atherosclerosis Risk In Communities (ARIC) study, greater degree of albuminuria had a progressively higher risk of developing HF.^{41, 42} The mechanism or association through which albuminuria contributes to risk of HF is hypothesized to be related to endothelial dysfunction. ⁴¹ However, whether reducing albuminuria also reduces HF risk still requires further independent study, especially in the context of newer drugs.

In the PREVEND study, 13 biomarkers were studied and among those NT-proBNP, midregional proANP, hs-TnT, cystatin-C, and urinary albumin excretion were predictive for new-onset HF.⁴³ Multiple studies have looked at multi-marker only risk scores and have demonstrated an association with HF risk.^{36, 44} In looking specifically at risk in developing HFpEF or HFrEF, initial studies found NPs, urine albumin to creatinine ratio (UACR), hs-troponin, cystatin C, D-dimer and CRP predicted incident HFrEF but only NPs and UACR were associated with HFpEF.⁴⁵ A sex-specific study of multiple biomarkers (NPs, cardiac troponins, plasminogen activator inhibitor-1, D-dimer, fibrinogen, CRP, sST2, galectin-3, cystatin-C, and UACR) found limited utility in risk prediction models of including subtle sex-related differences in markers and follows that these biomarkers can be used similarly for both sexes in models.⁴⁶ Ultimately, however, biomarkers may have limited incremental utility above and beyond traditional risk factors in the general population for risk stratification.

ECG and Cardiac Imaging

The ECG offers a possible early window into risk of HF development. The initial Framingham study documented that for each standard deviation greater log-QRS duration there is a 23% increase in HF risk, and incomplete and complete bundle branch block were associated with a 1.4-fold and 1.7-fold risk of HF, respectively. ⁴⁷ QRS duration has also been integrated into HF risk scores, such as the PCP-HF.^{16, 24} More recently, Akbilgic et al. created an ECG AI model to predict HF using deep residual convolutional neural network (CNN) and with the CNN just using ECG achieved an (area under the curve) AUC of 0.756.⁴⁸ When combined with demographic data, smoking status, history of CAD and DM, and vitals, the CNN reached an AUC of 0.818.⁴⁸

Cardiac MRI measured LVH also has strong predictive utility with a C-index of 0.871 in comparison to ECG measured LVH C-index 0.860 for HF. ⁴⁹ However, cardiac MRI would not be cost effective for broad-based population screening. Echocardiography variables, which are widely available, are also associated with risk for HF, including global longitudinal strain (GLS)^{50, 51}, fractional shortening at the endocardium and midwall,⁵² peak Doppler peak E,⁵² doppler E/A ratios⁵², left atrial(LA) enlargement (LAE)⁵¹, LVH^{51, 53}, and E/e’⁵¹. It is possible that echocardiography, in conjunction with biomarkers and traditional risk factors, may inform HF risk assessment but further study is needed to determine who should be screened with echocardiography. ^{13, 54}

Genetics

Genetics play an important role in prevention of HF that is due to dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM). DCM encompasses heart disease with a dilated LV cavity not due to ischemia, valvular disease, or increased loading.⁵⁵ By contrast, HCM comprises a thickened left ventricular wall, sometimes with concomitant LV outflow tract obstruction.⁵⁶ For DCM, genetic testing yields a pathogenic variant (i.e., a positive result) in approximately 25–40% of individuals with a positive family history and in about 10–30% of individuals with no family history.^{13, 57} For HCM, genetic testing identifies positive results in about 30–60% of affected individuals.⁵⁶ Genetics becomes relevant for family members of an individual with an inherited cardiomyopathy that could benefit from screening for genotype and phenotype status, which is a guideline recommendation for all first-degree family members.¹³ Importantly, genotype + individuals even in the absence of any overt phenotype are classified as Stage A, and first-degree family members of individuals with gene-elusive DCM and HCM should undergo regular clinical screening for phenotype. HF may also occur due to the cumulative effects of multiple variants in commonly occurring variants (single nucleotide polymorphisms) as has been demonstrated in many other chronic diseases, though polygenic risk scores for HF are not yet part of regular clinical practice.¹³ Furthermore, individuals with more severe DCM were more likely to carry pathogenic variants, which underscores the importance of genetic testing in first-degree relatives to assess DCM genetic risk and more aggressively treat other risk factors (HTN, DM, sleep apnea) in gene-positive/phenotype negative relatives for HF prevention.⁵⁸

Although ongoing surveillance is currently the standard of care in genotype +/ phenotype - family members, the Valsartan for Attenuating Disease Evolution in Early Sarcomeric Hypertrophic Cardiomyopathy (VANISH) trial evaluated the potential for valsartan to modify disease progression in these individuals and found in two years significant differences between LV wall thickness, mass and volumes, LA volume, tissue Doppler diastolic and systolic velocities, and serum biomarkers.^{59, 60} Importantly, truncating variants of the titin gene TTN, a known cause of familial DCM, has also been implicated as a “second-hit” in certain cardiomyopathy syndromes, such as alcohol-related cardiomyopathy, chemotherapy-associated cardiomyopathy, and peripartum cardiomyopathies. These truncating TTN variants are found in 1–2% of the general population and supports the hypothesis that healthy individuals with TTN truncating variants may be more likely to develop HF under conditions of stress.^{60, 61} This represents a key area for ongoing research in how to integrate rare variants into population-wide risk stratification. As more individuals receive genetic information from studies such as All of Us or commercial direct-to-consumer testing like Color, this may become increasingly relevant in risk prediction and prevention algorithms, but future research is needed.

CV HEALTH

Why Lifestyle?

