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Research Article
Originally Published 13 May 2021
Free Access

Epidemiology of Heart Failure: A Contemporary Perspective

Abstract

Designated as an emerging epidemic in 1997, heart failure (HF) remains a major clinical and public health problem. This review focuses on the most recent studies identified by searching the Medline database for publications with the subject headings HF, epidemiology, prevalence, incidence, trends between 2010 and present. Publications relevant to epidemiology and population sciences were retained for discussion in this review after reviewing abstracts for relevance to these topics. Studies of the epidemiology of HF over the past decade have improved our understanding of the HF syndrome and of its complexity. Data suggest that the incidence of HF is mostly flat or declining but that the burden of mortality and hospitalization remains mostly unabated despite significant ongoing efforts to treat and manage HF. The evolution of the case mix of HF continues to be characterized by an increasing proportion of cases with preserved ejection fraction, for which established effective treatments are mostly lacking. Major disparities in the occurrence, presentation, and outcome of HF persist particularly among younger Black men and women. These disturbing trends reflect the complexity of the HF syndrome, the insufficient mechanistic understanding of its various manifestations and presentations and the challenges of its management as a chronic disease, often integrated within a context of aging and multimorbidity. Emerging risk factors including omics science offer the promise of discovering new mechanistic pathways that lead to HF. Holistic management approaches must recognize HF as a syndemic and foster the implementation of multidisciplinary approaches to address major contributors to the persisting burden of HF including multimorbidity, aging, and social determinants of health.
The epidemiology of heart failure (HF) has been the subject of sustained interest since it was designated as a new epidemic in 1997.1 This designation was grounded in the observation of an exponential increase in HF hospitalizations and generated a provocative hypothesis examined in several epidemiological investigations. These investigations convincingly demonstrated that, since the middle of the 20th century, the incidence of HF had not increased in White populations, and that the increase in hospitalizations was related to improvement in survival after the diagnosis of HF, leading to an increase in the pool of persons living with HF and candidates for recurrent hospitalizations.2,3 Further, the heterogeneity of the HF syndrome became widely recognized including, specifically, the fact that HF could present with preserved or reduced left ventricular ejection fraction (EF).4 HF with preserved EF represents ≈50% of all HF cases in most studies, and therapeutic trials failed to identify effective treatments for HF with preserved EF.5 These epidemiological observations have been summarized in several comprehensive reviews including in Circulation Research in 2013.5–7 Importantly, these reviews highlighted major gaps in our knowledge of the epidemiology of HF in diverse populations.
The increase in HF-related deaths reported in the US constitutes a compelling rationale to review the most contemporary data on the epidemiology of HF with the goal of shedding some light on the drivers of the persisting burden of HF. Such drivers could include increases in incidence (more new cases) requiring intensification of primary prevention, or worsening outcomes calling for more effective treatment and management. The goal herein is to share information relevant to today’s clinical and public health practice and to underscore knowledge gaps that could guide future research.
First, the taxonomy of HF will be reviewed to discuss various classifications impact our understanding of its epidemiology. Second, contemporary data on the incidence, prevalence, mortality, and hospitalizations of HF will be discussed while summarizing data overall as well as in specific populations. Third, data on risk factors (conventional and emerging) will be discussed. In doing so, this review will outline gaps in knowledge and future research directions.

Classifications of HF

Studying the epidemiology of any disease requires defining and adopting criteria to diagnose the disease under consideration as well as to classify its severity. Specifically, for HF, this critical step requires consideration of the epidemiological definitions, of the stages of HF including the increasingly used entity of advanced HF, and of cardiac function.

Epidemiological Definitions of Overt HF

In the 2013 Circulation Research review on this topic,7 the criteria for the diagnosis of HF used in epidemiology research was summarized. These have not changed since that publication and are, therefore, reproduced with permission in Table 1 for ease of reference.
Table 1. Heart Failure Diagnostic Criteria
FraminghamBostonEuropean Society of CardiologyGothenburg score
Item and method of assessment
Major criteriaCategory I: HistorySymptoms of heart failure (at rest or during exercise) 
 Paroxysmal nocturnal dyspnea or orthopnea Rest dyspnea (4 pts) Cardiac score
  Orthopnea (4 pts) History of heart disease (1–2 pts)Self-report
 Neck vein distension Paroxysmal nocturnal dyspnea (3 pts)Objective evidence of cardiac dysfunction (at rest)Angina (1–2 pts)Self-report
 Rales Dyspnea on walking on level (2 pts)Response to treatment directed towards heart failure (in cases where diagnosis is in doubt).Edema (1 pt)Self-report
 Cardiomegaly
 Acute pulmonary edema Dyspnea on climbing (1 pt)   
 S3 gallopCategory II: Physical examination Nocturnal dyspnea (1 pt)Self-report
 Increased venous pressure ≥16 cm water Heart rate abnormality (1–2 pts)Criteria 1 and 2 should be fulfilled in all casesRales (1 pt)Physical exam
 Circ.time ≥25 sec Jugular venous pressure elevation (1–2 pts) Atrial fibrillation (1 pt)ECG
 Hepatojugular reflux Lung crackles (1–2 pts) Pulmonary score 
Minor criteria Wheezing (3 pts) History of chronic bronchitis/asthma(1–2 pts)Self-report
 Ankle edema Third heart sound (3 pts)   
 Night coughCategory III: Chest radiography Cough, phlegm, or wheezing (1 pt)Self-report
 Dyspnea on exertion
 Hepatomegaly Alveolar pulmonary edema (4 pts)   
 Pleural effusion
 Vital capacity decreased 1/3 from maximum Interstitial pulmonary edema (3 pts) Rhonchi (2 pts)Physical exam
 Tachycardia rate of ≥120/min Bilateral pleural effusions (3 pts) Cardiac and pulmonary score are calculated and used to differentiate 
Major or minor criterion Cardiothoracic ratio ≥0.50 (3 pts) Cardiac form pulmonary dyspnea 
 Weight loss ≥4.5 kg in 5 days in response to treatment Upper-zone flow redistribution (2 pts)   
 Heart failure present with 2 major or 1 major and 2 minor criteria Definite heart failure 8–12 pts, possible 5–7 pts, unlikely 4 pts or less   
Reproduced from Roger7 with permission. Copyright ©2013, Wolters Kluwer Health, Inc.

