Identification of distinct phenotypic clusters in heart failure with preserved ejection fraction

Clustering of Cardiovascular Risk Factors and Heart Failure in Older Adults from the Brazilian Far North

Given the aging global population, identifying heart failure (HF) phenotypes has become crucial, as distinct disease characteristics can influence treatment and prognosis in older adults. This study aimed to analyze the association between clustering of cardiovascular risk factors and HF in older adults. A cross-sectional epidemiological study was conducted with 1322 older adults (55% women, mean age 70.4) seen in primary health care. Diagnosis of HF was performed by a cardiologist based on diagnostic tests and medical history. Cardiovascular risk factors included hypertension, diabetes, hypercholesterolemia, and smoking. Using logistic regression, potential associations were tested. Individual risk factor analysis showed that older adults with hypertension, diabetes, or hypercholesterolemia had up to 7.6 times higher odds to have HF. The cluster where older adults had only one risk factor instead of none increased the odds of HF by 53.0%. Additionally, the odds of older patients having HF ranged from 3.59 times for the two-risk factor cluster to 20.61 times for the simultaneous presence of all four factors. The analysis of clusters substantially increasing HF risk in older adults revealed the importance of individualizing subgroups with distinct HF pathophysiologies. The clinical significance of these clusters can be beneficial in guiding a more personalized therapeutic approach.

Heart failure with mildly reduced and preserved ejection fraction: A review of disease burden and remaining unmet medical needs within a new treatment landscape

This review provides a comprehensive overview of heart failure with mildly reduced and preserved ejection fraction (HFmrEF/HFpEF), including its definition, diagnosis, and epidemiology; clinical, humanistic, and economic burdens; current pharmacologic landscape in key pharmaceutical markets; and unmet needs to identify key knowledge gaps. We conducted a targeted literature review in electronic databases and prioritized articles with valuable insights into HFmrEF/HFpEF. Overall, 27 randomized controlled trials (RCTs), 66 real-world evidence studies, 18 clinical practice guidelines, and 25 additional publications were included. Although recent heart failure (HF) guidelines set left ventricular ejection fraction thresholds to differentiate categories, characterization and diagnosis criteria vary because of the incomplete disease understanding. Recent epidemiological data are limited and diverse. Approximately 50% of symptomatic HF patients have HFpEF, more common than HFmrEF. Prevalence varies with country because of differing definitions and study characteristics, making prevalence interpretation challenging. HFmrEF/HFpEF has considerable mortality risk, and the mortality rate varies with study and patient characteristics and treatments. HFmrEF/HFpEF is associated with considerable morbidity, poor patient outcomes, and common comorbidities. Patients require frequent hospitalizations; therefore, early intervention is crucial to prevent disease burden. Recent RCTs show promising results like risk reduction of composite cardiovascular death or HF hospitalization. Costs data are scarce, but the economic burden is increasing. Despite new drugs, unmet medical needs requiring new treatments remain. Thus, HFmrEF/HFpEF is a growing global healthcare concern. With improving yet incomplete understanding of this disease and its promising treatments, further research is required for better patient outcomes.

Heart Failure with Preserved Ejection Fraction in the Elderly: Basic Mechanisms and Clinical Considerations

Heart failure (HF) with preserved ejection fraction (HFpEF) refers to a clinical condition in which the signs of HF, such as pulmonary congestion, peripheral edema and increased natriuretic-peptide levels, are present despite normal ejection-fractions and the absence of other causes (e.g. pericardial disease). The ejection-fraction cutoff for the definition of HFpEF has varied in the past, but recent society guidelines have settled on a consensus of 50%. HFpEF is particularly common in the elderly. The aim of this narrative review is to summarize the available literature regarding HFpEF in the elderly in terms of evidence for the age-dependence, specific clinical features and underlying mechanisms. In the clinical arena, we review the epidemiology, discuss distinct clinical phenotypes typically seen in the elderly, the importance of frailty, the role of biomarkers and the role of medical therapies (including sodium-glucose cotransport protein 2 (SGLT2)-inhibitors, renin-angiotensin-aldosterone system (RAAS) blockers, angiotensin-receptor/neprilysin inhibitors, diuretics and beta-adrenergic receptor blockers). We then go on to discuss the basic mechanisms implicated in HFpEF, including cellular senescence, fibrosis, inflammation, mitochondrial dysfunction, enhanced production of reactive-oxygen species, abnormal cellular calcium handling, changes in microRNA signaling, insulin resistance, and sex-hormone changes. Finally, we review knowledge gaps and promising areas of future investigation. Improved understanding of the specific clinical manifestations of HFpEF in the elderly and of the fundamental mechanisms contributing to the age-related risk of HFpEF promises to lead to novel diagnostic and treatment approaches that will improve outcomes for this common cardiac disorder in a vulnerable population.

The Therapy and Management of Heart Failure with Preserved Ejection Fraction: New Insights on Treatment

Heart failure with preserved ejection fraction (HFpEF) is a clinical syndrome characterised by the presence of diastolic dysfunction and elevated left ventricular filling pressure, in the setting of a left ventricular ejection fraction of at least 50%. Despite the epidemiological prevalence of HFpEF, a prompt diagnosis is challenging and many uncertainties exist. HFpEF is characterised by different phenotypes driven by various cardiac and non-cardiac comorbidities. This is probably the reason why several HFpEF clinical trials in the past did not reach strong outcomes to recommend a single therapy for this syndrome; however, this paradigm has recently changed, and the unmet clinical need for HFpEF treatment found a proper response as a result of a new class of drug, the sodium-glucose cotransporter 2 inhibitors, which beneficially act through the whole spectrum of left ventricular ejection fraction. The aim of this review was to focus on the therapeutic target of HFpEF, the role of new drugs and the potential role of new devices to manage the syndrome.

Does Heterogeneity Exist in Treatment Associations With Renin-Angiotensin-System Inhibitors or Beta-blockers According to Phenotype Clusters in Heart Failure with Preserved Ejection Fraction?

BACKGROUND

We explored the association between use of renin-angiotensin system inhibitors and beta-blockers, with mortality/morbidity in 5 previously identified clusters of patients with heart failure with preserved ejection fraction (HFpEF).

METHODS AND RESULTS

We analyzed 20,980 patients with HFpEF from the Swedish HF registry, phenotyped into young-low comorbidity burden (12%), atrial fibrillation-hypertensive (32%), older-atrial fibrillation (24%), obese-diabetic (15%), and a cardiorenal cluster (17%). In Cox proportional hazard models with inverse probability weighting, there was no heterogeneity in the association between renin-angiotensin system inhibitor use and cluster membership for any of the outcomes: cardiovascular (CV) mortality, all-cause mortality, HF hospitalisation, CV hospitalisation, or non-CV hospitalisation. In contrast, we found a statistical interaction between beta-blocker use and cluster membership for all-cause mortality (P = .03) and non-CV hospitalisation (P = .001). In the young-low comorbidity burden and atrial fibrillation-hypertensive cluster, beta-blocker use was associated with statistically significant lower all-cause mortality and non-CV hospitalisation and in the obese-diabetic cluster beta-blocker use was only associated with a statistically significant lower non-CV hospitalisation. The interaction between beta-blocker use and cluster membership for all-cause mortality could potentially be driven by patients with improved EF. However, patient numbers were diminished when excluding those with improved EF and the direction of the associations remained similar.

