The management of end-stage heart failure in cardiac amyloidosis due to both light chain (AL-CA) and transthyretin (ATTR-CA) with heart transplantation has historically been limited by concerns about systemic progression after transplant. As a result, heart transplantation as a strategy was often restrictive and many patients were excluded. This review synthesizes contemporary evidence and outlines a practical framework to guide heart transplant evaluation and decision making in advanced disease. We emphasize evolving multidisciplinary selection principles. In the recent era (since the 2010s), multicenter experience has demonstrated that carefully selected patients with advanced AL-CA and ATTR-CA can achieve favorable outcomes after heart transplantation. Specifically, selected individuals with wild type transthyretin cardiac amyloidosis (ATTRwt-CA) may be appropriate candidates. For hereditary transthyretin cardiac amyloidosis (ATTRv-CA), isolated heart or combined heart liver transplantation (CHLT) can been considered, although modern transthyretin targeted therapies increasingly influence this decision. Patients with AL-CA may undergo transplantation alongside timely plasma cell directed treatment. Historically, outcomes for heart transplantation in patients with cardiac amyloidosis were poor. Improved patient selection as well as advances in plasma cell targeted therapy for AL-CA and transthyretin stabilizing or gene silencing treatments for ATTR-CA have reshaped expectations. The improved outcomes seen in more contemporary cohorts support heart transplantation as a viable option for selected patients with advanced disease.
To identify the role of endomyocardial biopsy in the diagnosis, prognostication, and future understanding of mechanisms for immune checkpoint inhibitor myocarditis. The use of endomyocardial biopsy in immune checkpoint inhibitor myocarditis has led to recognizing a spectrum of disease that correlates with mild symptoms to more fulminant presentations. The primary infiltrate observed is CD8+ T cells but more studies are suggesting that the presence of CD68+ macrophages may portend worse prognosis. Currently, endomyocardial biopsy still has a role in diagnosing immune checkpoint inhibitor myocarditis especially when there is uncertainty from non-invasive diagnostic tools. Emerging evidence suggests endomyocardial biopsy can help with prognosis determination and potentially even guide future cancer therapy. Future research into understanding mechanisms of immune checkpoint inhibitor myocarditis will require endomyocardial biopsy for tissue acquisition.
Device deactivation in patients with heart failure is an increasingly relevant clinical and ethical challenge as the use of cardiovascular implantable electronic devices expands. Current guidelines affirm the ethical and legal permissibility of deactivating life-sustaining devices, including implantable cardioverter-defibrillators, pacemakers, and mechanical circulatory support systems, when requested by patients or surrogates with decision-making capacity. Importantly, guidelines from professional societies recommend that discussions about device deactivation be included in the pre-implantation consent process. However, clinical practice reveals persistent gaps in advance care planning, with device deactivation discussions remaining infrequent. Deactivating implantable cardioverter defibrillators can prevent painful and unnecessary shocks at the end of life, which may otherwise prolong dying without improving quality of life. Multidisciplinary involvement, including palliative care consultation, is essential to support patient-centered decision-making and symptom management. This review synthesizes the latest evidence and consensus on device deactivation in heart failure, emphasizing the need for communication, initiated early in the disease course, as well as individualized care, and integration of patient preferences throughout the disease trajectory.
We review potential applications and pitfalls of urinary biomarkers in people with heart failure (HF). Albuminuria may indicate kidney damage and guide prioritization of medications in HF. Urine sodium is useful in guiding diuretic management. Several experimental urine biomarkers have been evaluated, with the most robust data available for neutrophil gelatinase-associated lipocalin, kidney injury marker 1, insulin-like growth factor-binding protein 7 in conjunction with tissue inhibitor of metalloproteinases-2, angiotensinogen, and N-acetyl-β-D-glucosaminidase. None provide diagnostic superiority over serum creatinine in evaluating risk of kidney injury. Some may have utility in identifying those at risk for adverse clinical outcomes. While urine albumin and sodium have prognostic and diagnostic utility in HF, the current role for other urine biomarkers is less clear, particularly in predicting worsening kidney function. Some of these biomarkers may provide mechanistic insights into kidney damage in HF.
