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The health and economic burden of heart failure is significant and continues to grow each year. Loop diuretics are an integral part of symptom management in heart failure. Furosemide is used disproportionately compared with other loop diuretics, and there is currently no guidance for physicians regarding which agent to choose. However, there exist pharmacologic differences as well as other mechanistic differences that appear to favor torsemide use over furosemide. Compared with furosemide, torsemide improves surrogate markers of heart failure severity such as left ventricular function, plasma brain natriuretic peptide levels, and New York Heart Association functional class and may also reduce hospitalizations, readmissions, and mortality. Data suggest that these benefits could be mediated through torsemide's ability to positively affect the renin-angiotensin-aldosterone system. Specifically, torsemide has been shown to inhibit aldosterone secretion, synthesis, and receptor binding in vitro, as well as decrease transcardiac extraction of aldosterone, myocardial collagen production, and cardiac fibrosis in patients with heart failure. We identified pertinent literature using keyword MEDLINE searches and cross-referencing prior bibliographies. We summarize the available data suggesting potential benefits with torsemide over furosemide, and call attention to the need for a reappraisal of diuretic use in heart failure patients and also for a well-powered, randomized control trial assessing torsemide versus furosemide use.

Older patients hospitalized for heart failure were randomly assigned to a rehabilitation intervention (which included multiple function domains) or control; the intervention began during, or early after, hospitalization and continued for 3 months. At 3 months, physical function, as assessed by the Short Physical Performance Battery, was better in the intervention group than in the control group.

Aims There is considerable variability in the effect of intravenous iron on hard cardiovascular (CV)‐related outcomes in patients with heart failure (HF) in randomized controlled trials (RCTs). We use a meta‐analytic approach to analyse data from existing RCTs to derive a more robust estimate of the effect size of intravenous iron infusion on CV‐related outcomes in patients with HF. Method and results PubMed/Medline was searched using the following terms: (‘intravenous’ and ‘iron’ and ‘heart failure’) from inception till 6 November 2022 for RCTs comparing intravenous iron infusion with placebo or standard of care in patients with HF and iron deficiency. Outcomes were the composite of CV mortality and first hospitalization for HF; all‐cause mortality; CV mortality; first hospitalization for HF; and total hospitalizations for HF. Random effects risk ratio (RR) with 95% confidence intervals (CIs) were calculated. Ten RCTs with a total of 3438 patients were included. Intravenous iron resulted in a significant reduction in the composite of CV mortality and first hospitalization for HF [RR 0.0.85; 95% CI (0.77, 0.95)], first hospitalization for HF [RR 0.82; 95% CI (0.67, 0.99)], and total hospitalizations for HF [RR 0.74; 95% CI (0.60, 0.91)] but no statistically significant difference in all‐cause mortality [RR 0.95; 95% CI. (0.83, 1.09)] or CV mortality [OR 0.89; 95% CI (0.75, 1.05)]. Conclusions Intravenous iron infusion in patients with HF reduces the composite risk of first hospitalization for HF and CV mortality as well as the risks of first and recurrent hospitalizations for HF, with no effect on all‐cause mortality or CV mortality alone.

Machine learning and artificial intelligence are generating significant attention in the scientific community and media. Such algorithms have great potential in medicine for personalizing and improving patient care, including in the diagnosis and management of heart failure. Many physicians are familiar with these terms and the excitement surrounding them, but many are unfamiliar with the basics of these algorithms and how they are applied to medicine. Within heart failure research, current applications of machine learning include creating new approaches to diagnosis, classifying patients into novel phenotypic groups, and improving prediction capabilities. In this paper, we provide an overview of machine learning targeted for the practicing clinician and evaluate current applications of machine learning in the diagnosis, classification, and prediction of heart failure.