CONCLUSIONS

In patients with HFpEF, the association with all-cause mortality and non-CV hospitalisation was heterogeneous across clusters for beta-blockers. It remains to be elucidated how heterogeneity in HFpEF could influence personalized medicine and future clinical trial design.

HFpEF and sex: understanding the role of sex differences

Heart failure is a complex clinical syndrome with many etiological factors and complex pathophysiology affecting millions worldwide. Males and females can have distinct clinical presentation and prognosis, and there is an emerging understanding of the factors that highlight the similarities and differences to synthesize and present available data for sex-specific differences in heart failure with preserved ejection fraction (HFpEF). While the majority of data demonstrate more similarities than differences between females and males in terms of heart failure, there are key differences. Data showed that females have a higher risk of developing HFpEF, but a lower risk of mortality and hospitalization. This can be conditioned by different profiles of comorbidities, postmenopausal changes in sex hormone levels, higher levels of inflammation and chronic microvascular dysfunction in females. These factors, combined with different left ventricular dimensions and function, which are more pronounced with age, lead to a higher prevalence of LV diastolic dysfunction at rest and exercise. As a result, females have lower exercise capacity and quality of life when compared to males. Females also have different activities of systems responsible for drug transformation, leading to different efficacy of drugs as well as higher risk of adverse drug reactions. These data prove the necessity for creating sex-specific risk stratification scales and treatment plans.

Myocardial fibrosis and prognosis in heart failure with preserved ejection fraction: a pooled analysis of 12 cohort studies

OBJECTIVES

Heart failure with preserved ejection fraction (HFpEF) is a syndrome with significant clinical heterogeneity. Myocardial fibrosis has been considered a common pathological process in the development and progress of HFpEF. This study aimed to consolidate data on the prognostic effect of myocardial fibrosis, evaluated by cardiovascular magnetic resonance (CMR) imaging in patients with HFpEF.

METHODS

Three medical databases were searched for potentially related articles up to February 28, 2023. Cohort studies reporting associations between myocardial fibrosis and risk of all-cause mortality or composite major adverse cardiac outcomes (MACE) were included. Cardiac fibrosis was evaluated by CMR metrics, including late gadolinium enhancement (LGE) or myocardial extracellular volume (ECV). The hazard ratios (HRs) and 95% confidence intervals (CI) of the outcomes for higher myocardial fibrosis were calculated.

RESULTS

Twelve studies with 2787 patients with HFpEF were included for analysis. After a median follow-up duration of 31.2 months, a higher level of cardiac fibrosis was associated with a significant increase in the risk of MACE (HR = 1.34, 95% CI = 1.14-1.57) and all-cause mortality (HR = 1.74, 95% CI = 1.27-2.39), respectively. Furthermore, the increased risk of outcomes was both observed when cardiac fibrosis was defined according to LGE or ECV, respectively.

CONCLUSIONS

Higher burden of myocardial fibrosis evaluated by CMR can predict a poor prognosis in patients with HFpEF. Evaluation of LGE or ECV based on CMR could be recommended in these patients for risk stratification and guiding further treatment.

CLINICAL RELEVANCE STATEMENT

Inclusion of cardiovascular magnetic resonance examination in the diagnostic and risk-evaluation algorithms in patients with heart failure with preserved ejection fraction should be considered in clinical practice and future studies.

KEY POINTS

• Myocardial fibrosis is a common pathological process in heart failure with preserved ejection fraction. • A higher myocardial fibrosis burden on cardiac magnetic resonance predicts a poor prognosis in patients with heart failure with preserved ejection fraction. • Evaluation of myocardial fibrosis may be useful in patients with heart failure with preserved ejection fraction for risk stratification and treatment guidance.

Advance in the pharmacological and comorbidities management of heart failure with preserved ejection fraction: evidence from clinical trials

The prevalence of heart failure with preserved ejection fraction (HFpEF) accounts for approximately 50% of the total heart failure population, and with the aging of the population and the increasing prevalence of hypertension, obesity, and type 2 diabetes (T2DM), the incidence of HFpEF continues to rise and has become the most common subtype of heart failure. Compared with heart failure with reduced ejection fraction, HFpEF has a more complex pathophysiology and is more often associated with hypertension, T2DM, obesity, atrial fibrillation, renal insufficiency, pulmonary hypertension, obstructive sleep apnea, and other comorbidities. HFpEF has generally been considered a syndrome with high phenotypic heterogeneity, and no effective treatments have been shown to reduce mortality to date. Diuretics and comorbidity management are traditional treatments for HFpEF; however, they are mostly empirical due to a lack of clinical evidence in the setting of HFpEF. With the EMPEROR-Preserved and DELIVER results, sodium-glucose cotransporter 2 inhibitors become the first evidence-based therapies to reduce rehospitalization for heart failure. Subgroup analyses of the PARAGON-HF, TOPCAT, and CHARM-Preserved trials suggest that angiotensin receptor-neprilysin inhibitors, spironolactone, and angiotensin II receptor blockers may be beneficial in patients at the lower end of the ejection fraction spectrum. Other potential pharmacotherapies represented by non-steroidal mineralocorticoid receptor antagonists finerenone and antifibrotic agent pirfenidone also hold promise for the treatment of HFpEF. This article intends to review the clinical evidence on current pharmacotherapies of HFpEF, as well as the comorbidities management of atrial fibrillation, hypertension, T2DM, obesity, pulmonary hypertension, renal insufficiency, obstructive sleep apnea, and iron deficiency, to optimize the clinical management of HFpEF.

Heart failure registries - Future directions

Heart failure (HF) is a growing, global public health issue. Despite advances in HF care, many challenges remain and HF outcomes are poor. Some of the major reasons for this are the lack of understanding and treatment for certain HF sub-types as well as the lack of implementation of treatment in areas where effective treatment exists. HF registries provide the opportunity to transform clinical research and patient care. Recently the registry-based randomized clinical trial has emerged as a pragmatic and inexpensive alternative to the gold standard in clinical trial design, the randomized controlled trial. Registries may also provide platforms for strategy trials, implementation trials, and screening. Using examples from the Swedish Heart Failure Registry and others, the present review provides insights into how registry-based research can address many of the unmet needs in HF.

Phenotyping of heart failure with preserved ejection faction using electronic health records and echocardiography

Aims

Patients presenting symptoms of heart failure with preserved ejection fraction (HFpEF) are not a homogenous population. Different phenotypes can differ in prognosis and optimal management strategies. We sought to identify phenotypes of HFpEF by using the medical information database from a large university hospital centre using machine learning.

Methods and results

We explored the use of clinical variables from electronic health records in addition to echocardiography to identify different phenotypes of patients with HFpEF. The proposed methodology identifies four phenotypic clusters based on both clinical and echocardiographic characteristics, which have differing prognoses (death and cardiovascular hospitalization).

Conclusion

This work demonstrated that artificial intelligence-derived phenotypes could be used as a tool for physicians to assess risk and to target therapies that may improve outcomes.

Asymptomatic vs. symptomatic atrial fibrillation: Clinical outcomes in heart failure patients

BACKGROUND

The outcome implications of asymptomatic vs. symptomatic atrial fibrillation (AF) in specific groups of patients according to clinical heart failure (HF) and left ventricular ejection fraction (LVEF) need to be clarified.

METHODS

In a prospective observational study, patients were categorized according to overt HF with LVEF≤40 %, or with LVEF>40 %, or without overt HF with LVEF40 %≤ or > 40 %, as well as according to the presence of asymptomatic or symptomatic AF.

