Heart Failure Reviews最新文献

Heart failure (HF) is a global pandemic with a growing prevalence and is a growing burden on the healthcare system. Machine learning (ML) has the potential to revolutionize medicine and can be applied in many different forms to aid in the prevention of symptomatic HF (stage C). HF prevention currently has several challenges, specifically in the detection of pre-HF (stage B). HF events are missed in contemporary models, limited therapeutic options are proven to prevent HF, and the prevention of HF with preserved ejection is particularly lacking. ML has the potential to overcome these challenges through existing and future models. ML has limitations, but the many benefits of ML outweigh these limitations and risks in most scenarios. ML can be applied in HF prevention through various strategies such as refinement of incident HF risk prediction models, capturing diagnostic signs from available tests such as electrocardiograms, chest x-rays, or echocardiograms to identify structural/functional cardiac abnormalities suggestive of pre-HF (stage B HF), and interpretation of biomarkers and epigenetic data. Altogether, ML is able to expand the screening of individuals at risk for HF (stage A HF), identify populations with pre-HF (stage B HF), predict the risk of incident stage C HF events, and offer the ability to intervene early to prevent progression to or decline in stage C HF. In this narrative review, we discuss the methods by which ML is utilized in HF prevention, the benefits and pitfalls of ML in HF risk prediction, and the future directions.

In heart failure with reduced ejection fraction (HFrEF), the presence of concomitant chronic kidney disease (CKD) predicts poorer cardiovascular outcomes, more aggravated heart failure (HF) status, and higher mortality. Physicians might be reluctant to initiate life-saving anti-HF medications out of fear of worsening renal function and a higher incidence of adverse events. Moreover, international guidelines do not give clear recommendations on managing this subgroup of patients as well as advanced CKD was always an exclusion criterion in most major HF trials. Nevertheless, in this review, we will highlight several recent clinical trials and post-hoc analyses of major trials that showed the safety and efficacy of the different therapies in HFrEF patients with CKD, besides several small-scale cohorts that tested guideline-directed medical therapies in End Stage Kidney Disease (ESKD). Regarding interventions in this subgroup of patients, we will provide up-to-date data on implantable cardioverter defibrillators, cardiac resynchronization therapy, and coronary revascularization, in addition to mitral valve transcatheter edge-to-edge repair and implantable pulmonary artery pressure sensors.

The SCN5A gene encodes the alpha subunit of the cardiac sodium channel, which plays a fundamental role in the generation and propagation of the action potential in the heart muscle. During the past years our knowledge concerning the function of the cardiac sodium channel and the diseases caused by mutations of the SCN5A gene has grown. Although initially SCN5A pathogenic variants were mainly associated with channelopathies, increasing recent evidence suggests an association with structural heart disease in the form of heart muscle disease. The pathways leading to a cardiomyopathic phenotype remain unclear and require further elucidation. The aim of the present review is to provide a concise summary regarding the mechanisms through which SCN5A pathogenic variants result in heart disease, focusing in cardiomyopathy, highlighting along the way the complex role of the SCN5A gene at the intersection of cardiac excitability and contraction networks.

Diabetic cardiomyopathy is a unique cardiomyopathy that is common in diabetic patients, and it is also a diabetic complication for which no effective treatment is currently available. Moreover, relevant studies have revealed that a link exists between type 2 diabetes and heart failure and that abnormal thickening of EAT is inextricably linked to the development of diabetic heart failure. Numerous clinical studies have demonstrated that EAT is implicated in the pathophysiologic process of diabetic myocardial disease. In this overview, we will introduce the physiology, pathophysiology of the disease and potential therapeutic strategies, knowledge gaps, and future directions of the role of epicardial adipose tissue in type 2 diabetes mellitus and heart failure to promote the development of novel therapeutic approaches to improve the prognosis of patients with diabetic cardiomyopathy.

Left ventricular assist devices (LVAD) have improved mortality and quality of life for patients with end-stage heart failure by providing an alternative to cardiac transplant or as a bridge to transplantation. The improvement in functional capacity however is minimal to modest depending on the right ventricular function, optimal hemodynamics on LVAD therapy, and comorbidities. There is improvement in submaximal exercise capacity but improvement in peak aerobic capacity is limited. Exercise capacity can be improved by referral to cardiac rehabilitation after LVAD implantation. Cardiac rehabilitation is safe and effective with improvement in functional capacity, and decrease in mortality and heart failure hospitalizations. Cardiopulmonary exercise testing (CPET) is a specialized exercise stress test that measures gas exchange during exercise to determine a variety of variables that have been shown to be predictive of mortality in patients undergoing cardiac transplant. CPET is valuable for prognostication and is a predictor of adverse outcomes, including right heart failure in the immediate post-LVAD implantation period, long-term mortality. CPET is an additional testing that can aid in the decision making for LVAD explantation or decommissioning.