Stages of HF and Other Classifications

The stages of HF were first proposed in the 2001 American Heart Association American College of Cardiology guidelines.12 The intent of the classification was to emphasize both the evolution and progression of the disease by defining 4 stages: stages A and B are preclinical stages while stages C and D are characterized by the presence of overt clinical HF of increasing severity from C to D (Figure 1).13
Figure 1. Classifications of heart failure. AHA indicates American Heart Association; ACC, American College of Cardiology; HF, heart failure; and NYHA, New York Heart Association. Adapted from Truby et al13 with permission. Copyright ©2020, Elsevier.
The New York Heart Association classification categorizes on the relationship between symptoms of dyspnea and physical activity.12 New York Heart Association has 4 classes (New York Heart Association I–IV): class I, no symptoms with normal physical activity; class II, mild symptoms with usual physical activity, but none at rest; class III, patients comfortable at rest but experiencing moderate symptoms with less than normal physical activity; and class IV, severe dyspnea also at rest.
Advanced HF is a clinically important designation as it can guide consideration of advanced therapies including transplant and mechanical ventricular assistance. Professional societies including the Heart Failure Society of America, the American College of Cardiology/American Heart Association, the Heart Failure Association and the European Society of Cardiology have proposed criteria to define this condition. In the 2018 version of the European Society of Cardiology criteria, the diagnosis of advanced HF requires several findings which must all be present despite optimized guideline-directed medical therapy. These include severe symptoms (New York Heart Association class 3 or 4), severe cardiac dysfunction with EF ≤30%, right ventricular dysfunction, nonoperable valvular or congenital heart disease abnormalities, pulmonary congestion or low cardiac output requiring intravenous diuretics or inotropes, and severe reduction in exercise capacity believed to be cardiac in origin.14
A recent systematic review focused on clinical trials underscored the considerable variation in the definitions and criteria for advanced HF, with little consistency in criteria and quantitative cutoff points.15 This variability should be considered when examining the results of reports on the epidemiology of advanced HF. There is currently a paucity of data on this topic.

Classification According to Left Ventricular EF

The increasing clinical use of noninvasive imaging, in particular echocardiography, in the mid-1990s enabled identifying patients with clinical symptoms and signs of HF but without reduction in left ventricular EF, uncovering the syndromic and heterogeneous nature of HF. HF without reduction of left ventricular EF was initially labeled diastolic HF until the recognition of the presence of both systolic and diastolic functional abnormalities in most cases.16 Since then, HF without reduction of left ventricular EF is referred to as HF with preserved EF or HFpEF (Heart Failure with Preserved Ejection Fraction), while its counterpart is referred to as HF with reduced EF or HFrEF. While generally widely accepted, it is nevertheless important to mention that the concept of HFpEF as a distinct entity within the HF spectrum has been a topic of some controversy.17,18 While recognizing the clinical utility of the categorization by EF, some have underscored its over-simplistic nature and the need to identify a mechanistic approach to classify the HF syndrome while reflecting its complexity.17 The controversy about the classification of HF was further amplified when a new category was proposed by the American College of Cardiology/American Heart Association/European Society of Cardiology as HF with an EF of 40% to 49%,19 termed HF with mid-range EF.20
One important consideration lies in the imprecision of EF measurements and its variability over time.21 In a systematic review of the reliability of EF measurement by echocardiography, the technique most commonly used, the inter-observer variability was 8% to 21% and the intraobserver variability 6% to 13%,22,23 indicating that echocardiography may be adequate to establish that EF is clearly abnormal (<30%) or normal (>50%). However, between 30% and 50%, it is imprecise, and these cutoffs exceed the resolution and reliability of the method. Recently in a trial with rigorous core laboratory readings, the agreement across imaging modalities to measure EF lacked precision.24 In this context, the Australian and New Zealand guidelines do not recognize the HF with mid-range EF category because it does not represent a clearly defined clinical entity and does not justify specific management recommendations.25 A recent report from the United States also categorized HF using the same approach but excluded cases with EF between 40% and 50%.26 In studies that have examined the prevalence of HF with mid-range EF, it appears to ≈15%.27