RESULTS

A total of 8096 patients, divided into 8 groups according to HF and LVEF, were included with similar proportions of asymptomatic AF (ranging from 43 to 48 %). After a median follow-up of 730 [699 -748] days, the composite outcome (all-cause death and MACE) was significantly worse for patients with asymptomatic AF associated with HF and reduced LVEF vs. symptomatic AF patients of the same group (p = 0.004). On adjusted Cox regression analysis, asymptomatic AF patients with HF and reduced LVEF were independently associated with a higher risk for the composite outcome (aHR 1.32, 95 % CI 1.04-1.69) and all-cause death (aHR 1.33, 95 % CI 1.02-1.73) compared to symptomatic AF patients with HF and reduced LVEF. Kaplan-Meier curves showed that HF-LVEF≤40 % asymptomatic patients had the highest cumulative incidence of all-cause death and MACE (p < 0.001 for both).

CONCLUSIONS

In a large European cohort of AF patients, the risk of the composite outcome at 2 years was not different between asymptomatic and symptomatic AF in the whole cohort but adverse implications for poor outcomes were found for asymptomatic AF in HF with LVEF≤40 %.

Rationale and design of the ESC Heart Failure III Registry - Implementation and discovery

AIMS

Heart failure outcomes remain poor despite advances in therapy. The European Society of Cardiology Heart Failure III Registry (ESC HF III Registry) aims to characterize HF clinical features and outcomes and to assess implementation of guideline-recommended therapy in Europe and other ESC affiliated countries.

METHODS

Between 1 November 2018 and 31 December 2020, 10 162 patients with chronic or acute/worsening HF with reduced, mildly reduced, or preserved ejection fraction were enrolled from 220 centres in 41 European or ESC affiliated countries. The ESC HF III Registry collected data on baseline characteristics (hospital or clinic presentation), hospital course, diagnostic and therapeutic decisions in hospital and at the clinic visit; and on outcomes at 12-month follow-up. These data include demographics, medical history, physical examination, biomarkers and imaging, quality of life, treatments, and interventions - including drug doses and reasons for non-use, and cause-specific outcomes.

CONCLUSION

The ESC HF III Registry will provide comprehensive and unique insight into contemporary HF characteristics, treatment implementation, and outcomes, and may impact implementation strategies, clinical discovery, trial design, and public policy.

Discovering Distinct Phenotypical Clusters in Heart Failure Across the Ejection Fraction Spectrum: a Systematic Review

REVIEW PURPOSE

This systematic review aims to summarise clustering studies in heart failure (HF) and guide future clinical trial design and implementation in routine clinical practice.

FINDINGS

34 studies were identified (n = 19 in HF with preserved ejection fraction (HFpEF)). There was significant heterogeneity invariables and techniques used. However, 149/165 described clusters could be assigned to one of nine phenotypes: 1) young, low comorbidity burden; 2) metabolic; 3) cardio-renal; 4) atrial fibrillation (AF); 5) elderly female AF; 6) hypertensive-comorbidity; 7) ischaemic-male; 8) valvular disease; and 9) devices. There was room for improvement on important methodological topics for all clustering studies such as external validation and transparency of the modelling process. The large overlap between the phenotypes of the clustering studies shows that clustering is a robust approach for discovering clinically distinct phenotypes. However, future studies should invest in a phenotype model that can be implemented in routine clinical practice and future clinical trial design. HF = heart failure, EF = ejection fraction, HFpEF = heart failure with preserved ejection fraction, HFrEF = heart failure with reduced ejection fraction, CKD = chronic kidney disease, AF = atrial fibrillation, IHD = ischaemic heart disease, CAD = coronary artery disease, ICD = implantable cardioverter-defibrillator, CRT = cardiac resynchronization therapy, NT-proBNP = N-terminal pro b-type natriuretic peptide, BMI = Body Mass Index, COPD = Chronic obstructive pulmonary disease.

Advances in the design, generation, and application of tissue-engineered myocardial equivalents

Due to the limited regenerative ability of cardiomyocytes, the disabling irreversible condition of myocardial failure can only be treated with conservative and temporary therapeutic approaches, not able to repair the damage directly, or with organ transplantation. Among the regenerative strategies, intramyocardial cell injection or intravascular cell infusion should attenuate damage to the myocardium and reduce the risk of heart failure. However, these cell delivery-based therapies suffer from significant drawbacks and have a low success rate. Indeed, cardiac tissue engineering efforts are directed to repair, replace, and regenerate native myocardial tissue function. In a regenerative strategy, biomaterials and biomimetic stimuli play a key role in promoting cell adhesion, proliferation, differentiation, and neo-tissue formation. Thus, appropriate biochemical and biophysical cues should be combined with scaffolds emulating extracellular matrix in order to support cell growth and prompt favorable cardiac microenvironment and tissue regeneration. In this review, we provide an overview of recent developments that occurred in the biomimetic design and fabrication of cardiac scaffolds and patches. Furthermore, we sift in vitro and in situ strategies in several preclinical and clinical applications. Finally, we evaluate the possible use of bioengineered cardiac tissue equivalents as in vitro models for disease studies and drug tests.

Retrospective comparison of traditional and artificial intelligence-based heart failure phenotyping in a US health system to enable real-world evidence

OBJECTIVE

Quantitatively evaluate the quality of data underlying real-world evidence (RWE) in heart failure (HF).

DESIGN

Retrospective comparison of accuracy in identifying patients with HF and phenotypic information was made using traditional (ie, structured query language applied to structured electronic health record (EHR) data) and advanced (ie, artificial intelligence (AI) applied to unstructured EHR data) RWE approaches. The performance of each approach was measured by the harmonic mean of precision and recall (F1 score) using manual annotation of medical records as a reference standard.

SETTING

EHR data from a large academic healthcare system in North America between 2015 and 2019, with an expected catchment of approximately 5 00 000 patients.

POPULATION

4288 encounters for 1155 patients aged 18-85 years, with 472 patients identified as having HF.

OUTCOME MEASURES

HF and associated concepts, such as comorbidities, left ventricular ejection fraction, and selected medications.

RESULTS

The average F1 scores across 19 HF-specific concepts were 49.0% and 94.1% for the traditional and advanced approaches, respectively (p<0.001 for all concepts with available data). The absolute difference in F1 score between approaches was 45.1% (98.1% relative increase in F1 score using the advanced approach). The advanced approach achieved superior F1 scores for HF presence, phenotype and associated comorbidities. Some phenotypes, such as HF with preserved ejection fraction, revealed dramatic differences in extraction accuracy based on technology applied, with a 4.9% F1 score when using natural language processing (NLP) alone and a 91.0% F1 score when using NLP plus AI-based inference.

CONCLUSIONS

A traditional RWE generation approach resulted in low data quality in patients with HF. While an advanced approach demonstrated high accuracy, the results varied dramatically based on extraction techniques. For future studies, advanced approaches and accuracy measurement may be required to ensure data are fit-for-purpose.

Inflammation as a therapeutic target in heart failure with preserved ejection fraction

Heart failure with preserved ejection fraction (HFpEF) accounts for around half of all cases of heart failure and may become the dominant type of heart failure in the near future. Unlike HF with reduced ejection fraction there are few evidence-based treatment strategies available. There is a significant unmet need for new strategies to improve clinical outcomes in HFpEF patients. Inflammation is widely thought to play a key role in HFpEF pathophysiology and may represent a viable treatment target. In this review focusing predominantly on clinical studies, we will summarise the role of inflammation in HFpEF and discuss potential therapeutic strategies targeting inflammation.