The AHA Life’s Essential 8 was updated in 2022 from the Life’s Simple 7 (LS7) metric. LS7 was developed by the AHA Goals and Metrics Committee of the Strategic Planning Task Force in 2010. In 2022, sleep was added as an influential factor affecting heart health. These metrics were designed as a way for the general population to follow a heart-healthy lifestyle with easily digestible recommendations based on decades of studies showing significant morbidity and mortality benefits to CV health. The MESA cohort demonstrated a significant association between the LS7 and incident HF.⁶² The LS7 was also studied in the European Prospective Investigation into Cancer and Nutrition-Netherlands (EPIC-NL) cohort and found lower HF risk among those with ideal lifestyle scores (55%), or intermediate lifestyle scores (47%), compared to poor scores. Thus, even modest improvements in lifestyle may lower HF incidence. It also showed that clusters of two to three LS7 components, specifically glucose, BMI, smoking, and blood pressure, could reduce HF risk more than 1 component alone.⁶³

The Cardiovascular Health study examined the association between lifestyle risk factors and incident HF. It found that moderate alcohol use, physical activity (PA), not smoking, and avoiding obesity later in life were each independently associated with a lower risk of incident HF. Having 4 or more healthy lifestyle factors reduced relative risk of HF by >50%. After adjusting for demographic and lifestyle variables, sodium intake in the top quintile of consumption was associated with 19% higher risk of HF.⁶⁴ Similarly, the Physicians Health Study assessed body weight, smoking status, exercise, alcohol intake, consumption of breakfast cereal, and consumption of fruits and vegetables on HF incidence. Lifetime risk of developing HF was 1 in 5 in men adhering to no desirable lifestyle characteristics, compared with 1 in 10 in those adhering to 4 or more factors. A major limitation of this study was it included only male physicians, excluding females and selecting for individuals with higher health literacy, thereby limiting its application to the general population.⁶⁵

Barriers to Successful Implementation of Lifestyle Modifications

Lifestyle modification is well known and arguably, the most important, pressing and core component of HF risk reduction (and broadly CVD and chronic disease) (Figure 3). However, implementation of these changes – especially in populations with adverse social factors that represent structural and systemic barriers (e.g. low health literacy, low socioeconomic status, limited access to healthy food and green spaces)– is the crux of the challenge at both a population-level and with each patient seen in clinic. Whether a patient may be able to engage in various lifestyle recommendations depends on numerous factors including culture, attitude, psychosocial and life stress, and suboptimal support systems. Approaches such as motivational interviewing help overcome the activation energy needed to implement healthy lifelong habits. The 5 A’s approach (assess risk behaviors, advise, change, assist with treatment, agree on goals/ action plan, arrange follow-up) has been proven to improve certain health behaviors including smoking cessation, PA, and dietary choices.⁶⁶

Figure 3.

There are several known risk factors and risk-enhancing factors for the development of HF. At the core of HF prevention (inner ring) is a focus on cardiovascular health promotion through the AHA Life’s Essential 8 framework. The middle ring shows selected risk enhancers and/or risk enriched populations. Finally, the outer ring depicts the background of an individual’s lived experiences (e.g., social determinants of health) that influence risk.

Dietary Quality

The Mediterranean diet (MedDiet) has been recommended as the ideal heart-healthy diet to prevent CVD. It was first described as the traditional eating pattern in populations living in the Mediterranean region during the 1950’s, which at that time involved very low consumption of red meats, butter, whole-fat daily products and rich in locally grown, minimally processed produce.⁶⁷ The benefits of the MedDiet were first described in 1951 by Ancel Keys in the 7 Countries Study. Keys noted that a high monounsaturated fatty acids( MUFA) to saturated fatty acid (SFA) ratio appeared to offer favorable CVD outcomes. When discussing prevention of HF, higher adherence to the MedDiet has also been associated with lower relative risk specifically of HF.⁶⁸ A systematic review investigated the protective effect of certain dietary patterns on primary prevention of HF and demonstrated lower sodium intake was associated with lower risk of HF in individuals with underlying CAD.⁶⁹

PA

The AHA recommends 150 minutes of moderate intensity aerobic physical activity per week and 2 days a week of muscle-strengthening exercises, with an additional recommendation to include balance exercises after age 65 years. Secondary analysis from the Cooper Center Longitudinal Study showed that improvements in physical fitness over time in individuals with low baseline fitness was associated with a lower risk of incident HF, where a 1-metabolic equivalent of task (MET) improvement in fitness was associated with 17% decreased HF.⁷⁰

Tobacco Use and Exposure

Tobacco use is a well-known risk factor associated with CVD and remains the leading cause of preventable CVD-associated mortality in the US. Several studies have shown that there is a direct relationship between tobacco use and development of incident HF, even after controlling for CAD. Individuals who currently smoke, regardless of smoking intensity or lifetime burden, have a higher incident rate of HF compared with never-smokers.^{71, 72} In fact, a recent study using data from ARIC study, found that cigarette smoking showed a robust dose-response relationship with incident HF (HFpEF and HFrEF) and that a longer duration of smoking cessation was associated with a lower risk for HF overtime, with return to general population risk in > 30 years of cessation.⁷³

Sleep

Optimizing sleep for those with prevalent HF has been studied in great detail.^{74, 75} Shorter sleep duration (<5–6 hours / night) and poor sleep quality have been associated with high blood pressure, which is a major risk factor for HF development.⁷⁶ Prolonged sleep (>8–9 hours) was also associated with greater CVD risk in a meta-analysis.⁷⁷ However, neither of these findings distinguished overall CVD risk from risk of developing HF. A large RCT randomized over 2700 individuals with moderate to severe obstructive sleep apnea (OSA) and CAD and looked at whether implementation of continuous positive airway pressure (CPAP), and therefore tackling sleep-disordered breathing, reduced death from CVD as well as other secondary outcomes.⁷⁸ While there was no mortality benefit in CPAP implementation, there was an improvement in quality of life and daytime sleepiness.⁷⁸ Therefore, it is unclear if CPAP initiation would reduce risk of HF. However, detection and treatment of sleep disordered breathing is recommended for benefits related to sleep disturbance, and sleep as a ‘biomarker for HF risk’ may be utilized in the future.