Abstract Purpose of Review Heart failure (HF) is a complex clinical syndrome that develops as the final common manifestation of diverse cardiovascular disorders along a variety of different pathophysiological pathways. The trajectory towards HF is set decades earlier by a combination of non-modifiable risk factors and a substantial number of principally modifiable risk factors. Conceptualizing HF prevention as a continuum — from primordial prevention to tertiary prevention — highlights how consistently these factors determine and drive the risk to develop HF. Early at-risk and pre-HF stages therefore represent the most appropriate window for effective prevention, yet they are still underrated and under-recognized in clinical practice. Recent Findings A broad array of modifiable risk factors, together with echocardiographic and laboratory markers, are involved in disease progression. Combining these domains may optimize risk stratification and enable more targeted prevention efforts. Summary The rapidly evolving heart failure pandemic, largely driven by the rising prevalence of conditions that are to a substantial extent attributable to modifiable risk factors, poses an enormous burden on health systems worldwide. In consequence, enhanced global awareness, comprehensive assessment and more effective control of modifiable risk factors are urgently needed to prevent HF. A multidimensional risk assessment approach could facilitate early identification and timely intervention to prevent progression to symptomatic HF.
Heart failure (HF) is increasingly understood not as a single, uniformly treated diagnosis but as a heterogeneous syndrome requiring aetiological clarification, in which cardiac imaging is central. As the opening article of this journal's ‘Imaging in Heart Failure’ section, this review surveys the technologies currently reshaping HF imaging and sets out the section's scope and priorities, framing the shift from a descriptive, modality-siloed practice toward an integrated, predictive, patient-specific discipline. Artificial intelligence (AI) now delivers expert-level echocardiography automation, guides image acquisition by novices in resource-limited settings, detects aetiologies such as transthyretin amyloid cardiomyopathy from a single acquisition and enables deep phenotyping through radiomics and vendor-agnostic strain analysis. Handheld, AI-enabled point-of-care ultrasound extends imaging-guided triage beyond the echocardiography laboratory. Cardiovascular magnetic resonance (CMR) advances — parametric mapping, four-dimensional flow, diffusion tensor imaging, spectroscopy, and accelerated reconstruction — broaden tissue and metabolic characterisation, including patients with implanted devices. Molecular imaging with novel positron emission tomography tracers and hyperpolarised magnetic resonance is moving from depicting the structural consequences of disease to imaging active pathobiology, while photon-counting computed tomography and image-derived digital twins support one-stop structural assessment and in-silico prediction of therapy response. The convergence of AI, molecular imaging and advanced precision is transforming HF imaging from better pictures into smarter, integrated, personalised data that directly inform care. Realising this promise will require rigorous validation, attention to algorithmic bias and generalisability, demonstrated cost-effectiveness, curricular reform, and equitable access. This section aims to critically appraise these innovations and their translation into practice.
Cancer therapy–related cardiac dysfunction (CTRCD) remains a significant risk of contemporary cancer treatment. Despite advances in oncologic therapies, cardiac surveillance strategies have largely relied on uniform, intensive monitoring, often without consideration of individual cardiotoxicity risk. This review evaluates the rationale for transitioning from blanket surveillance to risk-based, personalized cardiac monitoring strategies, especially for HER2-targeted therapy. Evolving definitions of CTRCD and improved risk stratification tools, have highlighted substantial heterogeneity in cardiotoxicity risk. Prospective studies in low-risk patients receiving non-anthracycline HER2-targeted therapies demonstrate that reduced-frequency echocardiographic surveillance appears safe and does not compromise cardiovascular or oncologic outcomes. Similar paradigms need to be studied for other cardiotoxic therapies, including BRAF/MEK inhibitors and VEGF inhibitors, with a need for prospective validation before clinical application. Risk-adapted cardiac surveillance offers a pragmatic, evidence-based approach to optimize resource utilization while maintaining patient safety. Future research should focus on prospective validation and guideline harmonization to enable personalized cardio-oncology care.