Identifying distinct clinical clusters in heart failure with mildly reduced ejection fraction

INTRODUCTION

Heart failure (HF) is a heterogeneous syndrome, and the specific sub-category HF with mildly reduced ejection fraction (EF) range (HFmrEF; 41-49% EF) is only recently recognized as a distinct entity. Cluster analysis can characterize heterogeneous patient populations and could serve as a stratification tool in clinical trials and for prognostication. The aim of this study was to identify clusters in HFmrEF and compare cluster prognosis.

METHODS AND RESULTS

Latent class analysis to cluster HFmrEF patients based on their characteristics was performed in the Swedish HF registry (n = 7316). Identified clusters were validated in a Dutch cross-sectional HF registry-based dataset CHECK-HF (n = 1536). In Sweden, mortality and hospitalisation across the clusters were compared using a Cox proportional hazard model, with a Fine-Gray sub-distribution for competing risks and adjustment for age and sex. Six clusters were discovered with the following prevalence and hazard ratio with 95% confidence intervals (HR [95%CI]) vs. cluster 1: 1) low-comorbidity (17%, reference), 2) ischaemic-male (13%, HR 0.9 [95% CI 0.7-1.1]), 3) atrial fibrillation (20%, HR 1.5 [95% CI 1.2-1.9]), 4) device/wide QRS (9%, HR 2.7 [95% CI 2.2-3.4]), 5) metabolic (19%, HR 3.1 [95% CI 2.5-3.7]) and 6) cardio-renal phenotype (22%, HR 2.8 [95% CI 2.2-3.6]). The cluster model was robust between both datasets.

CONCLUSION

We found robust clusters with potential clinical meaning and differences in mortality and hospitalisation. Our clustering model could be valuable as a clinical differentiation support and prognostic tool in clinical trial design.

Clinical profiling of end-stage heart failure with preserved ejection fraction: The National Readmission Database

BACKGROUND

End-stage (Stage D) heart failure with preserved ejection fraction (HFpEF) is a poorly characterized syndrome that has heterogeneous underlying pathophysiology. A better characterization of the various clinical profiles of Stage D HFpEF is needed.

METHOD

1066 patients with Stage D HFpEF were selected from National Readmission Database. A Bayesian clustering algorithm based on a Dirichlet process mixture model was implemented. Cox proportional hazard regression model was used to relate the risk of in-hospital mortality with each identified clinical cluster.

RESULT

4 distinct clinical clusters were recognized. Group 1 had a higher prevalence of obesity (84.5%) and sleep disorders (62.0%). Group 2 had a higher prevalence of diabetes mellitus (92%), chronic kidney disease (98.3%), anemia (72.6%), and coronary artery disease (59.0%). Group 3 had a higher prevalence of advanced age (82.1%), hypothyroidism (28.9%), dementia (17.0%), atrial fibrillation (63.8%) and valvular disease (30.5%) and Group 4 had a higher prevalence of liver disease (44.5%), right-sided HF (20.2%) and amyloidosis (4.5%). During 2019, 193 (18.1%) in-hospital mortality events occurred. Considering Group 1 (with mortality rate of 4.1%) as a reference, the hazard ratio of in-hospital mortality was 5.4 [95% confidence interval (CI): 2.2-13.6] for Group 2, 6.4 (95% CI: 2.6-15.8) for Group 3 and 9.1 (95% CI: 3.5-23.8) for Group 4.

CONCLUSION

End-stage HFpEF presents with different clinical profiles with varied upstream causes. This may help provide evidence toward the development of targeted therapies.

Personalized Management for Heart Failure with Preserved Ejection Fraction

Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome with multiple underlying mechanisms and comorbidities that leads to a variety of clinical phenotypes. The identification and characterization of these phenotypes are essential for better understanding the precise pathophysiology of HFpEF, identifying appropriate treatment strategies, and improving patient outcomes. Despite accumulating data showing the potentiality of artificial intelligence (AI)-based phenotyping using clinical, biomarker, and imaging information from multiple dimensions in HFpEF management, contemporary guidelines and consensus do not incorporate these in daily practice. In the future, further studies are required to authenticate and substantiate these findings in order to establish a more standardized approach for clinical implementation.

Redefining clinical venous thromboembolism phenotypes: a novel approach using latent class analysis

BACKGROUND

Patients with venous thromboembolism (VTE) are commonly classified by the presence or absence of provoking factors at the time of VTE to guide treatment decisions. This approach may not capture the heterogeneity of the disease and its prognosis.

OBJECTIVES

To evaluate clinically important novel phenotypic clusters among patients with VTE without cancer and to explore their association with anticoagulant treatment and clinical outcomes.

METHODS

Latent class analysis was performed with 18 baseline clinical variables in 3062 adult patients with VTE without active cancer participating in PREFER in VTE, a noninterventional disease registry. The derived latent classes were externally validated in a post hoc analysis of Hokusai-VTE (n = 6593), a randomized trial comparing edoxaban with warfarin. The associations between cluster membership and anticoagulant treatment, recurrent VTE, bleeding, and mortality after initial treatment were studied.

RESULTS

The following 5 clusters were identified: young men cluster (n = 1126, 37%), young women cluster (n = 215, 7%), older people cluster (n = 1106, 36%), comorbidity cluster (n = 447, 15%), and history of venous thromboembolism cluster (n = 168, 5%). Patient characteristics varied by age, sex, medical history, and treatment patterns. Consistent clusters were evident on external validation. In Cox proportional hazard models, recurrence risk was lower in the young women cluster (hazard ratio [HR], 0.27; 95% CI, 0.12-0.61) compared with the comorbidity cluster, after adjusting for extended anticoagulation. The risk of bleeding was lower in young men, young women, and older people clusters (HR, 0.50; 95% CI, 0.38-0.66; HR, 0.23; 95% CI, 0.11-0.46; and HR, 0.55; 95% CI 0.41-0.73, respectively).

CONCLUSION

The heterogeneity of VTE cases extends beyond the distinction between provoked and unprovoked VTE.

Phenomapping in heart failure with preserved ejection fraction: insights, limitations, and future directions

Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous entity with complex pathophysiology and manifestations. Phenomapping is the process of applying statistical learning techniques to patient data to identify distinct subgroups based on patterns in the data. Phenomapping has emerged as a technique with potential to improve the understanding of different HFpEF phenotypes. Phenomapping efforts have been increasing in HFpEF over the past several years using a variety of data sources, clinical variables, and statistical techniques. This review summarizes methodologies and key takeaways from these studies, including consistent discriminating factors and conserved HFpEF phenotypes. We argue that phenomapping results to date have had limited implications for clinical care and clinical trials, given that the phenotypes, as currently described, are not reliably identified in each study population and may have significant overlap. We review the inherent limitations of aggregating and utilizing phenomapping results. Lastly, we discuss potential future directions, including using phenomapping to optimize the likelihood of clinical trial success or to drive discovery in mechanisms of the disease process of HFpEF.

Using Latent Class Analysis to Identify Different Clinical Profiles Among Patients With Advanced Heart Failure

Context

Although palliative care is guideline-indicated for patients with advanced heart failure (HF), the scarcity of a specialty-trained palliative care workforce demands better identification of patients who are most burdened by the disease.