Weight Management

A BMI above 25 kg/m² has been demonstrated to be associated with higher risk of developing HF. The pathophysiology of obesity-related HF risk involves insulin resistance, increased oxygen demand from skeletal muscle, adipocyte dysfunction, and ultimately an altered metabolic profile. Additionally, obesity hypoventilation syndrome, OSA, and decreased peripheral vascular resistance leads to increased LV filling pressures, hypoxia, and increased pulmonary artery pressures which also are associated with higher HF risk.⁷⁹ Decreasing weight in a healthy and sustainable manner decreases LV mass, lowers arterial pressures, decreases left and right sided filling pressures, decreases O2 demand, overall improving the hemodynamic profile and preventing cardiomyopathy. Data from the Framingham Heart study showed that increased BMI was associated with an increased risk of HF such that for every 1 kg/m² increment in baseline BMI, there was a 5–7% increased risk of HF development over a mean follow up of 14 years, even after adjustment for established risk factors.⁸⁰ Though commonly used, BMI is a crude measure of adiposity. Visceral adipose fat (VAT) and waist circumference (WC) have been shown to better predict cardiometabolic risk compared with BMI alone. Thus, when analyzing those with obesity, low VAT and WC can place patients within a ‘favorable obesity’ category. Overall, improving cardiometabolic risk and decreasing overall BMI, WC, and VAT continues to prevail as ideal weight management to prevent HF.

Cholesterol

It is well known that lowering low-density lipoprotein cholesterol (LDL-C), total cholesterol, and triglycerides while optimizing high-density lipoproteins confers lower risk of CAD. HMG-CoA reductase inhibitors (statins) have been the mainstay of cholesterol lowering medications.⁷⁶ Statins stabilize atheromas, prevent further plaque development, and allow for reduction in atheroma size ultimately leading to less ischemic cardiomyopathy leading to HF. The WOSCOPS study determined statin use decreased risk of incident HF. Post-hoc analysis of WOSCOPS showed that statin use was associated with lower levels high-sensitivity troponin, suggesting decreased subclinical cardiac ischemia and damage.⁸¹ Additionally, a large Danish study showed that non-fasting hypertriglyceridemia had a greater association with incident HF than LDL-C.⁸² Screening lipid profiles should continue to be standard of care for all adults per AHA prevention guidelines.⁷⁶

Blood Sugar Management

Diabetes is a well-known risk factor for CVD. Several mechanisms explain the increased risk of HF in people with DM. Diabetic cardiomyopathy is the development of microangiopathy, myocardial fibrosis and autonomic neuropathy.⁸³ Since cardiomyocytes are unable to effectively store lipids, the excess circulating glucose and free fatty acids cause direct myocardial damage and lipotoxicity, leading to mitochondrial edema and damage, as well as myofibril damage, which can lead to HF. ^{84, 85} Prediabetes and fasting blood glucose have been associated with higher risk of incident HF, with significant sex and racial disparities.⁸⁶ A recent systematic review and meta-analysis showed that diabetes was associated with a 2-fold increased risk of developing HF in the general population.⁸⁴

Blood Pressure (BP)

Current ACC/AHA BP guidelines define optimal systolic BP as <120 mmHg and diastolic BP as <80 mmHg in all adults with treatment for those individuals at high risk for CVD to threshold <130//80 mmHg.^{87, 88} Post-hoc analysis of the Framingham Heart Study showed that hypertensive individuals had a 2 to 3-fold increased risk of HF development.⁸⁹ In a 2021 meta-analysis by The Blood Pressure Lowering Treatment Trialists’ Collaboration, for patients with HTN with both prior and no prior CVD, a 5 mm Hg systolic BP reduction reduced the risk of HF development [no prior CVD- Hazard Ratio (HR) 0.83 (0.77–0.89); with prior CVD- HR 0.89 (0.83–0.95)].⁹⁰ Post-hoc analysis of the SPRINT trial showed that intensive BP lowering (goal SBP <120 mmHg) in patients with high predicted risk of HF at baseline (based on the PCP-HF score) had significantly higher HF-free survival as opposed to those receiving standard BP lowering (goal SBP 130–139 mmHg).⁸⁷ This suggests that baseline HF risk stratification can inform whom efforts to intensify BP lowering may have the greatest benefit. Selecting the ideal anti-hypertensive agent to achieve sustainable BP lowering is the next challenge faced by clinicians. A Bayesian meta-analysis compared various anti-hypertensive agents with their effect on HF prevention. The study found that diuretics, angiotensin receptor blockers (ARBs), and angiotensin converting enzyme inhibitors (ACE-I) were superior to placebo, calcium channel blockers, beta-blockers (BB) and alpha-blockers. Diuretics were more effective at preventing HF compared to ARBs and ACE-I.⁹¹

SELECT RISK ENHANCED POPULATIONS

There are several conditions that confer greater risk for HF. Herein we review selected risk enhancing factors for HF.

Viral

COVID-19

In a retrospective study of more than 42 million records between January 2019 and March 2022, patients who tested positive for COVID-19 had a HR of 2.3 (2.2–2.4) for developing HF compared to those who tested negative and had no symptoms..⁹² COVID-19 was also associated with an increased risk for HF hospitalization and in-hospital death.⁹³ Myocardial injury occurred in at least 10% of unselected COVID-19 cases and up to 41% in critically ill patients or in those with concomitant CVD comorbidities, which is similar to rates associated with influenza infection suggesting that the viral illness is the stressor and is not unique to COVID-19.^{94, 95} Overall, only few cases of COVID-19-related acute myocarditis led to severe reduction in the left ventricular ejection fraction.⁹⁴ In prevention of COVID-19, the mRNA COVID-19 vaccines became mainstay as proven in reducing the incidence, disease severity and systemic complications of COVID-19 infections. However, the COVID-19 mRNA vaccines were also associated myocarditis and pericarditis.⁹⁶ Nonetheless, the incidence, severity, hospital stay length, and mortality of COVID-19 mRNA vaccine myocarditis is less in comparison to COVID-19 myocarditis and ultimately, a systematic review found that the risk of myocarditis is 7 times more in COVID-19 patients as compared to a vaccination group.^{96, 97} Hence, overall, the benefits outweigh the risks in prevention of HF with the COVID-19 mRNA vaccines.