Incidence, Mortality, and Prevalence

These measures are the foundation of epidemiology investigations of the burden of a given disease in populations. Incidence is defined by the number of new cases within an enumerated population over a defined time period. Variations in incidence conceptually reflect the effectiveness of prevention measures. Mortality after the diagnosis of a disease can be measured at various times intervals during follow-up and theoretically reflect the impact of treatment. While this framework should not be applied overly literally, it informs our understanding of major determinants of disease burden, which in turn helps shape clinical management and public policies.
For HF, incidence as a measure of new cases is particularly helpful to assess how the occurrence of HF might have changed over time as a result of changes in risk factors. The validity of these measures relies on standardized criteria which should be, to the extent possible, not overly influenced by the adoption of testing that may increase the detection of the disease under consideration.
In 2013, Circulation Research reviewed data on the incidence and prevalence of HF from studies published up to the year 2013.7 Taking collectively, these data indicated that the prevalence of HF varied considerably from 1% to 12% based largely on United States and European reports. The incidence of HF differed across studies due to differences in definitions and analysis. Importantly, however, temporal trends were congruent across reports and indicated that the incidence of HF was stable or perhaps even decreasing. Available data indicate that lifetime risks are very high regardless of sex, race, and geography, underscoring the importance of population-wide efforts to contain the burden of HF.
Selected studies published since 2013 (Table 1) document several key points:
1.
Most recent studies analyzed administrative data that rely on diagnostic codes. As such, they are subject to several biases including shifts in coding practices as a result of changes in reimbursement. Few studies relied on standardized criteria.
2.
Different adjustment approaches preclude direct comparisons of incidence rates across studies. Useful information can, however, be derived from studies which reported and analyzed temporal trends indicated by an asterisk in the table. Importantly, regardless of ascertainment methods, all studies have documented a decline in the incidence of HF over time.
3.
Few reports analyzed data and time trends while stratifying by race and ethnicity. This important aspect will be addressed later in this review.
These limitations notwithstanding, data are congruent across studies and indicate a temporal decline in the incidence of HF. Not all studies reported on case mix according to EF. Those that did indicated that, within the overall decline in the incidence of HF, the proportion of cases with preserved EF increased. Given the current lack of effective treatment for HF with preserved EF, this observation is important as it suggests that an increasing proportion of the cases of HF will elude therapeutic options.
While reviewing HF mortality data, it is important to distinguish between deaths attributed to HF in National Vital Statistics from mortality as an outcome among cohorts of persons living with HF. Several analyses of National Statistics have convincingly shown that HF death rates have recently begun to increase.8,9,28 Recent increases broadly affect counties in the United States and racial and sex disparities persisted as mortality trends changed.29
Within cohorts, mortality data are conceptually related to progress in medical care and are found to be reported as short and long term. Among studies that used standardized criteria and reported long-term data (Table 2), mortality remains high and, in most recent years, ≈50% at 5 years.39–41 These findings are sobering and underscore that despite progress in management, survival after the diagnosis of HF remains as poor as that of some of the common cancers in both men and women.38 Further the proportion of deaths attributed to noncardiovascular causes has been increasing.41
Table 2. Selected Studies Reporting on the Incidence, Prevalence, and Mortality of Heart Failure
Diagnostic criteriaAuthor, publication yearYears studiedIncidencePrevalencePopulation sourceMortality
Nonstandardized criteria
 ICD codesZiaeian et al302002–2013527/100 000 in 2002365/100 000 in 2013National Inpatient SampleHospitalizations rates
 ICD codesSavitz et al312012–2016Hospitalizations and outpatient recordsKaiser Permanente Health Plan12/100 person-yrs
 Hospital diagnosisCheng et al27 AHA GWTGVoluntary registryCMS linkage1-y unadjusted36%
 ICD 9 code (428.xx)Nichols et al262008–2011Hospitalizations and outpatient recordsKaiser Permanente Health Plan30-day adjusted4.5%1 y adjusted24%
 ICD 9 codesDharmarajan er al322008–2014Medicare Standard Analytic and Denominator files30-day adjusted8.4%
 Hospital diagnosisGupta et al332006–2014AHA GWTGVoluntary registryCMS linkage30-day adjusted7.2% in 20068.6% in 20141 y adjusted31.3% in 200636.3% in 2014
 ICD codesConrad et al342002–2014358/100 000 person-yrs332/100 000 person-yrs1.5% to 1.6%Hospitalizations and primary care recordsUnited Kingdom
 ICD codesKhera et al352011–201635.7/1000 in 201126.5/1000 in 2016Administrative data: Medicare beneficiaries (mean age 73 y old)Age-adjusted30-day unadjusted2011:16.2/10002016:17.2/1000
 Hospital diagnosisParizo et al362007–2017HospitalizationsVeterans Affairs Health Care System30-day unadjusted5.6%1-year unadjusted25%
 ICD codesChristiansen et al371995–20121995 vs 2012 rates per 10 000 persons yrs164 vs 115 if age >74 y63 vs 35 if age 65–74 y20 vs 17 if age 55–64 yDenmarkHospitalizations and outpatient recordsNational Registry 
 Mamas et al38 Scotland 
Standardized criteria
 Framingham criteriaTsao et al391990–20091990 to 199919.7/1000 persons2000 to 200918.9/1000 personsFramingham Heart StudyAll ages, age adjusted, mostly White persons67% at 5 y unchanged overtime
 Framingham criteriaGerber et al402000–2010315.8/100 000 in 2000219.3 per 100 000 in 2010Olmsted CountyAll ages, age adjusted, mostly White persons53% at 5 y unchanged overtime
AHA indicates American Heart Association; CMS, Centers for Medicare and Medicaid Services; and GWTG, Get with the Guidelines.
The prevalence of HF reflects the combination of incidence and mortality. Expressed as the number of individuals living with HF and the corresponding proportion in a given population, it provides an indication of the societal and economic burden of HF.
In addition to the few studies shown in Table 2 that report on prevalence, the 2021 American Heart Association Statistical Update estimates the prevalence of HF to be 6 million which ≈1.8% of the total US population (332 601 million at 2020 Census).42,43
Other estimates indicate that the prevalence of HF in the United States and Canada is 1.5% to 1.9% of the population and in Europe 1% to 2%.44
The prevalence estimate in the US can be informally compared with the estimate of an American Heart Association forecasting study published in 2013.45 This study forecasted that the number of persons living with HF in 2020 in the United States would be 6 859 623, ≈2.1% of the population. Hence, the prevalence of HF increased slightly less than anticipated, which is consistent with the aforementioned reports of declining incidence and stable mortality.
However, HF is far more prevalent in older age groups, reaching 4.3% among persons aged 65 to 70 years old in 2012 and projected to increase steadily through year 2030 when the prevalence of HF could reach 8.5%.46 As a result, HF is often integrated into geriatric cardiovascular syndromes with their inherent burden of multimorbidity and frailty, which considerably amplifies the personal and societal impact of the disease.46 Black persons with HF, particularly women, experience a disproportionately large excess prevalence of disability.46
Finally, estimates of the prevalence of overt HF not unexpectedly underestimate the true prevalence of HF as it relies on cases coming to medical attention. The prevalence of HF as assessed by systematic echocardiographic screening is substantially higher, even reaching 11.8% in a meta-analysis which, however, underscored the heterogeneity of methods across studies.47