Cardio-oncology has emerged as a pivotal discipline aimed at preserving cardiovascular health in patients undergoing contemporary cancer therapies. Despite growing awareness of treatment-related cardiac injury, the evidence base supporting preventive strategies and standardized safety assessment remains fragmented. This review critically appraises randomized controlled trials evaluating pharmacological and non-pharmacological interventions for the prevention of cancer therapy–related cardiac dysfunction. In parallel, we examine how cardiovascular events are monitored, defined, and reported in major oncology trials, with particular emphasis on patient selection criteria and the use of structured surveillance protocols. Across drug classes repurposed from heart failure (HF) prevention and treatment, including neurohormonal antagonists, angiotensin receptor–neprilysin inhibition, lipid-lowering therapies, as well as exercise-based interventions, randomized evidence has demonstrated modest and inconsistent benefits. Reported effects are largely confined to subclinical alterations, such as changes in left ventricular systolic function, myocardial deformation parameters, or circulating cardiac biomarkers. By contrast, convincing reductions in clinically meaningful outcomes, including overt HF, treatment interruption, or cardiovascular mortality, remain limited. Concurrently, oncology trials frequently exhibit heterogeneous cardiovascular event definitions, incomplete safety reporting, and systematic exclusion of patients with pre-existing cardiac disease, thereby constraining external validity and obscuring the true burden of cardiotoxicity and competing cardiovascular risks. Advancing the field will require a paradigm shift toward individualized, risk-enriched prevention strategies anchored in clinically relevant endpoints. Broader inclusion of patients with stable cardiovascular comorbidities, managed under structured specialist supervision, alongside standardized frameworks for cardiovascular safety monitoring and reporting, is essential. Emerging artificial intelligence, as enabled tools applied to cardiac imaging, electrocardiography, and remote monitoring offer a promising opportunity to harmonize early detection of cardiotoxicity across trial sites and refine phenotyping of treatment-related cardiac injury. Integrating these elements into future trial design will be critical to ensure that therapeutic progress in oncology is not undermined by preventable cardiovascular harm. Key question Can cardiovascular therapies and strategies routinely used in clinical cardiology mitigate cardiovascular toxicity related to cancer treatments? In parallel, how consistently are cardiovascular adverse events identified, monitored, and reported within contemporary oncology randomised clinical trials? Key findings Several pharmacological and non-pharmacological interventions commonly employed in cardiovascular medicine, including neurohormonal inhibition, statins, and exercise-based strategies, have been evaluated in small randomised trials for the prevention of cancer therapy–related cardiovascular dysfunction. While these approaches have not demonstrated consistent reductions in major clinical cardiovascular outcomes, they have shown signals of benefit on surrogate endpoints, such as myocardial remodelling, biomarkers of injury, and subclinical ventricular dysfunction. In oncology trials, cardiovascular adverse events are variably reported, with heterogeneous definitions, monitoring protocols, and frequent exclusion of patients with pre-existing cardiovascular disease. Take-home message Cardiovascular and oncological diseases are increasingly intertwined. Current evidence highlights the need for standardised cardiovascular safety frameworks, harmonised monitoring strategies, and integration of emerging tools, including AI-based approaches, to improve cardiotoxicity detection and reporting in future oncology trials.
Acute myocarditis (AM) shows sex-specific differences in prevalence, clinical phenotype, and outcomes. This review summarizes evidence from studies published between 2000 and 2026 reporting sex-stratified data on AM epidemiology, presentation, and prognosis to guide future research and a more personalized management. AM is more frequent in males (54–84
This review synthesizes recent progress in applications of artificial intelligence to heart failure care, including phenotyping, risk stratification, imaging interpretation, and point-of-care decision support, and delineates barriers that currently limit safe and equitable clinical translation. Clinical datasets remain heterogeneous and incomplete; fragmentation across electronic records, telemetry, and imaging repositories constrains generalizability and external validity. Underrepresentation of key subgroups and outcome misclassification introduce systematic error that can widen disparities. Performance drifts as therapies and workflows evolve, yet monitoring after deployment is uncommon. Model opacity hinders error analysis and clinician trust. Regulatory and data-sharing frameworks are evolving and inconsistent, complicating multisite validation and ongoing surveillance. Mitigation strategies with the strongest support include rigorous cohort curation; transparent reporting; geographic and temporal external validation; prospective pilots with prespecified safety checks; bias auditing with equity metrics; concise documentation such as model cards and factsheets; continuous monitoring with clear contingency and rollback plans; and human oversight embedded throughout governance. Embedding safeguards into development and implementation can enable AI to deliver measurable value in heart failure care while protecting patient safety and equity. Immediate priorities are robust evaluation, routine surveillance for drift and harm, and alignment with outcomes that matter to patients.
Advanced heart failure (HF) is characterized by high symptom burden, poor quality of life (QoL), and recurrent hospitalizations. As survival improves, an increasing proportion of patients progresses to advanced stages, leading to a growing population of individuals with end-stage HF, many of whom are ineligible for heart transplantation or mechanical circulatory support due to age, comorbidities, frailty, or limited social support. This review aims to synthesize current evidence on palliative care (PC) integration in advanced HF, focusing on indications, timing of referral, and the role of multidisciplinary care in optimizing patient management. Recent evidence supports early integration of PC across the HF trajectory, demonstrating reductions in hospital readmissions and improvements in QoL. Screening tools, patient reported outcome measures, and frailty assessments have emerged as key instruments to identify unmet needs and appropriate referral. Multidisciplinary models involving cardiology, palliative specialists, nurses, and community care providers have shown benefits in symptom control, care coordination, and advance care planning. PC represents a fundamental component of advanced HF management and should be integrated early in the disease trajectory. A structured, multidisciplinary approach that supports holistic care, improves patient outcomes, and aligns treatment strategies with patient preferences should be routinely adopted by HF clinicians.