HIV

With the discovery of antiretroviral therapy (ART) for people living with HIV, life expectancy has improved leaving CVD, specifically HF, now as a major health complication among HIV-infected individuals.^{98, 99} People living with HIV are at increased risk of all types of HF: HFpEF, HFmrEF, and HFrEF when compared with uninfected individuals.⁹⁹ When assessing risk, a systematic review and meta-analysis found that both general population and HIV-specific CVD risk models had a general tendency to underpredict risk among people living with HIV.¹⁰⁰ This supports current recommendations in the ACC/AHA guidelines to consider HIV as a risk-enhancing factor.¹³ Ultimately, from a public health standpoint, there is a need for increasing awareness for the more modern comorbidities of an aging HIV population and, from a research standpoint, a need for more standardization of HF screening and risk estimation in this group of individuals.¹⁰¹

Cardio-Oncology

Recently, Larsa et al. provided new long-term data incidence of anthracycline related HF and identified higher cumulative incidence of HF for patients treated with anthracyclines at 1 year, 5 years, 10 years, 15 years and 20 years even after adjusting for baseline characteristics, including age, sex, DM, HTN, CAD, hyperlipidemia, obesity, and tobacco use.¹⁰² With such significant long-term CV toxicity seen with certain therapies, the 2022 European Society of Cardiology (ESC) Guidelines on Cardio-Oncology have created strong emphasis on initial risk stratification and preventative measures prior to starting therapy and then routine screening throughout therapy.¹⁰³ By using standard screening tools such as a readily available calculator for the HFA-ICOS risk assessment tool, BNP/troponin, ECG and if needed echocardiography, oncologists are able to risk stratify patients in conjunction with counseling on risk associated with proposed therapies.¹⁰³ Cardiology referral and discussion of risk/benefit with patient of treatment is a Class I recommendation in high-risk and very high-risk patients before anticancer therapy. ¹⁰³ The priority of prevention of cancer therapy-related CV toxicity is a re-focus on traditional CVD prevention strategies used in general population (Class I recommendation). However, as a class IIA recommendation, ACE-I/ARB and BBs should be considered for primary prevention in high- and very high-risk patients receiving anthracyclines or anti-HER2 therapies.¹⁰³ Medication directed prevention recommendation is based on multiple studies that have shown modest effects on reduction of LV dysfunction (often <5% between groups) but often no meaningful overall reduction in the incidence of HF even in the short term.^{13, 104} A meta-analysis looking at 15 of these RCTs showed that ACE-I/ARB or BB therapy had a statistically significant higher LVEF in the treatment group during anthracycline therapy (χ2 = 155.43, I2 = 91.0%, p = 0.000) and during trastuzumab therapy (χ2 = 19.56, I2 = 69.3%, p = 0.003) but this study also supported higher quality future RCTs in the setting of moderate to high heterogeneity. ¹⁰⁵ Here we have only summarized a limited scope of the components of prevention of HF for patients undergoing anticancer therapy. Currently, there are multiple ongoing studies to look at various other BBs and ACE-I/ARB, ivabradine, statins, and angiotensin receptor/neprilysin inhibitors (ARNI), which will continue to push the field of cardio-oncology into preventive approaches in CVD managment.¹⁰⁴

CONSIDERATIONS FOR HF RISK

Sex-Based Differences

The incidence of HF in women is increasing, particularly diagnoses of HFpEF.^{3, 106} Although less than men, 63% of women with HFrEF have CAD and ischemic cardiomyopathy.¹⁰⁷ HTN is the most common CVD risk factor in women, yet, despite receiving treatment, women are less likely to achieve blood pressure control compared with men (44.8% vs. 51.1%).¹⁰⁸ This lack of BP control can worsen with age, such that when measured in 100,000 postmenopausal women from 1994–1998, only 29% of women aged older than 70 years have adequate BP control.¹⁰⁹ Similarly, when compared with men, women with DM are less likely to have a hemoglobin HbA1c of <7%.¹¹⁰ For example, in a study of exenatide [a glucagon-like peptide-1(GLP-1) receptor agonist], after a 1-year follow-up, the proportion of patients with a target HbA1c of < 7% was lower in women than in men.¹¹¹ Although sodium-glucose cotransporter-2 inhibitors (SGLT2-i) are not as effective in A1c lowering, they do provide cardioprotective properties in patients with DM and one study found females had lower odds of filling their SGLT2i prescription.¹¹² Along with traditional risk factors, complications of pregnancy (hypertensive disorders of pregnancy, preterm birth, small-for-gestational age infant, gestational diabetes) and hormonal changes associated with menopause also represent sex-specific risk factors that require increasing awareness among clinicians.^{113, 114}

Despite the theorized benefit of estrogen in CV protection, studies have failed to demonstrate a protective effect with supplemental estrogen therapy for primary or secondary CVD prevention.¹⁰⁶ A study in the Women’s Health Initiative found that HTN and obesity accounted for approximately two-thirds of the attributable risk for HFpEF in women.¹¹⁵ In line with 2022 Call to Action for Cardiovascular Disease in Women from the AHA, there needs to be a focus closing the gap in preventive health outcomes between men and women as well as special consideration in research in closing gaps in knowledge and care delivery for women.¹¹⁶

SDOH

SDOH encompass the social, demographic, economic, and environmental domains that affect a person’s health.¹⁶ In particular, it is important to highlight that race and ethnicity are social constructs. This has been particularly important in the US in regard to Black Americans, as Black Americans are disproportionately affected by HF and bear a higher prevalence at an earlier age.^{117, 118} This higher incidence is attributed, in part, to greater risk for obesity, DM, and HTN.¹¹⁷ Systemic racism greatly influences the health of patients. For example, a recent study found that Black patients living in zip codes exposed to historical redlining practices experience a higher risk of HF than those living in non-redlined areas.¹¹⁹ In addition, lower socioeconomic standing, independent of traditional risk factors, is associated with higher risk of HF. ¹²⁰ Both short-term and long-term exposure to air pollution is also associated with incident HF.^{121, 122} In a meta-analysis, both short- and long-term exposure to particulate matter with an aerodynamic diameter ≤2.5μm (PM_2.5), PM₁₀ and nitrogen dioxide (NO₂) were significantly associated with higher risk of HF.¹²¹ Sulfur Dioxide (SO₂) and carbon monoxide (CO) only had associations with HF with short-term exposure and ozone (O3) had no association with increased risk.¹²¹ When discussing population-wide prevention, the data are compelling that SDOH should not only just play in individual discussions with patients but systematically be considered, as supported by the AHA guidelines and emerging CMS recommendations.¹³

THE ROLE OF THERAPEUTICS

There are several therapeutics proposed to decrease risk for HF. Herein we review selected therapeutics and the relevant studies in support of their use (Table 1).