Hospitalizations and Readmissions

The interpretation of data on hospitalizations requires a brief review of nomenclature. In the literature, reported data can be centered on the first hospitalization for HF as a proxy for incidence date. Subsequent hospitalizations, most often termed readmissions, can be HF related or due to a different cause. It is important to distinguish between hospitalizations among individuals living with HF and HF-related hospitalizations. This is relevant given the high comorbidity burden among persons living with HF that can often lead to hospitalizations for causes different than HF. Finally, ascertaining the cause of hospitalization can be problematic for several reasons: coding practices aim at maximizing reimbursement and clinical causes for hospitalizations often coexist among elderly and frail patients with HF.
Beyond these matters of definitions, and specifically for the United States, the trends in hospitalizations over the last decade must be interpreted considering changes in national policies. In 2012, the Hospital Readmissions Reduction Program (HRRP) was implemented as part of the Patient Protection and Affordable Care Act to reduce readmissions, lower costs, and improve safety and outcomes for several conditions including HF. Under HRRP, Centers for Medicare and Medicaid Services imposed financial penalties on hospitals with higher than expected 30-day readmission rates.48 Hospitals responded rapidly to the HRRP,48 and specifically for HF, readmissions rates declined notably after its implementation.49,50
Since its implementation, the HRRP generated concerns51–55 related to the risk adjustment methods, the penalization of safety net hospitals, the marginal effectiveness of the program, and unattended consequences resulting in an increase in mortality.51–55 A study of fee for service Medicare beneficiaries pertaining to years 2008 to 2014 indicated that readmission rates were weakly correlated with a reduction in 30-day mortality rates. These findings were interpreted as not supporting a concern for increased postdischarge mortality related to reducing readmissions after implementation of the HRRP.32 Conversely, data from the Get With The Guidelines voluntary registry from fee for service Medicare beneficiaries pertaining to years 2006 through 2014 indicated an increase in adjusted 30-day mortality from 7.2% to 8.6% after the HRRP penalties were implemented raised the question of a deleterious impact on mortality after the implementation of the HRRP.33
While the impact of policy changes must be considered in interpreting trends in hospitalizations over the period of interest in the United States, causality between HRRP and mortality in HF cannot be conclusively established as these reports are ecological analyses, inherently vulnerable to residual confounding by other unmeasured concurrent changes in management of patients, treatment modalities, or other factors. While a controlled trial following a cluster randomization approach would have been helpful, such evaluation was not conducted before implementing this policy and is challenging to envision at this stage. Thus, these reports cannot by design conclusively support or refute the hypothesis of an excess mortality related to the HRRP. Nevertheless, the persistently high mortality of HF along with profound health disparities in HF outcomes provide a compelling rationale to modifying the HRRP program by improving readmission measures and optimizing approaches to risk adjustment while improving equity.56
Given the controversy surrounding HRRP, it is important to examine results of studies conducted in settings and countries that are not subject to the HRRP.
The Veterans Affairs Health Care System provides incentives to reduce HF readmissions without financial penalty. Between 2007 and 2017, readmissions for HF declined in the Veterans Affairs System, while 30-day mortality remained unchanged.36 Stratification by EF yielded similar results.
European policies and practices are of course not subject to the HRRP. Data from Norway pertaining to all hospital stays for the entire country between 1994 and 2014 enabled the analysis of temporal trends in all hospitalizations with HF as the primary discharge diagnosis. The total number of hospitalizations did not change during the study period while mortality from HF declined.57 The German Federal Health Monitoring provides an annual complete census of in-patient data. Between 2000 and 2017, the number of hospitalizations increased markedly overtime while the length of stay decreased and in hospital mortality remained unchanged.58
Taken collectively, these data emphasize that the short- and long-term mortality of HF has at best remained stable. This observation is sobering given the number of programs and initiatives deployed over the years to improve the management of HF including adherence to guideline-directed medical therapy and transitions from hospital to home.59–70