The clinical and economic impact of heart failure (HF) is immense and will continue to rise due to the increasing prevalence of the disease. Despite the availability of guideline-recommended medications that improve mortality, reduce hospitalizations, and enhance quality of life, there are major gaps in the implementation of such care. Quality improvement interventions have generally focused on clinicians. While certain interventions have had modest success in improving the use of heart failure medications, they remain insufficient in optimizing HF care. Here, we discuss how patient-facing interventions can add value and supplement clinician-centered interventions. We discuss how digital health can be leveraged to create patient activation tools that create a larger, sustainable impact. Small studies have suggested the promise of digital tools for patient engagement and self-care, but there are also important barriers to the adoption of such interventions that we describe. We share key principles and strategies around the design and implementation of digital health innovations to maximize patient participation and engagement. By uniquely activating patients in their own care, digital health can unlock the full potential of both existing and new quality improvement initiatives to drive forward high-quality and equitable heart failure care.

Myocarditis, marked by heart muscle inflammation, poses significant clinical challenges. This study, guided by PRISMA guidelines, explores the expanding role of artificial intelligence (AI) in myocarditis, aiming to consolidate current knowledge and guide future research. Following PRISMA guidelines, a systematic review was conducted across PubMed, Cochrane Reviews, Scopus, Embase, and Web of Science databases. MeSH terms including artificial intelligence, deep learning, machine learning, myocarditis, and inflammatory cardiomyopathy were used. Inclusion criteria involved original articles utilizing AI for myocarditis, while exclusion criteria eliminated reviews, editorials, and non-AI-focused studies. The search yielded 616 articles, with 42 meeting inclusion criteria after screening. The identified articles, spanning diagnostic, survival prediction, and molecular analysis aspects, were analyzed in each subsection. Diagnostic studies showcased the versatility of AI algorithms, achieving high accuracies in myocarditis detection. Survival prediction models exhibited robust discriminatory power, particularly in emergency settings and pediatric populations. Molecular analyses demonstrated AI's potential in deciphering complex immune interactions. This systematic review provides a comprehensive overview of AI applications in myocarditis, highlighting transformative potential in diagnostics, survival prediction, and molecular understanding. Collaborative efforts are crucial for overcoming limitations and realizing AI's full potential in improving myocarditis care.

The concept of quadruple therapy as a "one-size-fit-all" approach is effective among all eligible patients with heart failure with reduced ejection fraction, with consistent and significant clinical benefits including reduced mortality across various subgroups. However, with exception of sodium-glucose cotransporter 2 inhibitors, the consistency of benefit with therapies does not extend to patients with heart failure with preserved ejection fraction. The clinical benefits of other promising medical therapies, such as angiotensin receptor-neprilysin inhibitors, mineralocorticoid receptor antagonists, and glucagon-like peptide-1 receptor agonists, have been demonstrated only in certain phenotypes of the highly heterogenous heart failure with preserved ejection fraction population. This variability can confuse frontline practicing cardiologists, potentially leading to the under-implementation of these medications. Therefore, we propose a simple approach: "targeted" combination therapy. This strategy aims to optimize evidence-based medications in heart failure with preserved ejection fraction by tailoring treatments to specific subgroups within the heart failure with preserved ejection fraction population where significant benefits are most evident.

Increased sympathetic and reduced parasympathetic nerve activity is associated with disease progression and poor outcomes in patients with chronic heart failure. The demonstration that markers of autonomic imbalance and vagal dysfunction, such as reduced heart rate variability and baroreflex sensitivity, hold prognostic value in patients with chronic heart failure despite modern therapies encourages the research for neuromodulation strategies targeting the vagus nerve. However, the approaches tested so far have yielded inconclusive results. This review aims to summarize the current knowledge about the role of the parasympathetic nervous system in chronic heart failure, describing the pathophysiological background, the methods of assessment, and the rationale, limits, and future perspectives of parasympathetic stimulation either by drugs or bioelectronic devices.

Diuresis to achieve decongestion is a central aim of therapy in patients hospitalized for acute decompensated heart failure (ADHF). While multiple approaches have been tried to achieve adequate decongestion rapidly while minimizing adverse effects, no single diuretic strategy has shown superiority, and there is a paucity of data and guidelines to utilize in making these decisions. Observational cohort studies have shown associations between urine sodium excretion and outcomes after hospitalization for ADHF. Urine chemistries (urine sodium ± urine creatinine) may guide diuretic titration during ADHF, and multiple randomized clinical trials have been designed to compare a strategy of urine chemistry-guided diuresis to usual care. This review will summarize current literature for diuretic monitoring and titration strategies, outline evidence gaps, and describe the recently completed and ongoing clinical trials to address these gaps in patients with ADHF with a particular focus on the utility of urine sodium-guided strategies.