Table 1.

Summary of potential classes of therapies for the primary prevention of HF

Treatment Class	Type of study	Trial Name/Population studied	Interventions	Comparators	Outcomes	Timing	Authors
Anti hyperglycemic agents	Nested case-control studies	336,334 individuals with type 2 diabetes without history of CVD,	The primary composite end point was the first record of MACCE after cohort entry. The secondary end point was the first record of HF after cohort entry.	Combined SGLT2i and GLP-1RA regimens, GLP-1RA regimens without SGLT2i agents, SGLT2i regimens without GLP- 1RA agents, other combination regimens excluding GLP-1RA and SGLT2i agents, other monotherapy regimens, or no exposure to antidiabetic medications.	Combined GLP-1RA and SLGT2i regimens conferred a lower OR [95% CI] of incident HF (0.24 [0.12,0.81) compared to SGLT2 alone (0.51 [0.25,0.96]) or GLP-1RA alone (0.76 [0.40,1.46]).	January 1998 – July 2018	Wright AK, et al.
Thiazide diuretics	Multivariate post-hoc analysis	2,847 nondiabetic patients receiving intensive blood pressure treatment in the SPRINT study	The primary outcome was composite end point of MI, ACS not resulting in MI, stroke, ADHF, or cardiovascular death. Secondary outcomes included a major adverse HF.	Patients assigned to intensive blood pressure treatment strategy and receiving at least one of the following at baseline: thiazides, ACE-inhibitors, ARBs, CCB, (β-blockers, alphablockers, or loop diuretics.	Risk of heart failure was lower in groups taking thiazides (HR 0.19 [95% CI 0.04 – 0.87] p=0.03) compared to other anti-hypertensive agents.	November 2010 – March 2013	Tsujimoto, T, & Kajio, H.
Angiotensin converting enzyme inhibitors (ACEi)	Double-blind placebo controlled randomized trial	HOPE (Heart Outcomes Prevention Evaluation) Trial; 9,297 individuals with hx of CAD, CVA, PAD, diabetes, or other CV risk factors	The impact of ramipril vs. placebo to prevent heart failure in patients at high risk of cardiovascular events, and the magnitude of benefit in relevant subgroups.	Ramipril vs. Placebo (vitamin E)	Ramipril had a lower rate of new-onset HF compared with placebo (11.5% vs. 9.0%, RR 0.77, 95% CI 0.68–0.87, p<0.0001).	December 1993 – June 1995	Arnold, J. M. O., et al.
Angiontensin-receptor/Neprolysin inhibitor (ARNI)	Prospective, double-blind, double-dummy, randomized clinical trial	PARABLE (Personalized Prospective Comparison of ARNI With ARB in Patients With Natriuretic Peptide Elevation); 250 individuals with symptomatic hypertension and/or T2DM	Maximal left atrial volume index and left ventricular end diastolic volume index, ambulatory pulse pressure, N-terminal pro-BNP, and adverse cardiovascular events.	Sacubitril/valsartan vs. Valsartan	Several markers of heart failure were reduced in those taking sacubitril/valsartan compared to valsartan alone, including left atrial volume, changes in pulse pressure and N-terminal pro-BNP.	April 2015 – June 2021	Ledwidge M, et al.
Spironolactone	Randomized control trial	349 individuals with baseline obesity, T2DM, or hypertension and no heart failure at baseline.	Patients were block randomized 1:1 to intervention or usual care. In the intervention arm, identification of subclinical LVD triggered initiation of spironolactone.	Echocardiography-guided therapy vs. Usual care	Underpowered study. However, per-protocol analysis showed resolution of left-ventricular dysfunction in patients who received therapy versus usual care (59% vs. 33%,p=0.01).	April 2017 – June 2019	Potter E, et al.
Finerenone	Subanalysis of the Finerenone in Chronic Kidney Disease and Type 2 Diabetes: Combined FIDELIO-DKD and FIGARO-DKD Trial Programme Analysis (FIDELITY) randomized clinical trial	13,026 individuals with CKD, type 2 diabetes, estimated glomerular filtration rate greater than 25, and moderately to severely increased albuminuria.	Combined data from (FIDELITY), a pooled analysis of 2 phase 3 trials, with National Health and Nutrition Examination Survey (NHANES) data to simulate the number of composite cardiovascular events that may be prevented per year with finerenone at a population level.	Finerenone vs. Placebo and NHANES data	Implementation of finerenone in this population was estimated to prevent approximately 14,000 hospitalizations for HF annually.	September 2015 through October 2018 Over 4 years of consecutive National Health and Nutrition Examination Survey data cycles (2015–2016 and 2017–2018)	Agarwal, R., et al.
	Randomized control trial	7,352 individuals with T2DM and CKD without HF diagnosed at baseline	Time to new-onset HF, composite time to cardiovascular death or first HHF; a composite of time to HF-related death or first HHF; time to first HHF; a composite of time to cardiovascular death or total (first or recurrent HHF; a composite of time to HF-related death or total HHF; and time to total HHF.	Finerenone vs. Placebo	New-onset HF was significantly reduced with finerenone versus placebo (HR 0.68, p=0.0162).	September 2015 – February 2021	Filippatos G, et al.