Objectives

We sought to identify latent subgroups with variations regarding symptom burden, functional status, and multimorbidity in an advanced HF population.

Methods

We performed a latent class analysis (LCA) of baseline data from a trial enrolling advanced HF patients. As LCA input variables, we chose indicators of HF severity, physical and psychological symptom burden, functional status, and the number of comorbidities.

Results

Among 563 patients, two subgroups emerged from LCA, Class A (352 [62.5%]) and Class B (211 [37.5%]). Patients in Class A were less often classified as NYHA class III or IV (88.0% vs. 97.5%, P < 0.001), as compared to Class B patients. Class A patients had fewer symptoms, fewer comorbidities, only 25.9% had impairments in activities of daily living (ADL), and virtually none suffered from clinically significant anxiety (0.4%) or depression (0.9%). In Class B, every patient reported more than three symptoms, almost all patients (92.6%) had some impairment in ADL, and nearly a third had anxiety (30.2%) or depression (28.3%). All-cause mortality after 12 months was higher in Class B, as compared to Class A (18.5% vs. 12.5%, P = 0.047).

Conclusion

Among advanced HF patients, we identified a distinct subgroup characterized by a conjunction of high symptom burden, anxiety, depression, multimorbidity, and functional status impairment, which might profit particularly from palliative care interventions. J Pain Symptom Manage 2022;000:1-9.

Increased Soluble Epoxide Hydrolase Activity Positively Correlates with Mortality in Heart Failure Patients with Preserved Ejection Fraction: Evidence from Metabolomics

Epoxyeicosatrienoic acids (EETs) have pleiotropic endogenous cardiovascular protective effects and can be hydrolyzed to the corresponding dihydroxyeicosatrienoic acids by soluble epoxide hydrolase (sEH). Heart failure with preserved ejection fraction (HFpEF) has shown an increased prevalence and worse prognosis over the decades. However, the role of sEH activity in HFpEF remains unclear. We enrolled 500 patients with HFpEF and 500 healthy controls between February 2010 and March 2016. Eight types of sEH-related eicosanoids were measured according to target metabolomics, and their correlation with clinical endpoints was also analyzed. The primary endpoint was cardiac mortality, and the secondary endpoint was a composite of cardiac events, including heart failure (HF) readmission, cardiogenic hospitalization, and all-cause mortality. Furthermore, the effect of sEH inhibitors on cardiac diastolic function in HFpEF was investigated in vivo and in vitro. Patients with HFpEF showed significantly enhanced EET degradation by the sEH enzyme compared with healthy controls. More importantly, sEH activity was positively correlated with cardiac mortality in patients with HFpEF, especially in older patients with arrhythmia. A consistent result was obtained in the multiple adjusted models. Decreased sEH activity by the sEH inhibitor showed a significant effective effect on the improvement of cardiac diastolic function by ameliorating lipid disorders in cardiomyocytes of HFpEF mouse model. This study demonstrated that increased sEH activity was associated with cardiac mortality in patients with HFpEF and suggested that sEH inhibition could be a promising therapeutic strategy to improve diastolic cardiac function. Clinical trial identifier: NCT03461107 (https://clinicaltrials.gov).

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-022-00069-8.

Does the Measurement of Ejection Fraction Still Make Sense in the HFpEF Framework? What Recent Trials Suggest

Left ventricular ejection fraction (LVEF) is universally accepted as a cardiac systolic function index and it provides intuitive interpretation of cardiac performance. Over the last two decades, it has erroneously become the leading feature used by clinicians to characterize the left ventricular function in heart failure (HF). Notably, LVEF sets the basis for structural and functional HF phenotype classification in current guidelines. However, its diagnostic and prognostic role in patients with preserved or mildly reduced contractile function is less clear. This is related to several concerns due to intrinsic technical, methodological and hemodynamic limitations entailed in LVEF measurement that do not describe the chamber's real contractile performance as expressed by pressure volume loop relationship. In patients with HF and preserved ejection fraction (HFpEF), it does not reflect the effective systolic function because it is prone to preload and afterload variability and it does not account for both longitudinal and torsional contraction. Moreover, a repetitive measurement could be assessed over time to better identify HF progression related to natural evolution of disease and to the treatment response. Current gaps may partially explain the causes of negative or neutral effects of traditional medical agents observed in HFpEF. Nevertheless, recent pooled analysis has evidenced the positive effects of new therapies across the LVEF range, suggesting a potential role irrespective of functional status. Additionally, a more detailed analysis of randomized trials suggests that patients with higher LVEF show a risk reduction strictly related to overall cardiovascular (CV) events; on the other hand, patients experiencing lower LVEF values have a decrease in HF-related events. The current paper reports the main limitations and shortcomings in LVEF assessment, with specific focus on patients affected by HFpEF, and it suggests alternative measurements better reflecting the real hemodynamic status. Future investigations may elucidate whether the development of non-invasive stroke volume and longitudinal function measurements could be extensively applied in clinical trials for better phenotyping and screening of HFpEF patients.

Laboratory and Metabolomic Fingerprint in Heart Failure with Preserved Ejection Fraction: From Clinical Classification to Biomarker Signature

Heart failure with preserved ejection fraction (HFpEF) remains a poorly characterized syndrome with many unknown aspects related to different patient profiles, various associated risk factors and a wide range of aetiologies. It comprises several pathophysiological pathways, such as endothelial dysfunction, myocardial fibrosis, extracellular matrix deposition and intense inflammatory system activation. Until now, HFpEF has only been described with regard to clinical features and its most commonly associated risk factors, disregarding all biological mechanisms responsible for cardiovascular deteriorations. Recently, innovations in laboratory and metabolomic findings have shown that HFpEF appears to be strictly related to specific cells and molecular mechanisms' dysregulation. Indeed, some biomarkers are efficient in early identification of these processes, adding new insights into diagnosis and risk stratification. Moreover, recent advances in intermediate metabolites provide relevant information on intrinsic cellular and energetic substrate alterations. Therefore, a systematic combination of clinical imaging and laboratory findings may lead to a 'precision medicine' approach providing prognostic and therapeutic advantages. The current review reports traditional and emerging biomarkers in HFpEF and it purposes a new diagnostic approach based on integrative information achieved from risk factor burden, hemodynamic dysfunction and biomarkers' signature partnership.

Novel metabolic phenotypes for extrahepatic complication of nonalcoholic fatty liver disease

BACKGROUND AND AIMS

Phenotypic heterogeneity among patients with NAFLD is poorly understood. We aim to identify clinically important phenotypes within NAFLD patients and assess the long-term outcomes among different phenotypes.

METHODS

We analyzed the clinical data of 2311 participants from the Third National Health and Nutrition Examination Survey (NHANES III) and their linked mortality data through December 2019. NAFLD was diagnosed by ultrasonographic evidence of hepatic steatosis without other liver diseases and excess alcohol use. A 2-stage cluster analysis was applied to identify clinical phenotypes. We used Cox proportional hazard models to explore all-cause and cause-specific mortality between clusters.

RESULTS

We identified 3 NAFLD phenotypes. Cluster 1 was characterized by young female patients with better metabolic profiles and lower prevalence of comorbidities; Cluster 2 by obese females with significant insulin resistance, diabetes, inflammation, and advanced fibrosis and Cluster 3 by male patients with hypertension, atherogenic dyslipidemia, and liver and kidney damage. In a median follow-up of 26 years, 989 (42.8%) all-cause mortality occurred. Cluster 1 patients presented the best prognosis, whereas Cluster 2 and 3 had higher risks of all-cause (Cluster 2-adjusted HR: 1.48, 95% CI: 1.16-1.90; Cluster 3-adjusted HR: 1.29, 95% CI: 1.01-1.64) and cardiovascular (Cluster 2-adjusted HR: 2.01, 95% CI: 1.18-3.44; Cluster 3-adjusted HR: 1.75, 95% CI: 1.03-2.97) mortality.