HF in Populations

Epidemiology is the study of disease across persons, place, and time. Hence, it is central to the discipline and to the goal of this article to evaluate the burden of diseases in diverse populations.
The incidence and prevalence of HF increases with age when it is often integrated in a complex context of multimorbidity and geriatric syndromes.71,72 With regards to sex distribution, women are ≈2× more likely than men to develop HFpEF.73
With regards to race or ethnicity, studies summarized in Table 2 seldom report stratified results and the data are overall sparse.

Black Populations

Disparities in all key indicators of the population burden of HF including incidence, prevalence, and outcomes have been clearly identified in Black populations.
Disparities in the incidence of HF were reported long ago within large multi-racial cohorts supported by the National Heart Lung and Blood Institute of the National Institutes of Health. These disparities are most striking in younger ages as shown in the CARDIA study (Coronary Artery Risk Development in Young Adults), where 26 out of 27 cases of HF reported under age 50 occurred in Black participants.74 While partially attenuated with age, disparities in incidence persisted among middle-aged populations as shown in the MESA (Multi-Ethnic Study of Atherosclerosis) and in the ARIC (Atherosclerosis Risk in Communities) studies.75,76 National Health and Nutrition Examination Survey data identified a similar pattern for self-reported prevalence of HF according to age.77
With regard to outcomes, recent reports generated important insights. Analyses of national and state-specific data underscored a persisting large excess risk of hospitalizations and deaths among Black persons, particularly in younger age groups.28,30,78 Data from Kaiser Permanente Northern California reported an excess risk of hospitalizations contrasting with a lower risk of deaths. As acknowledged by the authors, these findings, which persisted despite extensive adjustment, are discordant from other studies and may in part reflect the unique nature of these data emanating from an integrated delivery system of insured individuals.31
The determinants of these disparities are multifactorial and interrelated. In most reports, statistical adjustment for differences in risk factors (including age, sex, diabetes, hypertension, cholesterol, smoking, and left ventricular hypertrophy) attenuated or eliminated most or all of the excess risk of HF among Black persons compared with White persons. These results led to the interpretation that disparities the burden of HF were attributable to the associated burden of risk factors. While methodologically correct, this interpretation stops short of calling out the root cause of the disproportionate burden of HF among Blacks persons; indeed, disparities in cardiovascular risk factors are key components of the causal pathway from primary prevention to excess overt HF. The root cause of this adverse trajectory is grounded in structural racism, emerges early in life for Black persons and is amplified over time by detrimental social determinants of health, disparity in the access to and the delivery of care.79 The complexity of the drivers of persisting disparities in HF among Blacks was recently highlighted in an in-depth review.80 Acknowledging this complexity is the requisite first step to address this urgent issue. To do so, operationalizing the concept of syndemics is helpful. Syndemics is a conceptual framework for understanding diseases or health conditions that arise in populations and that are exacerbated by the social, economic, environmental, and political milieu in which a population is immersed.81 Figure 2 illustrates how this conceptual framework can be applied to HF to contribute to a more comprehensive appraisal of disparities. The integration of social and environmental factors expands our comprehension of the HF epidemic beyond the boundaries of disease management and health systems. While this approach can come across as overly broad, it can be integrated into multiscale, multisystem intervention models.
Figure 2. Syndemic framework applied to heart failure. GDMT indicates guideline directed medical therapy; HCM, hypertrophic cardiomyopathy; TTR, transthyretin.