Artificial intelligence (AI) is poised to transform heart failure (HF) care across the clinical continuum, yet a substantial gap remains between model development and implementation. This review aims to summarize key AI-enabled innovations across HF care and provide a practical framework for clinical implementation. AI applications based on electrocardiography, echocardiography, electronic health records, wearable devices, and large language models have demonstrated promise for early detection, diagnosis, risk stratification, and treatment optimization in HF. The field is shifting from retrospective model development toward prospective evaluation, workflow integration, and health-system deployment, though challenges related to bias, generalizability, interoperability, and clinician adoption persist. Here, we propose a practical stepwise framework to support the safe, scalable, and sustainable implementation of AI in real-world heart failure care.
Ventricular assist devices (VADs) have evolved from a bridge-to-transplantation strategy into a cornerstone of long-term advanced heart failure management. While continuous-flow technology has dramatically improved survival and device reliability, the paradigm shift toward extended mechanical circulatory support has exposed critical unresolved challenges. This review examines current limitations and four transformative technological directions shaping the next generation of VAD therapy. Four key areas are driving innovation in the field. First, growing evidence linking prolonged non-pulsatile hemodynamics to vascular dysfunction and hemorrhagic complications has renewed interest in miniaturized pulsatile devices, with multiple engineering approaches under active investigation. Second, the development of physiologically responsive "smart pump" technology represents a major advance, enabling dynamic modulation of pump output in response to patient hemodynamic demands. Third, transcutaneous energy transfer (TET) systems combined with advanced battery technologies are progressing toward fully implantable designs that would eliminate driveline-related infections, a persistent and clinically significant complication of current-generation devices. Fourth, total artificial heart (TAH) development continues to advance, with both pulsatile and continuous-flow platforms incorporating TET technology to address biventricular failure. Despite ongoing challenges in balancing miniaturization, durability, and biocompatibility, these four innovations collectively hold promise for transforming mechanical circulatory support into a viable long-term therapeutic option, improving both survival and quality of life for a broader population of patients with advanced heart failure.
Structural heart disease (SHD) encompasses diseases involving the heart valves, chambers, walls, and muscles. Current diagnostic methods have limited accessibility and predictive value. This review aims to present recent advances in artificial intelligence (AI)-guided tools in the screening of SHD and valvular heart disease (VHD), and to present challenges and opportunities for their use in clinical practice. AI-guided models trained on ECGs, chest X-rays, and coronary artery calcium scans have a high accuracy in the diagnosis of SHD, heart failure, low left ventricular ejection fraction, and VHD. Some of these models can highlight the signals that influence their predictions, improving explainability. The use of AI in screening for SHD and VHD could lead to earlier diagnosis, enhanced accuracy, and better accessibility. However, outcome data on earlier diagnosis using these tools is required before broad deployment.
The development of effective treatments for heart failure (HF) often fails due to the lack of preclinical models that closely reflect the native structure and function of the human myocardium. Living myocardial slices (LMS) are ultra-thin sections of heart tissue that have shown to retain the complexity, multicellularity, and function of the innate adult myocardium. The number of studies using LMS for HF research and its underlying diseases has been increasing rapidly over the last few years, mainly due to methodological advances that have prolonged LMS culture. This review summarizes key findings and various applications of LMS in HF research. LMS derived from both animal and human hearts, including end-stage HF explants, donor hearts, or surgical specimens, have increasingly been used to model HF and related cardiac diseases. Moreover, LMS have enabled the study of human-specific responses to potential therapeutic drugs and replicate other drug-related effects, such as cardiotoxicity, as they appear in the clinic. Additionally, they have been used to validate the impact of gene delivery of pro-regenerative targets previously investigated in animal studies. More recently, LMS platforms have also been used to mimic device therapy for HF patients by controlling the mechanical and electrical parameters of LMS in culture. LMS represent a physiologically relevant model that bridges the gap between conventional in vitro systems and in vivo models in HF research. Despite remaining challenges related to tissue availability and culturing, LMS provide a highly translational platform for testing potential treatment strategies and understanding the underlying mechanisms of HF.