Anti-Hyperglycemic Agents

SGLT2-i are drugs that lower blood glucose by increasing urinary glucose excretion and decreasing systemic glucose absorption. First designed as a diabetes medication, cardioprotective and kidney-specific benefits were later discovered. SGLT2-i are now a cornerstone of GDMT for patients with prevalent HF across the entire spectrum of LVEF, those with diabetes, and CKD. GLP- 1 receptor antagonists (GLP-1RAs) may also be used in combination with an SGLT2-i to prevent CVD, including HF. A group in the UK conducted nested case-control studies looking at the incidence of major adverse cardiac or cerebrovascular events in patients with T2DM treated with non-insulin medications.¹²³ The team found that SGLT2-i or a GLP-1RA conferred lower odds of incident HF compared to other DM medication combinations, and the SLGT2i/GLP-1RA combination was associated with 57% lower odds of HF, while both agents alone had higher odds of HF in comparison (51% lower odds with SGLT2-i alone, 18% lower odds with GLP-1RA alone). Most data surrounding the use of GLP-1RA agents as primary prevention of CVD is underpowered and sparse. While SGLT2-i will remain a cornerstone of HF therapy and prevention, new combination therapy with agents such as GLP-1RA could be on the horizon.

Thiazide Diuretics

Thiazide diuretics are frequently used to treat HTN. Tsujimoto et al analyzed data from the SPRINT trial in order to distinguish between certain antihypertensive medications’ effect on preventing CVD, particularly HF. The team found that when thiazide diuretics were used as part of aggressive BP management, there was a significantly lower risk of HF in those patients.¹²⁴ When studying people with T2DM, those who received thiazides for HTN management have been shown to have a 21% lower risk for HF.¹²⁵ Therefore, the argument could be made that diuretics, in particular thiazides, should be used as first-line HTN management in order to prevent HF.

ACE-I

The HOPE trial (2003) was a landmark study which randomized patients with CVD risk factors but no known HF to receiving ramipril vs. placebo. The study found that ramipril had a lower rate of new-onset HF compared with placebo (11.5% vs. 9.0%, RR 0.77), with a greater effect seen in those with systolic BP > 139 mmHg and those with an interim MI.¹²⁶ Since the HOPE trial, the importance of HTN management has been paramount in preventing HF in at-risk patients.

ARNI

Sacubitril/valsartan is a relatively new medication (e.g., ARNI) which has been shown to have many beneficial properties for treating HF and is now on a cornerstone of GDMT for HFrEF and HF with less than normal LVEF. The utility of sacubitril/valsartan to prevent HF seems to lie largely in its ability to suppress or reverse age-related physiologic changes related to HTN. It is well known that aging increases arterial stiffness, decreases glomerular filtration rate (GFR), and increases salt sensitivity which together contributes to systolic HTN with a wide pulse pressure, as well as salt-sensitive HTN which ultimately leads to HF. The PARAMETER study showed that sacubitril/valsartan decreased NT-proBNP levels more than ARBs, showing their utility in preventing volume-overload states which could precede HF.¹²⁷

Currently there are no guideline directed medical therapies for Stage B HF that dictate treatment for this population, other than general recommendations to manage risk factors. Recently, the PARABLE trial randomized patients to either sacubitril/valsartan or valsartan and evaluated LV end diastolic volume indexes, ambulatory pulse-pressures, N-terminal pro-BNP, and adverse CVD events. The study ultimately showed that sacubitril-valsartan decreased many markers of pre-HF and could be considered as preventative treatment in patients with pre-HF.¹²⁸ Treating stage B HF would require new screening guidelines to identify patients with pre-HF because current medical practice involves further cardiac evaluation after patients demonstrate symptoms. For example, the AHA/ACC would have to recommend screening echocardiography and BNP levels for patients who are high risk of developing HF. Risk stratification would have to be better delineated to reduce the risk of unnecessary testing and increased healthcare spending.

Spironolactone

Spironolactone is a mineralocorticoid-receptor antagonist (MRA) which affects CV pathophysiology by several mechanisms. Not only does it reduce BP, but it also has an anti-fibrotic effect by affecting type-1 collagen turnover, improving GLS on echocardiography and diastolic function. Potter et al performed an interesting RCT with patients who had baseline obesity, T2DM, or HTN and no HF diagnosis to determine incidence of HF at 24 months. Patients underwent usual care with general lifestyle recommendations vs. echocardiography to determine evidence of cardiac dysfunction which would trigger addition of spironolactone. The study was underpowered because of a high rate of spironolactone discontinuation due to renal dysfunction. However, per-protocol analysis showed resolution of left-ventricular dysfunction in patients who received therapy versus usual care (59% vs. 33%, p=0.01). Further research is needed to determine utility of spironolactone on HF prevention while preserving renal function. ¹²⁹

Finerenone

Finerenone is a nonsteroidal third generation MRA which blocks sodium reabsorption, overactivation, and inflammation in the kidneys, heart, and blood vessels. Compared to eplerenone and spironolactone, it causes hyperkalemia to a lesser extent and has higher potency and selectivity for the mineralocorticoid receptor, a shorter half-life, and no active metabolites, and less of an effect on BP.¹³⁰ FIDELIO-DKD and FIGARO-DKD were phase III trials assessing fatal and non-fatal CV events and progression of kidney disease. The FIDELITY study showed that finerenone significantly reduced risk of first HF (HR 0.78, p=0.003), however 7.7% of the population studied had a history of HF and the study did not stratify for incident HF occurrence. A subanalysis of the FIDELITY trial (Combined FIDELIO-DKD and FIGARO-DKD Trial Programme Analysis) combined with National Health and Nutrition Examination Survey data was conducted to simulate the number of composite CVD events that may be prevented per year with finerenone at a population level. The team found that finerenone was associated with a reduction in composite CV risk in patients with CKD, T2DM, estimated GFR > 25, and moderately to severely increased albuminuria. It was estimated that implementation of finerenone in this population could prevent approximately 14,000 hospitalizations for HF annually. It is unclear, however if this study looked at hospitalizations for newly diagnosed HF, or if patients in the study already had some baseline HF.¹³¹

Filappatos et al. took data from FIGARO-DKD and evaluated patients with T2DM and CKD without HF diagnosed at baseline who were randomized to either placebo or finerenone, then looked at several end-points including time-to-first-event of new-onset HF in those patients.¹³² They showed that new-onset HF was significantly reduced with finerenone versus placebo (HR 0.68, p=0.0162). Thus, finerenone seems to be a key component in medical management of high-risk patients to prevent incident HF.