CONCLUSIONS

Three phenotypically distinct and clinically meaningful NAFLD subgroups have been identified with different characteristics of metabolic profiles. This study reveals the substantial disease heterogeneity that exists among NAFLD patients and underscores the need for granular assessments to define phenotypes and improve clinical practice.

Genetic architecture of heart failure with preserved versus reduced ejection fraction

Pharmacologic clinical trials for heart failure with preserved ejection fraction have been largely unsuccessful as compared to those for heart failure with reduced ejection fraction. Whether differences in the genetic underpinnings of these major heart failure subtypes may provide insights into the disparate outcomes of clinical trials remains unknown. We utilize a large, uniformly phenotyped, single cohort of heart failure sub-classified into heart failure with reduced and with preserved ejection fractions based on current clinical definitions, to conduct detailed genetic analyses of the two heart failure sub-types. We find different genetic architectures and distinct genetic association profiles between heart failure with reduced and with preserved ejection fraction suggesting differences in underlying pathobiology. The modest genetic discovery for heart failure with preserved ejection fraction (one locus) compared to heart failure with reduced ejection fraction (13 loci) despite comparable sample sizes indicates that clinically defined heart failure with preserved ejection fraction likely represents the amalgamation of several, distinct pathobiological entities. Development of consensus sub-phenotyping of heart failure with preserved ejection fraction is paramount to better dissect the underlying genetic signals and contributors to this highly prevalent condition.

Machine learning to define phenotypes and outcomes of patients hospitalized for heart failure with preserved ejection fraction: Findings from ASCEND-HF

BACKGROUND

Heart Failure with Preserved Ejection Fraction (HFpEF) is a heterogenous disease with few therapies proven to provide clinical benefit. Machine learning can characterize distinct phenotypes and compare outcomes among patients with HFpEF who are hospitalized for acute HF.

METHODS

We applied hierarchical clustering using demographics, comorbidities, and clinical data on admission to identify distinct clusters in hospitalized HFpEF (ejection fraction >40%) in the ASCEND-HF trial. We separately applied a previously developed latent class analysis (LCA) clustering method and compared in-hospital and long-term outcomes across cluster groups.

RESULTS

Of 7141 patients enrolled in the ASCEND-HF trial, 812 (11.4%) were hospitalized for HFpEF and met the criteria for complete case analysis. Hierarchical Cluster 1 included older women with atrial fibrillation (AF). Cluster 2 had elevated resting blood pressure. Cluster 3 had young men with obesity and diabetes. Cluster 4 had low resting blood pressure. Mortality at 180 days was lowest among Cluster 3 (KM event-rate 6.2 [95% CI: 3.5, 10.9]) and highest among Cluster 4 (18.8 [14.6, 24.0], P < .001). Twenty four-hour urine output was higher in Cluster 3 (2700 mL [1800, 3975]) than Cluster 4 (2100 mL [1400, 3055], P < .001). LCA also identified four clusters: A) older White or Asian women, B) younger men with few comorbidities, C) older individuals with AF and renal impairment, and D) patients with obesity and diabetes. Mortality at 180 days was lowest among LCA Cluster B (KM event-rate 5.5 [2.0, 10.3]) and highest among LCA Cluster C (26.3 [19.2, 35.4], P < .001).

CONCLUSIONS

In patients hospitalized for HFpEF, cluster analysis demonstrated distinct phenotypes with differing clinical profiles and outcomes.

Heart failure: an update from the last years and a look at the near future

In the last years, major progress occurred in heart failure (HF) management. Quadruple therapy is now mandatory for all the patients with HF with reduced ejection fraction. Whilst verciguat is becoming available across several countries, omecamtiv mecarbil is waiting to be released for clinical use. Concurrent use of potassium-lowering agents may counteract hyperkalaemia and facilitate renin-angiotensin-aldosterone system inhibitor implementations. The results of the EMPagliflozin outcomE tRial in Patients With chrOnic heaRt Failure With Preserved Ejection Fraction (EMPEROR-Preserved) trial were confirmed by the Dapagliflozin in Heart Failure with Mildly Reduced or Preserved Ejection Fraction (DELIVER) trial, and we now have, for the first time, evidence for treatment of also patients with HF with preserved ejection fraction. In a pre-specified meta-analysis of major randomized controlled trials, sodium-glucose co-transporter-2 inhibitors reduced all-cause mortality, cardiovascular (CV) mortality, and HF hospitalization in the patients with HF regardless of left ventricular ejection fraction. Other steps forward have occurred in the treatment of decompensated HF. Acetazolamide in Acute Decompensated Heart Failure with Volume Overload (ADVOR) trial showed that the addition of intravenous acetazolamide to loop diuretics leads to greater decongestion vs. placebo. The addition of hydrochlorothiazide to loop diuretics was evaluated in the CLOROTIC trial. Torasemide did not change outcomes, compared with furosemide, in TRANSFORM-HF. Ferric derisomaltose had an effect on the primary outcome of CV mortality or HF rehospitalizations in IRONMAN (rate ratio 0.82; 95% confidence interval 0.66-1.02; P = 0.070). Further options for the treatment of HF, including device therapies, cardiac contractility modulation, and percutaneous treatment of valvulopathies, are summarized in this article.

Distinct implications of body mass index in different subgroups of nonobese patients with heart failure with preserved ejection fraction: a latent class analysis of data from the TOPCAT trial

BACKGROUND

Obesity is a well-defined risk factor for heart failure with preserved ejection fraction (HFpEF), but it is associated with a better prognosis in patients with diagnosed HFpEF. The paradoxically poor prognosis in nonobese patients with HFpEF may be driven by a subset of high-risk patients, which suggests that the nonobese HFpEF subpopulation is heterogeneous.

METHODS

Latent class analysis (LCA) was adopted to identify the potential subgroups of 623 nonobese patients enrolled in the TOPCAT trial. The baseline characteristics of the identified nonobese subgroups were compared with each other and with the obese patients. The risks of all-cause, cardiovascular, and noncardiovascular mortality, and an HF composite outcome were also compared.

RESULTS

Two subgroups of nonobese patients with HFpEF (the physiological non-obesity and the pathological non-obesity) were identified. The obese patients were younger than both nonobese subgroups. The clinical profile of patients with pathological non-obesity was poorer than that of patients with physiological non-obesity. They had more comorbidities, more severe HF, poorer quality of life, and lower levels of physical activity. Patients with pathological non-obesity showed low serum hemoglobin and albumin levels. After 2 years of follow-up, more patients in the pathological group lost ≥ 10% of body weight compared with those in the physiological group (11.34% vs. 4.19%, P = 0.009). The prognostic implications of the two subgroups were opposite. Compared to patients with obesity, patients with physiological non-obesity had a 47% decrease in the risk of HF composite outcome (hazard ratio [HR] 0.53, 95% confidence interval [CI] 0.40-0.70, P<0.001) and a trend of decreased all-cause mortality risk (HR 0.75, 95% CI 0.55-1.01, P=0.06), while patients with pathological non-obesity had a 59% increase (HR 1.59, 95% CI 1.24-2.02, P<0.001) in all-cause mortality risk.