Hispanic and Latine Persons

Data on the incidence of HF in Hispanic and Latine persons are quite sparse. Available data come mainly from MESA, which estimated the incidence of HF in Hispanics to be 3.5 per 1000 person-years, higher than that observed among non-Hispanic Whites (2.4 per 1000 person-years), and lower than that observed among non-Hispanic Blacks (4.6 per 1000 person-years).75,82
South Asians (people from Bangladesh, Bhutan, India, the Maldives, Nepal, Pakistan, and Sri Lanka) are one of the fastest-growing ethnic groups in the United States.83 Data on the epidemiology of HF are mostly lacking for this population, pointing to an important knowledge gap to be addressed in future studies given the high burden of atherosclerotic cardiovascular disease and associated risk factors among South Asians.

Causes of HF, Risk Factors, Prediction, and Prevention

Causes of HF and Conventional Risk Factors

HF is the end-stage manifestation of most forms of heart disease. Hence traditional risk factors play an important role in the genesis of HF as they do for many other manifestations of heart disease. Hypertension, diabetes, sedentarity, hyperlipidemia, and smoking have all been reported as associated with incident HF, either mediated by coronary disease or in some case directly associated with HF such diabetes or obesity, which are increasingly recognized as being implicated in the genesis of HF through various pathways.84
The estimated respective responsibility of each of these risk factors varied across time and by race and ethnicity.
The role of coronary disease is likely evolving, reflecting the evolving epidemiology of coronary disease. The association between coronary disease and HF was conceptually thought to be primarily mediated by the occurrence of large myocardial infarctions (MI), leading to sizable ventricular scarring and remodeling, both harbingers of overt clinical HF. However, over the past decade, the severity of MI has declined as has the frequency of large territories of infarction.85 The occurrence of HF after MI has not decreased in proportion to the changing epidemiology of MI suggesting that other mechanisms could play a role in the genesis of HF after MI.86 In a community cohort of patients who experienced an incident MI, the extent and severity of coronary disease at angiography was associated with HF after the MI independently of a recurrent clinical coronary event. These data emphasized the possibility that other processes are related to the occurrence of HF post-MI independently of epicardial coronary obstruction.87
Traditional cardiovascular risk factors often coexist and can interact with one another to increase the likelihood of developing HF. The same consideration applies to multimorbidity which is highly prevalent in elderly populations with HF. We recently reported on the prevalence and attributable risk of several risk factors and comorbid conditions in a community case-control study.71 Figure 3 illustrates these findings by showing that the prevalence of several risk factors is higher among patients with HF compared with controls. In most cases except for hypertension, these proportions were similar regardless of EF. Cardiometabolic risk factors (diabetes and obesity) were more strongly associated with HF in younger age groups. Cardiac arrhythmias were found to play a notable role in the genesis of HF in this study, and this observation deserves further investigations. The attributable risk provides information about the absolute effect of the exposure by indicating the excess risk of disease in those exposed compared with those nonexposed.88 This construct is relevant to population health since it provides insight into the proportion of the cases of disease that would be avoided if the risk factor under consideration was eliminated. For example, if we consider the excess risk of HF related to hypertension, the attributable risk is the theoretical decrement in the rate of HF to be expected if hypertension was eliminated. Rather than the complete elimination of the risk factor, which is improbable, this measure can be used to provide insights into the results that might be achieved if only a fraction of the prevalent risk factor in consideration is removed from the population.
Figure 3. Prevalence and attributable risk of comorbidities in heart failure (HF). Adapted from Chamberlain et al71 with permission. Copyright ©2020, Elsevier. AR indicates attributable risk; and CAD, coronary artery disease.
Finally, regardless of the aforementioned limitation of current, EF-driven classification, HFpEF has emerged as a highly complex entity reflecting, beyond left ventricular diastolic dysfunction, several contributory factors, including impaired left ventricular systolic reserve, systemic and pulmonary vascular function, coronary microvascular endothelial dysfunction, chronotropic incompetence, left atrial and right heart dysfunction, and impaired ventricular-vascular compliance.5 The respective responsibilities of each of these factors is not fully delineated, underscoring the importance of ongoing research on this topic.

Risk Models and Predictive Equations

A systematic review of 28 risk prediction models indicated an acceptable but not excellent performance in predicting HF; however, in this early work, only 2 models had been externally validated.89 More recently, Khan et al90 reported on a model developed in 5 pooled National Heart Lung and Blood Institute-sponsored cohorts including the ARIC study, the CARDIA study, the CHS (Cardiovascular Health Study), and the Framingham Heart Study Offspring Cohort and MESA. The pooled cohort was randomly split into derivation and validation samples. External validation was conducted in the Jackson Heart Study and the Prevention of Renal and Vascular End-Stage Disease cohorts. Model performance was examined in sex- and race-specific strata and performance well overall in the 4 sex- and race-specific strata, although the C statistic was noticeably lower among Black men and women.
The pooled cohort equations included age, blood pressure, fasting glucose, body mass index, cholesterol, smoking status, and QRS duration. These variables are routinely obtained as part of clinical evaluation and could be integrated in electronic health records (EHR) as suggested by the authors who developed a web-based tool to facilitate clinical identification of individuals at higher risk of developing HF. These individuals could then undergo sequential advanced risk prediction that could include biomarkers such as natriuretic peptides, galectin, soluble ST2 receptor, or advanced imaging. In a subsequent report, the pooled cohort equation was evaluated within an EHR-derived primary prevention cohort and showed excellent discrimination in White men and women but only adequate discrimination in Black men and women, where a systematic underestimation of HF risk was noted.91 This observation is critically important as it resonates with concerns of embedded bias in EHR data due to the pervasive impact of systemic racism on access to and delivery of care.