There are currently no prospective trials that have shown preventative benefit of finerenone on HF, however several are under way. The FINE-REAL observational study aims to give insight into the use of finerenone in routine clinical practice, with a recruitment target of 5500 adults with CKD and T2DM in an estimated 200 sites across 22 countries.¹³³ The study will look at several CV adverse events and is due to start in June 2022 and estimated to end in 2027. The ongoing CONFIDENCE trial is a randomized, controlled, double-blind, double-dummy, international, multicenter, three-armed, Phase 2 study looking at the safety and efficacy of finerenone in combination with empagliflozin vs. empagliflozin alone vs. finerenone alone in 807 adults with T2DM, stage 2–3 CKD and UACR ≥300-<5000 mg/g.¹³⁴

ACCESS AND DELIVERY

Use of Telehealth

The use of telemedicine has grown rapidly as a result of the Covid-19 pandemic and is often cited as a solution to healthcare access.¹³⁵ Telemedicine is defined by management via telecommunication or automated information sharing.¹³⁶ In diagnosed HF patients, both branches of telemedicine have shown to be effective in reducing mortality and hospital admission rates among people with HF, especially in the short-term. ^{136, 137} However, equally robust benefits are not seen when applied for the management of CVD risk factors in prevention of HF, especially long term.^{137, 138} This lack of data likely follows from the lower utilization of telemedicine for preventative health and higher usage in established patients and in psychiatric or behavioral treatments.¹³⁵ However, studies have been more positive for more individual CVD factors like telemedicine in managing DM and HTN. Several studies and metanalyses have shown greater or equal reduction in blood pressure even by 4 weeks when compared to traditional care.^{139, 140} Moreover, in one study, the telehealth group were 26% more likely to highly rate their care experience and reported higher convenience although with increased burden of BP measuring.¹³⁹ Similarly in diabetes care, multiple metanalyses have found that telemedicine is equally or more effective for improving HbA1c than in-clinic care and suggest that teleconsultation is the most effective telemedicine strategy. ^141–143 In April 2023, the US Department of Health and Human Resources released a report on national survey on the utilization of telehealth from 2021 to 2022 (a survey of 1,180,248 adults) and found Hispanic, Latino, Black, and multi-racial patients had higher overall telehealth use than White respondents but were also less likely to use video telehealth over audio only when compared to their White counterparts.¹⁴⁴ Additionally, video telehealth use rate increased with higher income, education levels, and younger age.¹⁴⁴ Hence, this raises the question that although telehealth seems to increase access, does it do so equitably. Although promising, future studies are needed to evaluate the efficacy of telemedicine in improving access to HF prevention effectively and equitably.

Care for Rural Patients

In a recent study of ~27000 patients (68.8% Black and 20.0 % rural patients), rural patients had a 19% greater risk of incident HF even after adjustment for demographic information (including race and sex), CVD risk factors, health behaviors, and socio-economic status (including education, income, and marital status). ¹⁴⁵ Although not measured, these disparities were thought to be in part due to access to healthcare and preventative measures.¹⁴⁵ This is in line with previous studies showing that traditional CVD risk factors like smoking, obesity, physical inactivity, DM, HTN, hyperlipidemia, stroke, and CAD are in greater prevalence in rural populations.¹⁴⁶ This extends to secondary and tertiary prevention of Stage C and D HF when recent studies suggest that rural patients were less likely to be prescribed cardiac resynchronization therapy (CRT) and GDMT (such as ACE-I/ARB/ARNI therapy), even after adjustment for other hospital characteristics, such as geographic region, bed size, and teaching status.¹⁴⁷ However, with improvement, this recent study did not show differences in in-hospital mortality and 30-day post-discharge between rural and urban hospitals unlike previous studies.¹⁴⁷ These recent studies followed the recent 2020 presidential advisory call to action for the AHA and other stakeholders on addressing urban-rural disparities through programming, research, and policy. ¹⁴⁶ Some suggestions by this call to action, particularly pertaining to access and prevention improvement, were (1) Partnering with pharmacists, advanced practice providers and community health workers to expand the reach of preventative health measures (2) Use of telehealth (3) Medicaid expansion.¹⁴⁶

Social Isolation and Loneliness

The recent pandemic brought to light many of the gaps remaining in health care delivery and SDOH. Two such factors were social isolation, objective isolation or infrequent social connections, and loneliness, a painful feeling from a discordance between desire for connections and actual degree of connections.^{148, 149} In line with previous US findings, a recent UK study found that those with a higher level of social isolation or loneliness were more likely to have more unhealthy lifestyle factors (such as smoking, physical inactivity, unhealthy sleep, and a higher percentage of obesity) and history of chronic diseases ( such as CAD and DM). ^{148, 149} However, the study also found that both social isolation and loneliness increased the risk of incident HF by ~15%-20% in a dose-dependent manner (even when adjusted for demographics, employment status, education level, certain lifestyle factors, Townsend index, and medical and medication history).¹⁴⁸ Besides possible unknown biological pathways, this independent relation to incident HF was hypothesized to be through possible restrictions in social support and seeking healthcare.¹⁴⁸ This diminished access to healthcare would then result in poor preventative healthcare for HF. The pandemic brought new, promising solutions to the issues of isolation such as layperson telephone call programs. ¹⁵⁰ However, more sustainable change would require access to psychological therapies, community-based strategies, and public policies to provide access to those who are isolated.¹⁴⁸