Key Points Acute heart failure (AHF) is a complex syndrome characterized by the worsening of signs and symptoms of heart failure Clinical evaluation of patients with AHF involves a focused history and physical examination, along with ancillary data from laboratory and diagnostic tests New team-based and device-based approaches to the clinical assessment and management of patients with AHF have improved outcomes and reduced hospitalization Neurohormonal activation, venous congestion, endothelial dysfunction, myocardial injury, and renal dysfunction are central to the pathophysiology of AHF Studies are currently underway to investigate the utility of identifying specific AHF phenotypes for targeted therapeutic interventions Acute heart failure (AHF) is a complex disease characterized by an acute worsening of heart failure symptoms, usually in patients with pre-existing heart failure. Clinical evaluation of patients with AHF involves the assessment of clinical history, a comprehensive physical examination, and laboratory and diagnostic testing. In this Review, Mentz and O'Connor highlight the literature on the clinical evaluation of AHF, in addition to summarizing the pathophysiological mechanisms underlying its development. Acute heart failure (AHF) is a complex syndrome characterized by worsening heart failure (HF) symptoms that requires escalation of therapy. Intrinsic cardiac abnormalities and comorbid conditions, including lung and renal disease, and sleep-disordered breathing, can contribute to the development of AHF. In this Review, we summarize and discuss the literature on the clinical evaluation and underlying pathophysiology of AHF. Important features of AHF evaluation include identification of precipitating factors to the disease, and assessment of circulatory–renal limitations associated with use of HF medications, prior HF hospitalizations, congestion and perfusion profiles, and end-organ dysfunction. The pathophysiological contributions of endothelial dysfunction, neurohormonal activation, venous congestion, and myocardial injury to the development of AHF are also discussed. These potential causative mechanisms provide a framework for clinicians to evaluate and manage patients with AHF and highlight possible future targets for therapies designed to improve clinical outcomes.

Despite initial in-hospital treatment of acute heart failure (HF), some patients experience worsening HF (WHF). There are limited data about the outcomes and characteristics of patients who experience in-hospital WHF. We assessed the characteristics and outcomes of patients with and without WHF in the ASCEND-HF trial. Worsening HF was defined as at least 1 symptom or sign of new, persistent, or WHF requiring additional intravenous inotropic/vasodilator or mechanical therapy during index hospitalization. We assessed the relationship between WHF and 30-day mortality, 30-day mortality or HF hospitalization, and 180-day mortality. We also assessed whether there was a differential association between early (days 1-3) vs late (day ≥4) WHF and outcomes. Of 7,141 patients with acute HF, 354 (5%) experienced WHF. Patients with WHF were more often male and had a history of atrial fibrillation or diabetes, lower blood pressure, and higher creatinine. After risk adjustment, WHF was associated with increased 30-day mortality (odds ratio 13.37, 95% CI 9.85-18.14), 30-day mortality or HF rehospitalization (odds ratio 6.78, 95% CI 5.25-8.76), and 180-day mortality (hazard ratio 3.90, 95% CI 3.14-4.86) (all P values < .0001). There was no evidence of a difference in outcomes between early and late WHF (all P values for comparison ≥ .2). Worsening HF during index hospitalization was associated with worse 30- and 180-day outcomes. Worsening HF may represent an important patient-centered outcome in acute HF and a focus of future treatments.

Background Heart failure (HF), chronic kidney disease (CKD), and type 2 diabetes mellitus (T2DM) are common and interrelated conditions, each with a significant burden of disease. HF and kidney disease progress through pathophysiologic pathways that culminate in end-stage disease, for which T2DM is a major risk factor. Intervention within these pathways can disrupt disease processes and improve patient outcomes. Sodium-glucose cotransporter-2 inhibitors (SGLT2is) have been investigated in patient populations with combinations of T2DM, CKD, and/or HF. However, until recently, the effect of these agents in patients with HF with preserved ejection fraction (HFpEF) was not well studied. Main body The aim of this review is to summarize key information regarding the interaction between HFpEF, CKD, and T2DM and discuss the role of SGLT2 inhibition in the management of patients with comorbid HFpEF and CKD, with or without T2DM. Literature was retrieved using Boolean searches for English-language articles in PubMed and Google Scholar and included terms related to SGLT2is, HFpEF, T2DM, and CKD. The reference lists from retrieved articles were also considered. Conclusion SGLT2is are efficacious and safe in treating HFpEF in patients with comorbid CKD with and without T2DM. The totality of evidence from clinical trials data suggests there are benefits in using SGLT2is across the spectrum of left ventricular ejection fractions, but there may be a potential for different renal effects in the different ejection fraction groups. Further analysis of these clinical trials has highlighted the need to obtain more accurate phenotypes for patients with HF and CKD to better determine which patients might respond to guideline-directed medical therapies, including SGLT2is. Graphical Abstract CI confidence interval, EF ejection fraction, eGFR estimated glomerular filtration rate, HF heart failure, HHF hospitalization for HF, HR hazard ratio, LVEF left ventricular ejection fraction, SGLT2i sodium-glucose cotransporter-2 inhibitor, UACR urine albumin-creatinine ratio. a Mean value, unless otherwise stated, b SGLT2i vs. placebo, c Data reanalyzed using more conventional endpoints (≥ 50% sustained decrease in eGFR, and including renal death) (UACR at baseline not stated in trial reports)