Emerging Risk Factors and Multi-Omics

An exhaustive review of emerging risk factors, including multi-omics is beyond the scope of this review, given the relative paucity of epidemiological data on this topic as it relates to HF. These are mentioned here to underscore the need for further research in these domains.

Inflammation and Fibrosis

There are multiple lines of evidence suggesting that these are part of the causal pathway from risk factors to HF. While multiple mechanistic underpinnings might be at play, this consideration seems to apply to both HFpEF and HFrEF. Within the specific context of MI, prospective community studies of post-MI ventricular remodeling, (a precursor to overt HF) and clinical HF after MI have consistently reported associations between inflammatory and fibrosis markers (CRP [C-reactive protein], ST2, galectin), and the occurrence of HF.92–94
Amyloid fibril formation results from a destabilizing mutation in hereditary ATTR (transthyretin amyloid) amyloidosis or from an aging-linked process in wild-type ATTR amyloidosis. Myocardial deposition of misfolded transthyretin (TTR) or prealbumin causes a cardiomyopathy, and transthyretin amyloid cardiomyopathy (ATTR-CM) is increasingly recognized as a cause of HF. It is estimated that 10% to 15% of older adults with HF may have unrecognized wild-type ATTR. Clinical signs, including carpal tunnel syndrome and lumbar spinal stenosis, should raise suspicion. Imaging studies such as echocardiography or cardiac magnetic resonance imaging are not diagnostic but can further support the diagnostic hypothesis which can be evaluated with noninvasive nuclear imaging in the absence of a monoclonal protein.95 However, epidemiological data on this important matter are not yet available. Given emerging medical therapies that can slow or halt ATTR-CM progression, delineation of the population prevalence of this condition is important to guide evaluation strategies.

Multi-Omics

The HF syndrome is the advanced stage manifestation of many forms of heart disease. Hence, the identification of genetic contributions to HF is best conceptualized by focusing on the genetic determinant of the upstream manifestations of heart disease before reaching the HF stage. To this end, dilated cardiomyopathies are a frequent cause of HF and the contribution of genetic factors was recently reviewed. Available clinical data suggest that genetic testing leads to the identification of a culprit variant in ≈15% to 40% of the cases depending on whether not the dilated cardiomyopathy is a familial or sporadic.96 The guidelines currently recommend that all first-degree relatives of patients with familial dilated cardiomyopathy undergo genetic testing but are less prescriptive for cases of sporadic cardiomyopathy. However, a recent analysis of clinical trial data validated in the UK Biobank suggest that the contribution of Mendelian variants to HF may not be as rare as initially hypothesized, underscoring the importance of further research in this domain.97
Proteomics have taken an increasingly prominent role as a means to elucidate the mechanistic underpinnings of several disease entities. While this is still in emerging field, several studies have shown some promising associations with some proteomics signatures and various manifestations of HF including stage, progression, and mortality.98–101

E-Epidemiology: EHR Phenotyping

Rapid growth in the implementation of EHRs, fueled in part by incentives to health systems demonstrating meaningful use of EHRs, has considerably expanded the availability of dense longitudinal data sets.102 This represents an unprecedented opportunity to combine new data and new methods to achieve precision phenotyping, recognize individual variations in presentation and outcomes, refine patient groups for personalized interventions, and identify new drug targets.103 EHR data have the potential of contributing to our understanding of the epidemiology of cardiovascular disease in general and HF in particular.104
Capturing all clinically collected data can enable the identification of previously unrecognized relationships underpinning the HF syndrome and the delineation of mechanistically coherent phenotypes. The clinical applicability of EHR-based approaches is particularly appealing for risk prediction as EHRs constitute comprehensive repositories of the clinical experience of patients and can integrate a vast amount of complex data that oversubscribed clinicians caring for complex patients could not easily integrate.
EHR-based epidemiology is a major departure from historical methods which creates methodological challenges. Voluminous and complex data from heterogeneous sources must become suitable for extraction and analysis while ensuring traceability, validity, and reproducibility. Regardless of size, observational data sets remain vulnerable to selection biases, confounding, and lack of generalizability, which underscores the importance of relying on a strong epidemiological foundation in well-characterized and representative populations.105
With regards to machine learning, in 2017 representatives from academia, the National Institutes of Health, the US Food and Drug Administration, the Centers for Medicare and Medicaid Services and industry106 summarized controversies surrounding HFpEF. They agreed on the need for deep phenotyping to better classify HF and underscored one concern as quoted: “Techniques, such as machine learning that can integrate data from various sources may lead to discovery of biologically distinct phenotypes and potentially differentiate responsiveness to therapy. However, ultimately machine learning is only as good as the input variables, and if the critical pathophysiologic features that drive HFpEF are not directly or indirectly represented within the input variables, the analysis can be unhelpful…”106 This statement encapsulates the key limitations of EHR-based epidemiology and clinical research, where health care data are the end product of access to care, care-seeking behaviors, and care delivery processes, thus leading to complex patterns of data missingness and inherently subject to biases.

