Current Heart Failure Reports最新文献

Purpose of review: Cardiovascular effects of obesity may be driven, in part, by the distribution of fat. More recently, epicardial adipose tissue (EAT) has gained recognition as an adverse visceral fat impacting cardiac dysfunction in heart failure with preserved ejection fraction (HFpEF).

Recent findings: EAT can be identified and measured using several non-invasive imaging techniques, including transthoracic echocardiography, computed tomography, and cardiac magnetic resonance. The presence of EAT is associated with increased risk of HFpEF and worse clinical outcomes among patients with established HFpEF, independent of total adiposity. EAT may serve a pivotal role in the pathogenesis of HFpEF by worsening volume distribution, enhancing pericardial restraint and ventricular interaction, worsening right ventricular dysfunction, and diminishing exercise tolerance. No large trials have tested the effects of reducing fat in specific areas of the body on cardiovascular outcomes, but some studies that followed people in communities and trials over time have suggested that drug and non-drug treatments that lower EAT could improve the risk factors for heart problems in patients with HFpEF. Further understanding the role that pathogenic fat depots play in HFpEF incidence and progression may provide future therapeutic targets in treating the obese-HFpEF phenotype.

Purpose of review: The pathophysiology of heart failure (HF), a complex and heterogenous condition, remains to be fully understood. Troponin and b-type natriuretic peptide are the only biomarkers that are utilized in clinical practice for HF clinical management. Recent advances in proteomics present a powerful tool to identify risk markers and ultimately, potential molecular mechanisms underlying HF pathogenesis. Herein, we explore traditional and novel heart biomarkers, highlighting their potential role in the pathogenesis of HF.

Recent findings: Recent proteomic analyses have identified numerous proteins including Galectin-3, sST2, GDF-15, FGF21, Endotrophin, THSB-2, ADAMSTL, SVEP1, and anthracycline that are associated with clinical outcomes in HF. These biomarkers are not presently utilized in HF management but may be useful in the future for prediction of death or HF hospitalization. While traditional biomarkers remain essential, proteomic strategies have revealed additional targets that require further mechanistic exploration. Future research should focus on validating these biomarkers and translating proteomic insights into clinical practice to enhance HF management.

Purpose of review: This review aims to present the current state of the art in measuring frailty in patients with heart failure (HF), covering the entire spectrum from diagnosis to advanced stages of the disease. Frailty is a critical factor that significantly impacts outcomes in heart failure, and accurate assessment is essential for guiding treatment and improving prognosis.

Recent findings: Frailty is increasingly recognized as a key determinant of morbidity and mortality in HF patients. Various tools are available for assessing frailty, but there is no consensus on the optimal method. The assessment of frailty needs to be multidimensional, incorporating physical, cognitive, and social domains. Early detection of frailty, coupled with personalized interventions, has the potential to improve patient outcomes. Integrating routine frailty assessments into the clinical care of heart failure patients is essential for optimizing treatment. Future research should focus on standardizing frailty assessment tools and integrating innovative technologies, such as artificial intelligence, to enhance the precision and applicability of these assessments in clinical practice.

Purpose of review: Both cardiac and renal dysfunction can lead to water overload - commonly referred to as "congestion". Identification of congestion is difficult, especially when clinical signs are subtle.

Recent findings: As an extension of an echocardiographic examination, ultrasound can be used to identify intravascular (inferior vena cava diameter dilation, internal jugular vein distension or discontinuous venous renal flow) and tissue congestion (pulmonary B-lines). Combining assessment of cardiac structure, cardiac and renal function and measures of congestion informs the management of heart and kidney disease, which should improve patient outcomes. In this manuscript, we describe imaging techniques to identify and quantify congestion, clarify its origin, and potentially guide the management of patients with cardio-renal syndrome.

Purpose of review: Introduction of modern cancer therapies has led to increased survival of affected patients. With this advantage, the incidence of cancer therapy-related cardiac dysfunction (CTRCD) has increased and reasonable prevention strategies become necessary. This review outlines the major approaches to limit development and progression of CTRCD.

Recent findings: A broad range of cancer therapies can provoke CTRCD ranging from mild asymptomatic forms to severe heart failure. Profound cardiological assessment of cardiovascular comorbidities before initiation of cancer therapy allows identification of cancer patients at higher risk developing CTRCD which may also require closer surveillance. Cardioprotective adjustment of cancer therapy and initiation of cardioprotective medication and lifestyle optimization prior to anti-cancer treatment additionally limit the risk of CTRCD. During therapy, regular examination of cancer patients using high-sensitive cardiological diagnostic tools as three-dimensional (3D) echocardiography and global longitudinal strain (GLS) enables early detection of mild forms of CTRCD. This allows appropriate adjustment of cancer therapy and initiation of CTRCD treatment to prevent further progression to severe forms. Cardiological risk stratification before treatment initiation, cardioprotective interventions before and during cancer therapy, along with regular monitoring of treated cancer patients, can help prevent the development of CTRCD. This maintains the antitumor effects of cancer therapy while limiting cardiotoxic side effects resulting in improved quality of life and mortality of affected cancer patients.

Purpose of review: The most common type of cardiac amyloidosis is transthyretin amyloidosis (ATTR-CM). Early forms of the disease can often go undetected. Effective pharmacological treatments are available for ATTR-CM. However, current treatment options may be more effective when used earlier in the disease, making early detection paramount. Below, we discuss updates with regards to the role that blood-based biomarkers play in detecting subclinical cardiac amyloidosis.