The present study aims to provide an up-to-date umbrella review of existing meta-analyses of randomized control trials (RCT) of cognitive behavioral therapy (CBT) among patients with heart failure (HF). The umbrella review covered five meta-analyses published in the past five years with 18 primary studies (conducted between 2010 and 2022) of 1,687 non-duplicated patients aged 51.63 to 77.40. Primary studies originated from the USA, Sweden, Netherlands, China, Iran, and Philippines. Common benefits of CBT included depressive symptoms (89
Sex disparities in left ventricular assist device (LVAD) therapy remain a significant challenge in advanced heart failure care. This review synthesizes evidence on differences in epidemiology, clinical presentation, anatomical suitability, complications, and long-term recovery to clarify why women remain underrepresented despite comparable survival with contemporary devices. Women constitute nearly half of the heart failure population but only 20–25
Advanced heart failure is a growing global health challenge, and the use of Left Ventricular Assist Devices (LVADs) as a long-term treatment has increased. This development shifts substantial responsibility to informal caregivers, who play a crucial role in patients’ daily management and wellbeing. However, knowledge about caregivers’ experiences across the entire LVAD trajectory remains limited. This review aims to synthesize available evidence on informal caregivers’ experiences of everyday life when supporting an adult patient with advanced heart failure treated with an LVAD. This systematic review and thematic synthesis included 17 studies. The analysis identified ten descriptive themes related to caregivers’ experiences, which were further synthesized into four analytical themes: Before LVAD implantation, Early post-LVAD, Later post-LVAD, and End of the LVAD journey. These themes illustrate the evolving and dynamic nature of caregiving across the LVAD trajectory. In addition, one overarching analytical theme, The caregiver’s network of support, was identified. The findings highlight caregivers’ experiences of both receiving and providing support through formal and informal networks throughout the LVAD trajectory, emerging as a continuous and integral component of caregiving. The review underscores the importance of sustained and structured support for caregivers across the entire LVAD pathway, rather than support limited to specific time points.
Myocarditis, defined as inflammation of heart muscle, can be triggered by viral infection or have an autoimmune origin. In some cases, it progresses to dilated cardiomyopathy (DCM) and heart failure. The human leukocyte antigen (HLA) system is important in adaptive immunity by influencing antigen presentation and autoimmunity. This review intends to clarify the role of HLA polymorphisms in the progression, resistance, and outcomes of myocarditis and DCM. Evidence indicates that HLA is linked to myocarditis and DCM through familial aggregation, myocardial HLA upregulation, circulating cardiac autoantibodies, candidate association studies, genome-wide association signals at chromosome 6p21, and transgenic models demonstrating HLA-restricted disease susceptibility. HLA alleles influence viral clearance, molecular mimicry, autoimmune priming, cytokine signaling, and pathogen-specific autoantibody production. Translation of this evidence into clinical practice has been limited by modest effect sizes, population variability, and the lack of genotype-guided therapies. Using HLA as a risk-stratification tool can allow more accurate surveillance and immunomodulatory strategies for myocarditis and DCM. Future research priorities should include multi-ethnic HLA studies, viral epitope mapping, integration of immune profiling, and HLA-stratified clinical trials.
To integrate clinical and preclinical evidence on insulin-like growth factor-binding protein-7 (IGFBP7) in heart failure (HF) and identify key priorities for advancing IGFBP7-targeted therapies toward human translation. Circulating IGFBP7 is strongly associated with HF development, diastolic dysfunction, and disease progression across HF phenotypes. Preclinical studies show that genetic or pharmacologic inhibition of IGFBP7 attenuates cardiac remodelling, reduces cardiomyocyte senescence, and ameliorates cardiac dysfunction in murine HF models. Apparent mechanistic discrepancies between studies likely reflect cell-specific actions: cardiomyocyte-derived IGFBP7 activates IGF-1 receptor signalling, promoting cardiomyocyte hypertrophy and senescence, whereas endothelial-derived IGFBP7 inhibits cardiomyocyte insulin receptor signalling, impairing metabolic homeostasis. IGFBP7 is a senescence-associated biomarker linked to HF development, diastolic dysfunction, and HF progression. Preclinical studies converge on the conclusion that IGFBP7 inhibition attenuates cardiac remodelling and ameliorates cardiac dysfunction in experimental HF, positioning IGFBP7 as a promising therapeutic target. Ongoing research into IGFBP7-targeted strategies may expand HF treatment beyond conventional hemodynamic and neurohormonal interventions by directly addressing the biology of cardiovascular ageing and senescence-driven myocardial dysfunction.
