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Heart failure with preserved ejection fraction (HFpEF) is a major challenge in cardiovascular medicine, accounting for approximately 50% of all cases of heart failure. Due to the lack of effective therapies for this condition, the mortality associated with HFpEF remains higher than that of most cancers. Despite the ongoing efforts, no medical device has yet received FDA approval. This is largely due to the lack of an in vivo model of the HFpEF hemodynamics, resulting in the inability to evaluate device effectiveness in vivo prior to clinical trials. Here, we describe the development of a highly tunable porcine model of HFpEF hemodynamics using implantable soft robotic sleeves, where controlled actuation of a left ventricular and an aortic sleeve can recapitulate changes in ventricular compliance and afterload associated with a broad spectrum of HFpEF hemodynamic phenotypes. We demonstrate the feasibility of the proposed model in preclinical testing by evaluating the hemodynamic response of the model post-implantation of an interatrial shunt device, which was found to be consistent with findings from in silico studies and clinical trials. This work addresses several of the limitations associated with previous models of HFpEF, such as their limited hemodynamic fidelity, elevated costs, lengthy development time, and low throughput. By showcasing exceptional versatility and tunability, the proposed platform has the potential to revolutionize the current approach for HFpEF device development and selection, with the goal of improving the quality of life for the 32 million people affected by HFpEF worldwide.
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http://dx.doi.org/10.1101/2023.07.26.550654 | DOI Listing |
Cureus
August 2025
Department of Health Sciences, University of Jamestown, Fargo, USA.
Background Heart failure (HF) is a leading cause of morbidity and hospitalization, encompassing distinct phenotypes: heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF). Disparities in diagnostic imaging may contribute to underdiagnosis and unequal care. This study evaluates differences in combined diagnostic imaging utilization between HFpEF and HFrEF, focusing on social determinants of health (SDoH) and hospital region.
View Article and Find Full Text PDFCardiovasc Diabetol
September 2025
Computational Biomedicine, Center for Thrombosis and Hemostasis (CTH), Mainz, Germany.
Background: Sodium-glucose cotransporter 2 (SGLT2) inhibitors, such as Empagliflozin, are antidiabetic drugs that reduce glucose levels and have emerged as a promising therapy for patients with heart failure (HF), although the exact molecular mechanisms underlying their cardioprotective effects remain to be fully elucidated. The EmDia study, a randomized, double-blind trial conducted at the University Medical Center of Mainz, has confirmed the beneficial effects of Empagliflozin in HF patients after both one and twelve weeks of treatment. In this work, we aimed to assess whether changes in lipid profiles driven by Empagliflozin use in HF patients in the EmDia trial could assist in gaining a better understanding of its cardioprotective mechanisms.
View Article and Find Full Text PDFJ Cardiol
September 2025
Catholic Research Institute for Intractable Cardiovascular Disease, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea; Department of Cardiology, Daejeon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea. Electronic addr
Heart failure with preserved ejection fraction (HFpEF) accounts for more than half of all HF cases and its incidence and prevalence continue to increase, with a substantial burden of morbidity and mortality. Despite advances in our understanding of heterogeneous pathophysiology underlying HFpEF, the diagnosis, risk assessment, and management of this disease entity remain challenging in everyday practice. Artificial intelligence (AI) algorithm can handle large amounts of complex data and machine learning (ML), a subfield of AI, allows for the identification of relevant patterns by learning from big data.
View Article and Find Full Text PDFCell Signal (Middlet)
January 2025
Department of Pathology, Johns Hopkins University School of Medicine, Baltimore MD, USA.
Aging contributes significantly to the deterioration of cardiac function and increases the prevalence of heart failure, including those with reduced or preserved ejection fraction. Heart failure with preserved ejection fraction (HFpEF) is highly prevalent in the elderly population and it has become a leading cause of morbidity and mortality in this group. This commentary discusses the important findings and broader implications of the study by Daneshgar .
View Article and Find Full Text PDFEur J Heart Fail
September 2025
Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Aims: To jointly model longitudinal measures of left ventricular ejection fraction (LVEF) and E/A ratio in late-life, and to assess whether predicted trajectory membership is associated with heart failure risk.
Methods And Results: Using a Bayesian non-parametric trajectory approach, trajectories were modelled among 747 Jackson Heart Study participants who underwent ≥2 echocardiograms in 2000-2004 (age 65 ± 5 years), 2011-2013 (75 ± 5), and 2018-2019 (81 ± 5). Using the resulting model, we predicted trajectory membership for 4419 distinct Atherosclerosis Risk in Communities (ARIC) study participants based on single time-point measures of LVEF and E/A ratio (age 75 ± 5 years; 'testing cohort').