Recent findings: Carriers of amyloidogenic mutations in the TTR gene are at a heightened risk of developing heart failure and have higher mortality rates compared with noncarrier counterparts. Conventional biomarkers, such as the cardiac troponins and natriuretic peptides, may be useful to monitor subclinical cardiac amyloidosis. In addition, recent studies have demonstrated links between amyloidogenic TTR carrier status and low levels of circulating transthyretin (TTR) and retinol-binding protein 4 (RBP4). Laboratory advances have also allowed for the development of peptide-based detection methods. Probes targeting transthyretin aggregates and nonnative TTR peptides have shown promise in differentiating ATTR from non-ATTR amyloidosis populations. Finally, recent studies have identified neurofilament light chains as potential biomarkers for detecting polyneuropathy-predominant amyloidosis. Conventional biomarkers, such as cardiac troponin and natriuretic peptides may indicate evolving amyloid deposition in early ATTR-CM. However, they are non-specific and emerging biomarkers such as serum transthyretin levels, retinol-binding protein 4, transthyretin aggregates, nonnative TTR, and neurofilament light chains may hold promise in characterizing subclinical ATTR.

Purpose of review: Heart failure is a complex and heterogenous disease state that affects millions worldwide. Over recent decades, advancements in medical therapy and device implementation have significantly transformed the landscape of heart failure outcomes, while improvements in imaging modalities and greater accessibility to genome sequencing have led to increasing recognition of distinct heart failure endotypes. There is rising evidence to suggest all patients do not benefit equally from intensification of guideline directed medical therapy (GDMT). Efforts to personalize medical therapy to maximize benefits while minimizing side effects remains an ongoing challenge. We review key manuscripts from The BIOlogy Study to TAilored Treatment in Chronic Heart Failure (BIOSTAT-CHF), a multicenter observational study conducted across Europe, which prospectively enrolled patients with acute or worsening heart failure.

Findings: BIOSTAT-CHF was designed to characterize biological pathways associated with varied patient responses to GDMT for heart failure. Utilizing a diverse cohort of European patients, the authors were able to develop risk models to predict mortality and heart failure hospitalization from clinically available data plus standard and novel biomarkers. They also utilized modeling to refine characterization of heart failure subtypes and personalization of GDMT titration. In this review we highlight key insights from the BIOSTAT-CHF cohort and how they relate to: (1) prognosis and monitoring treatment response in heart failure, (2) personalization of heart failure treatment, and (3) elucidation of biological pathways and future directions for research. These insights summarize how BIOSTAT-CHF has contributed to a deeper understanding of heart failure, focusing on using biomarkers personalizing treatment approaches, with a goal of ultimately improving patient outcomes.

Purpose of review: Heart failure (HF) is often accompanied by a constellation of comorbidities, leading to diverse patient presentations and clinical trajectories. While traditional methods have provided valuable insights into our understanding of HF, network medicine approaches seek to leverage these complex relationships by analyzing disease at a systems level. This review introduces the concepts of network medicine and explores the use of comorbidity networks to study HF and heart disease.

Recent findings: Comorbidity networks are used to understand disease trajectories, predict outcomes, and uncover potential molecular mechanisms through identification of genes and pathways relevant to comorbidity. These networks have shown the importance of non-cardiovascular comorbidities to the clinical journey of patients with HF. However, the community should be aware of important limitations in developing and implementing these methods. Network approaches hold promise for unraveling the impact of comorbidities in the complex presentation and genetics of HF. Methods that consider comorbidity presence and timing have the potential to help optimize management strategies and identify pathophysiological mechanisms.

Purpose of review: This review aims to explore the emerging potential of artificial intelligence (AI) in refining risk prediction, clinical diagnosis, and treatment stratification for cardiomyopathies, with a specific emphasis on arrhythmogenic cardiomyopathy (ACM).

Recent findings: Recent developments highlight the capacity of AI to construct sophisticated models that accurately distinguish affected from non-affected cardiomyopathy patients. These AI-driven approaches not only offer precision in risk prediction and diagnostics but also enable early identification of individuals at high risk of developing cardiomyopathy, even before symptoms occur. These models have the potential to utilise diverse clinical input datasets such as electrocardiogram recordings, cardiac imaging, and other multi-modal genetic and omics datasets. Despite their current underrepresentation in literature, ACM diagnosis and risk prediction are expected to greatly benefit from AI computational capabilities, as has been the case for other cardiomyopathies. As AI-based models improve, larger and more complicated datasets can be combined. These more complex integrated datasets with larger sample sizes will contribute to further pathophysiological insights, better disease recognition, risk prediction, and improved patient outcomes.

Over the past decade, gamification, an umbrella term that refers to tools that engage and motivate participants through the use of game design elements (e.g., challenges and rewards) in a non-gaming context, has emerged as a promising approach in the management of chronic diseases. Specifically, it has been demonstrated to be effective in the education of both patients as well as healthcare professionals on medication adherence, risk reduction, patient self-care, and rehabilitation. There is some evidence suggesting that gamification might have similar benefits for heart failure (HF) patients and their health care professionals. This is of specific interest since HF is a chronic disease associated with a severely compromised long-term prognosis and subsequent high health care resource utilization. However, more robust research is needed to confirm these findings and determine the optimal method, as well as barriers, to the implementation of gamification in the working process of health care professionals or treatment adherence to patients specifically in the setting of HF. This viewpoint explores the literature concerning HF and gamification and aims to identify its various potentials in the management of HF patients.