Conclusions

More than 2 decades after its designation as an emerging epidemic, HF remains a clinical and public health problem of major proportion. HF is associated with significant mortality and morbidity, particularly among those aged 65 and older, which does not reflect an increase in the incidence of the disease but rather illustrates the chronic course of patients living with HF, with frequent hospitalizations and stagnating mortality. Studies of the epidemiology of HF over time have improved our appraisal of the complexity of this syndrome. However, progress in the occurrence and outcomes of HF has not been commensurate with the resources dedicated to its management. Our collective understanding of the HF syndrome remains incomplete, which hinders therapeutic progress and contribute to fuel the epidemic. Research is urgently needed to improve our understanding of the molecular mechanisms of HF while new holistic care models must be devised to manage HF synergistically across health systems and communities.

Footnote

Nonstandard Abbreviations and Acronyms

ARIC
Atherosclerosis Risk in Communities
CARDIA
Coronary Artery Risk Development in Young Adults
CHS
Cardiovascular Health Study
EF
ejection fraction
EHR
electronic health record
HF
heart failure
HRRP
Hospital Readmissions Reduction Program
MESA
Multi-Ethnic Study of Atherosclerosis
MI
myocardial infarction

Acknowledgment

I thank Deborah Strain for her administrative support.

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Go to Circulation Research
Go to Circulation Research
Circulation Research
Pages: 1421 - 1434
PubMed: 33983838

History

Published online: 13 May 2021
Published in print: 14 May 2021

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Keywords

  1. chronic disease
  2. heart failure
  3. prevalence
  4. risk factor
  5. syndemic

Subjects

Authors

Affiliations

Department of Quantitative Health Sciences and Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN. Now at Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health. Véronique L Roger, MD, MPH is now at Chief, Epidemiology and Community Health Branch National Heart, Lung and Blood Institute, National Institutes of Health.

Notes

For Disclosures, see page 1431.
Correspondence to: Véronique L. Roger, MD, MPH, Mayo Clinic; 200 First St, SW; Rochester, MN 55905. Email [email protected]

Disclosures

Disclosures None.

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  1. CardioMEMS Heart Failure System: An Up-to-Date Review, Cureus, (2025).https://doi.org/10.7759/cureus.77816
    Crossref
  2. The influence of clinical pharmacist-directed PDCA and DRG on the hospital’s antitumor treatments and safety management, Indian Journal of Cancer, 61, 3, (654-661), (2025).https://doi.org/10.4103/ijc.ijc_266_24
    Crossref
  3. Predictors and Trends of 30-day Readmissions in Patients With Acute Decompensated Heart Failure With Preserved Ejection Fraction: Insight From the National Readmission Database, International Journal of Heart Failure, 7, 1, (21), (2025).https://doi.org/10.36628/ijhf.2024.0041
    Crossref
  4. Four-Dimensional Magnetic Resonance Pulmonary Flow Imaging for Assessing Pulmonary Vasculopathy in Patients with Postcapillary Pulmonary Hypertension, Journal of Clinical Medicine, 14, 3, (929), (2025).https://doi.org/10.3390/jcm14030929
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  5. High mRNA Expression of 24 Dehydrocholesterol Reductase (DHCR24) in the Treatment of Doxorubicin-Induced Heart Failure in Rats, International Journal of Molecular Sciences, 26, 1, (312), (2025).https://doi.org/10.3390/ijms26010312
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  6. Short-Chain Fatty Acids and Their Metabolic Interactions in Heart Failure, Biomedicines, 13, 2, (343), (2025).https://doi.org/10.3390/biomedicines13020343
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  7. Cinnamic acid alleviates hypertensive left ventricular hypertrophy by antagonizing the vasopressor activity and the pro-cardiac hypertrophic signaling of angiotensin II, Frontiers in Pharmacology, 16, (2025).https://doi.org/10.3389/fphar.2025.1555991
    Crossref
  8. Impact of acupuncture on mortality in patients with disabilities and newly diagnosed heart failure: a nationwide cohort study, Frontiers in Medicine, 12, (2025).https://doi.org/10.3389/fmed.2025.1519588
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  9. Impact of comorbid constipation on the survival of patients with heart failure: a multicenter, prospective cohort study conducted in Japan, Frontiers in Cardiovascular Medicine, 11, (2025).https://doi.org/10.3389/fcvm.2024.1470216
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  10. The role of cardiovascular aging in heart failure, Asia-Pacific Journal of Surgical & Experimental Pathology, (9-18), (2025).https://doi.org/10.32948/ajsep.2025.01.10
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