CONCLUSION

With increasing prevalence, mortality, and healthcare expenditures attributed to Stage C/D HF, a shift upstream focusing on the primary prevention of Stage A/B HF is needed. Herein, we outline approaches to the primary prevention of HF. First, we support assessment of risk with identification of risk factors, such as traditional risk factors utilizing the framework of Life’s Essential 8, adverse SDOH, inherited risk of cardiomyopathies, and identification of risk-enhancing factors (e.g., COVID-19, HIV, chemotherapy, adverse pregnancy outcomes). Second, we endorse assessment of absolute risk of HF consistent with the 2022 ACC/AHA/HFSA Guidelines for the Management of HF with risk prediction tools such as PCP-HF or other contemporary tools that may allow calculation of HF risk. Third, we outline risk reduction strategies through a focus on traditional risk factors and targeted use of therapeutics such as fineronone and SGLT2i in a risk-based paradigm to prevent HF. Fourth, we suggest a focus on implementation of evidence-based therapies for HF prevention by improving delivery through methods like telemedicine and improving access by focusing on rural subsets of the populations who may face greater barriers to accessing care. We recommend continued monitoring and re-assessment of HF risk that may evolve over time, particularly with aging. Lastly, we identify key gaps in the current knowledge base, particularly focusing on inherited risk of HF, novel tools for risk stratification, and targeted therapeutic pathways to prevent HF in those people at risk.

ALPHABETICAL LIST OF ABBREVIATIONS

ACC

American College of Cardiology

ACCORD

Action to Control Cardiovascular Risk in Diabetes

ACE-I

Angiotensin-converting enzyme inhibitors

AHA

American Heart Association

AHEAD

Action for Health in Diabetes

AI

Artificial intelligence

ALLHAT

Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial

ARB

Angiotensin receptor blockers

ARIC

Atherosclerosis Risk in Communities

ARNI

Angiotensin receptor/neprilysin inhibitors

ART

Antiretroviral therapy

AUC

Area under the curve

BB

Beta-blockers

BMI

Body mass index

BNP

B-Type natriuretic peptide

BP

Blood pressure

CABG

Coronary artery bypass grafting

CAD

Coronary artery disease

CKD

Chronic kidney disease

CMS

Centers for Medicare & Medicaid Services

CNN

Convolutional neural network

CO

Carbon monoxide

CONFIDENCE

COmbinatioN effect of FInerenone anD EmpaglifloziN in participants with CKD and T2DM using a UACR Endpoint

CPAP

Continuous positive airway pressure

CRP

C-reactive protein

CRT

Cardiac resynchronization therapy

CV

Cardiovascular

CVD

Cardiovascular disease

DCM

Dilated cardiomyopathy

DM

Diabetes mellitus

ECG

Electrocardiogram

EF

Ejection fraction

EHR

Electronic health record

EPIC-NL

European Prospective Investigation into Cancer and Nutrition-Netherlands

ESC

European Society of Cardiology

FHS

Framingham Heart Study

FIDELIO-DKD

Finerenone in Reducing Kidney Failure and Disease Progression in Diabetic Kidney Disease

FIGARO-DKD

Finerenone in Reducing Cardiovascular Mortality and Morbidity in Diabetic Kidney Disease

FPG

Fasting plasma glucose

GDMT

Guideline-directed medical therapy

GFR

glomerular filtration rate

GLP-1RAs

Glucagon-like peptide 1 receptor antagonists

GLS

Global longitudinal strain

HCM

Hypertrophic cardiomyopathy

HF

Heart failure

HFA-ICOS

Heart Failure Association-International Cardio-Oncology Society

HFimpEF

HF with improved LVEF

HFmrEF

HF with mildly reduced LVEF

HFpEF

HF with preserved LVEF

HFrEF

HF with reduced LVEF

HFSA

Health Failure Society of America

HOPE

Heart Outcomes Prevention Evaluation

HR

Hazard ratio

HTN

Hypertension

LA

Left atrial

LAE

Left atrial enlargement

LS7

Life’s Simple 7

LV

Left ventricular

LVEF

Left ventricular ejection fraction

LVH

Left ventricular hypertrophy

MedDiet

Mediterranean diet

MESA

Multi-Ethnic Study of Atherosclerosis

MET

Metabolic equivalent of task

MI

Myocardial infarction

ML

Machine learning

MRA

Mineralocorticoid-receptor antagonist

MUFA

Monounsaturated fatty acids

NO₂

Nitrogen dioxide

NP

Natriuretic peptide

O3

Ozone

OSA

Obstructive sleep apnea

PA

Physical activity

PARABLE

Personalized Prospective Comparison of ARNI Within Patients With NP Elevation

PARAMETER

Prospective Comparison of ARNI with ARB Measuring Arterial Stiffness in the Elderly

PCP-HF

Pooled Cohort equations to Prevent HF

PM₁₀

Particulate matter with an aerodynamic diameter ≤10μm

PM_2.5

Particulate matter with an aerodynamic diameter ≤2.5μm

PONTIAC

PreventiOn of cardiac eveNts in a populaTion of dIabetic patients without A history of Cardiac disease

PREVEND

Prevention of Renal and Vascular End-stage Disease

RCT

Randomized control trial

RENAAL

Reduction of Endpoints in NIDDM with the Angiotensin II Antagonist Losartan

SBP

Systolic blood pressure

SDOH

Social determinants of health

SFA

Saturated fatty acid

SGLT2-I

Sodium-glucose cotransporter-2 inhibitor

SO₂

Sulfur dioxide

SPRINT

Systolic Blood Pressure Intervention Trial

STOP-HF

St Vincent’s Screening to Prevent Heart Failure

T2DM

Type 2 diabetes mellitus

TECOS

Trial Evaluating Cardiovascular Outcomes with Sitagliptin

UACR

Urine albumin to creatinine ratio

US

United States

VANISH

Valsartan for Attenuating Disease Evolution in Early Sarcomeric Hypertrophic Cardiomyopathy

VAT

Visceral adipose fat

WC

Waist circumference

WOSCOPS

West of Scotland Coronary Prevention Study