CONCLUSIONS

Two subgroups of nonobese patients with HFpEF with distinct clinical profiles and prognostic implications were identified. The low BMI was likely physiological in one group but pathological in the other group. Using a data-driven approach, our study provided an alternative explanation for the "obesity paradox" that the poor prognosis of nonobese patients with HFpEF was driven by a pathological subgroup.

Defining the Phenotypes for Heart Failure With Preserved Ejection Fraction

PURPOSE OF REVIEW

Heart failure with preserved ejection fraction (HFpEF) imposes a significant burden on society and healthcare. The lack in efficacious therapies is likely due to the significant heterogeneity of HFpEF. In this review, we define various phenotypes based on underlying comorbidities or etiologies, discuss phenotypes arrived at by novel methods, and explore therapeutic targets.

RECENT FINDINGS

A few studies have used machine learning methods to uncover sub-phenotypes within HFpEF in an unbiased manner based on clinical features, echocardiographic findings, and biomarker levels. We synthesized the literature and propose three broad phenotypes: (1) young, with few comorbidities, usually obese and with low natriuretic peptide levels, (2) obese with substantive cardiometabolic burden and comorbidities and impaired ventricular relaxation, (3) old, multimorbid, with high rates of atrial fibrillation, renal and coronary artery disease, chronic obstructive pulmonary disease, and left ventricular hypertrophy. We also propose potential therapeutic strategies for these phenotypes.

The Conundrum of HFpEF Definition: Non-Invasive Assessment Uncertainties and Alternative Diagnostic Strategies

Heart failure (HF) with preserved ejection fraction (HFpEF) is a heterogeneous syndrome including several morphological phenotypes and varying pathophysiological mechanisms. The conventional classification of HF based on left ventricular ejection fraction (LVEF) has created an oversimplification in diagnostic criteria. Although LVEF is a standardized parameter easy to calculate and broadly applied in the large clinical trials, but it is erroneously considered an index of left ventricular (LV) systolic function. Indeed,  it is affected by preload and afterload and it has limitations related to reproducibility, reduced sensitivity and scarce prognostic values especially when above 50%. Notably, additional diagnostic parameters have been recently proposed in order to improve diagnostic accuracy and to homogenise the different HFpEF populations. Unfortunately, these algorithms comprise sophisticated measurements that are difficult to apply in the daily clinical practice. Additionally, the scarce diffusion of these diagnostic criteria may have led to neutral or negative results in interventional phase 3 trials . We propose changes to the current HFpEF diagnostic approach mainly based on LVEF stratification measurement aiming towards a more inclusive model taking into consideration an integrative approach starting from the main diseases responsible for cardiac dysfunction  through to cardiac structural and functional alterations. Accordingly, with recent universal HF definitions, a stepwise model could be helpful in recognizing patients with early vs. overt HFpEF by the appraisal of specific Doppler echocardiographic variables. Thus, we would encourage the application of new criteria in order to better identify the different phenotypes and to move towards more personalized medicine.

Epidemiology and Clinical Features of Heart Failure with Preserved Ejection Fraction

Heart failure (HF) with preserved ejection (HFpEF) constitutes a large and growing proportion of patients with HF around the world, and is now responsible for more than half of all HF cases in ageing societies. While classically described as a condition of elderly, hypertensive women, recent studies suggest heterogeneity in clinical phenotypes involving differential characteristics and pathophysiological mechanisms. Despite a paucity of disease-modifying therapy for HFpEF, an understanding of phenotypic similarities and differences among patients with HFpEF around the world provides the foundation to recognise the clinical condition for early treatment, as well as to identify modifiable risk factors for preventive intervention. This review summarises the epidemiology of HFpEF, its common clinical features and risk factors, as well as differences by age, comorbidities, race/ethnicity and geography.

[Treatment of patients with heart failure and preserved ejection fraction: reliance on clinical phenotypes]

The article discusses the problem of improving the effectiveness of treatment of heart failure with preserved left ventricular ejection fraction (HFpEF). The relative "failure" of early studies with renin-angiotensin-aldosterone system inhibitors was largely due to the lack of understanding that patients with HFpEF represent a heterogeneous group with various etiological factors and pathogenetic mechanisms of the disease. Therefore, the so-called personalized approach should be used in the treatment of these patients. This approach is based on the identification of clearly defined disease phenotypes, each characterized by a set of demographic, pathogenetic, and clinical characteristics. Based on the literature and own experience, the authors consider four main phenotypes of HFpEF: 1) phenotype with brain natriuretic peptide "deficiency" syndrome associated with moderate/severe left ventricular hypertrophy; 2) cardiometabolic phenotype; 3) phenotype with mixed pulmonary hypertension and right ventricular failure; and 4) cardiac amyloidosis phenotype. In the treatment of patients with phenotype 1, it seems preferable to use the valsartan + sacubitril (possibly in combination with spironolactone) combination treatment; with phenotype 2, the empagliflozin treatment is the best; with phenotype 3, the phosphodiesterase type 5 inhibitor sildenafil; and with phenotype 4, transthyretin stabilizers. Certain features of different phenotypes overlap and may change as the disease progresses. Nevertheless, the isolation of these phenotypes is advisable to prioritize the choice of drug therapy. Thus, the diuretic treatment (preferably torasemide) should be considered in the presence of congestion, regardless of the HFpEF phenotype; the valsartan + sacubitril and spironolactone treatment is appropriate not only in the shortage of brain natriuretic peptide but also in the presence of concentric left ventricular hypertrophy (except for the amyloidosis phenotype); and the treatment with empagliflozin and statins may be considered in all situations where pro-inflammatory mechanisms are involved.

Identifying novel subgroups in heart failure patients with unsupervised machine learning: A scoping review

Background

Heart failure is currently divided into three main forms, HFrEF, HFpEF, and HFmrEF, but its etiology is diverse and highly heterogeneous. Many studies reported a variety of novel subgroups in heart failure patients, with unsupervised machine learning methods. The aim of this scoping review is to provide insights into how these techniques can diagnose and manage HF faster and better, thus providing direction for future research and facilitating its routine use in clinical practice.

Methods

The review was performed following PRISMA-SCR guideline. We searched the PubMed database for eligible publications. Studies were included if they defined new subgroups in HF patients using clustering analysis methods, and excluded if they are (1) Reviews, commentary, or editorials, (2) Studies not about defining new sub-types, or (3) Studies not using unsupervised algorithms. All study screening and data extraction were conducted independently by two investigators and narrative integration of data extracted from included studies was performed.

Results

Of the 498 studies identified, 47 were included in the analysis. Most studies (61.7%) were published in 2020 and later. The largest number of studies (46.8%) coming from the United States, and most of the studies were authored and included in the same country. The most commonly used machine learning method was hierarchical cluster analysis (46.8%), the most commonly used cluster variable type was comorbidity (61.7%), and the least used cluster variable type was genomics (12.8%). Most of the studies used data sets of less than 500 patients (48.9%), and the sample size had negative correlation with the number of clustering variables. The majority of studies (85.1%) assessed the association between cluster grouping and at least one outcomes, with death and hospitalization being the most commonly used outcome measures.

Conclusion

This scoping review provides an overview of recent studies proposing novel HF subgroups based on clustering analysis. Differences were found in study design, study population, clustering methods and variables, and outcomes of interests, and we provided insights into how these studies were conducted and identify the knowledge gaps to guide future research.