Within the last decade, advancements in left ventricular assist device therapy have allowed patients with end-stage heart failure (HF) to live longer and with better quality of life. Like other life-saving interventions, however, there remains the risk of complications including infections, bleeding episodes, and stroke. The candidate for left ventricular assist device therapy faces complex challenges going forward, both physical and psychological, many of which may benefit from the application of palliative care principles by trained specialists. Despite these advantages, palliative care remains underused in many advanced HF programs. Here, we describe the benefits of palliative care, barriers to use within HF, and specific applications to the integrated care of patients on mechanical circulatory support.

Background Glucagon-like peptide-1 receptor agonists are a viable option for the prevention of Alzheimer’s disease (AD) but the mechanisms of this potential disease modifying action are unclear. We investigated the effects of once-weekly exenatide (EQW) on AD associated proteomic clusters. Methods The Exenatide Study of Cardiovascular Event Lowering study compared the cardiovascular effects of EQW 2 mg with placebo in 13,752 people with type 2 diabetes mellitus. 4,979 proteins were measured (Somascan V0.4) on baseline and 1-year plasma samples of 3,973 participants. C-reactive protein (CRP), ficolin-2 (FCN2), plasminogen activator inhibitor 1 (PAI-1), soluble vascular cell adhesion protein 1 (sVCAM1) and 4 protein clusters were tested in multivariable mixed models. Results EQW affected FCN2 (Cohen’s d -0.019), PAI-1 (Cohen’s d -0.033), sVCAM-1 (Cohen’s d 0.035) and a cytokine-cytokine cluster (Cohen’s d 0.037) significantly compared with placebo. These effects were sustained in individuals over the age of 65 but not in those under 65. Conclusions EQW treatment was associated with significant change in inflammatory proteins associated with AD. Trial Registration EXSCEL is registered on ClinicalTrials.gov: NCT01144338 on 10th of June 2010.

A subset of patients hospitalized with acute heart failure experiences in-hospital worsening heart failure, defined as persistent or worsening signs or symptoms requiring an escalation of therapy. We analyzed data from the Acute Decompensated Heart Failure National Registry (ADHERE) linked to Medicare claims to develop and validate a risk model for in-hospital worsening heart failure. Our definition of in-hospital worsening heart failure included events such as escalation of medical therapy (eg, inotropic medications) >12hours after admission. We considered candidate risk prediction variables routinely assessed at admission, including age, medical history, biomarkers, and renal function. We used logistic regression with robust standard errors to generate a risk model in a 66% random derivation sample; we validated the model in the remaining 34%. We evaluated the calibration and discrimination of the model in both samples. We evaluated 23,696 patients hospitalized with acute heart failure. Baseline characteristics were well matched in the derivation and validation samples, and the occurrence of in-hospital worsening heart failure was similar in both samples (15.4% and 15.6%, respectively). In the multivariable model, the strongest predictors of in-hospital worsening heart failure were increased troponin and creatinine. The model was well calibrated and had good discrimination in the derivation sample (c statistic, 0.74) and validation sample (c statistic, 0.72). The ADHERE worsening heart failure risk model is a clinical tool with good discrimination for use in patients hospitalized with acute heart failure to identify those at increased risk for in-hospital worsening heart failure. This tool may be useful to target treatment strategies for patients at high risk for in-hospital worsening heart failure.