Heart failure subphenotypes based on repeated biomarker measurements are associated with clinical characteristics and adverse events (Bio-SHiFT study)

BACKGROUND

This study aimed to identify heart failure (HF) subphenotypes using 92 repeatedly measured circulating proteins in 250 patients with heart failure with reduced ejection fraction, and to investigate their clinical characteristics and prognosis.

METHODS

Clinical data and blood samples were collected tri-monthly until the primary endpoint (PEP) or censoring occurred, with a maximum of 11 visits. The Olink Cardiovascular III panel was measured in baseline samples and the last two samples before the PEP (in 66 PEP cases), or the last sample before censoring (in 184 PEP-free patients). The PEP comprised cardiovascular death, heart transplantation, Left Ventricular Assist Device implantation, and hospitalization for HF. Cluster analysis was performed on individual biomarker trajectories to identify subphenotypes. Then biomarker profiles and clinical characteristics were investigated, and survival analysis was conducted.

RESULTS

Clustering revealed three clinically diverse subphenotypes. Cluster 3 was older, with a longer duration of, and more advanced HF, and most comorbidities. Cluster 2 showed increasing levels over time of most biomarkers. In cluster 3, there were elevated baseline levels and increasing levels over time of 16 remaining biomarkers. Median follow-up was 2.2 (1.4-2.5) years. Cluster 3 had a significantly poorer prognosis compared to cluster 1 (adjusted event-free survival time ratio 0.25 (95%CI:0.12-0.50), p < 0.001). Repeated measurements clusters showed incremental prognostic value compared to clusters using single measurements, or clinical characteristics only.

CONCLUSIONS

Clustering based on repeated biomarker measurements revealed three clinically diverse subphenotypes, of which one has a significantly worse prognosis, therefore contributing to improved (individualized) prognostication.

Clinical Phenotypes of Heart Failure With Preserved Ejection Fraction to Select Preclinical Animal Models

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To better define HFpEF clinically, patients are nowadays often clustered into phenogroups, based on their comorbidities and symptoms

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Many animal models claim to mimic HFpEF, but phenogroups are not yet regularly used to cluster them

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HFpEF animals models often lack reports of clinical symptoms of HF, therefore mainly presenting as extended models of LVDD, clinically seen as a prestate of HFpEF

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We investigated if clinically relevant phenogroups can guide selection of animal models aiming at better defined animal research

At least one-half of the growing heart failure population consists of heart failure with preserved ejection fraction (HFpEF). The limited therapeutic options, the complexity of the syndrome, and many related comorbidities emphasize the need for adequate experimental animal models to study the etiology of HFpEF, as well as its comorbidities and pathophysiological changes. The strengths and weaknesses of available animal models have been reviewed extensively with the general consensus that a “1-size-fits-all” model does not exist, because no uniform HFpEF patient exists. In fact, HFpEF patients have been categorized into HFpEF phenogroups based on comorbidities and symptoms. In this review, we therefore study which animal model is best suited to study the different phenogroups—to improve model selection and refinement of animal research. Based on the published data, we extrapolated human HFpEF phenogroups into 3 animal phenogroups (containing small and large animals) based on reports and definitions of the authors: animal models with high (cardiac) age (phenogroup aging); animal models focusing on hypertension and kidney dysfunction (phenogroup hypertension/kidney failure); and models with hypertension, obesity, and type 2 diabetes mellitus (phenogroup cardiometabolic syndrome). We subsequently evaluated characteristics of HFpEF, such as left ventricular diastolic dysfunction parameters, systemic inflammation, cardiac fibrosis, and sex-specificity in the different models. Finally, we scored these parameters concluded how to best apply these models. Based on our findings, we propose an easy-to-use classification for future animal research based on clinical phenogroups of interest.

Signaling cascades in the failing heart and emerging therapeutic strategies

Chronic heart failure is the end stage of cardiac diseases. With a high prevalence and a high mortality rate worldwide, chronic heart failure is one of the heaviest health-related burdens. In addition to the standard neurohormonal blockade therapy, several medications have been developed for chronic heart failure treatment, but the population-wide improvement in chronic heart failure prognosis over time has been modest, and novel therapies are still needed. Mechanistic discovery and technical innovation are powerful driving forces for therapeutic development. On the one hand, the past decades have witnessed great progress in understanding the mechanism of chronic heart failure. It is now known that chronic heart failure is not only a matter involving cardiomyocytes. Instead, chronic heart failure involves numerous signaling pathways in noncardiomyocytes, including fibroblasts, immune cells, vascular cells, and lymphatic endothelial cells, and crosstalk among these cells. The complex regulatory network includes protein-protein, protein-RNA, and RNA-RNA interactions. These achievements in mechanistic studies provide novel insights for future therapeutic targets. On the other hand, with the development of modern biological techniques, targeting a protein pharmacologically is no longer the sole option for treating chronic heart failure. Gene therapy can directly manipulate the expression level of genes; gene editing techniques provide hope for curing hereditary cardiomyopathy; cell therapy aims to replace dysfunctional cardiomyocytes; and xenotransplantation may solve the problem of donor heart shortages. In this paper, we reviewed these two aspects in the field of failing heart signaling cascades and emerging therapeutic strategies based on modern biological techniques.

Minimal subphenotyping model for acute heart failure with preserved ejection fraction

Aims

Application of the latent class analysis to acute heart failure with preserved ejection fraction (HFpEF) showed that the heterogeneous acute HFpEF patients can be classified into four distinct phenotypes with different clinical outcomes. This model‐based clustering required a total of 32 variables to be included. However, this large number of variables will impair the clinical application of this classification algorithm. This study aimed to identify the minimal number of variables for the development of optimal subphenotyping model.

Methods and results

This study is a post hoc analysis of the PURSUIT‐HFpEF study (N = 1095), a prospective, multi‐referral centre, observational study of acute HFpEF [UMIN000021831]. We previously applied the latent class analysis to the PURSUIT‐HFpEF dataset and established the full 32‐variable model for subphenotyping. In this study, we used the Cohen's kappa statistic to investigate the minimal number of discriminatory variables needed to accurately classify the phenogroups in comparison with the full 32‐variable model. Cohen's kappa statistic of the top‐X number of discriminatory variables compared with the full 32‐variable derivation model showed that the models with ≥16 discriminatory variables showed kappa value of >0.8, suggesting that the minimal number of discriminatory variables for the optimal phenotyping model was 16. The 16‐variable model consists of C‐reactive protein, creatinine, gamma‐glutamyl transferase, brain natriuretic peptide, white blood cells, systolic blood pressure, fasting blood sugar, triglyceride, clinical scenario classification, infection‐triggered acute decompensated HF, estimated glomerular filtration rate, platelets, neutrophils, GWTG‐HF (Get With The Guidelines‐Heart Failure) risk score, chronic kidney disease, and CONUT (Controlling Nutritional Status) score. Characteristics and clinical outcomes of the four phenotypes subclassified by the minimal 16‐variable model were consistent with those by the full 32‐variable model. The four phenotypes were labelled based on their characteristics as ‘rhythm trouble’, ‘ventricular‐arterial uncoupling’, ‘low output and systemic congestion’, and ‘systemic failure’, respectively.

Conclusions

The phenotyping model with top 16 variables showed almost perfect agreement with the full 32‐variable model. The minimal model may enhance the future clinical application of this clustering algorithm.