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.
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.
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.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Congestion and volume overload are the hallmarks of acute decompensated heart failure (ADHF), and loop diuretics have historically been the cornerstone of treatment. The demonstrated efficacy of loop ...diuretics in managing congestion is balanced by the recognized limitations of diuretic resistance, neurohormonal activation, and worsening renal function. However, the recently published DOSE (Diuretic Optimization Strategies Evaluation) trial suggests that previous concerns about the safety of high-dose diuretics may not be valid. There has been a growing interest in alternative strategies to manage volume retention in ADHF with improved efficacy and safety profiles. Peripheral venovenous ultrafiltration (UF) represents a potentially promising approach to volume management in ADHF. Small studies suggest that UF may allow for more effective fluid removal compared with diuretics, with improved quality of life and reduced rehospitalization rates. However, further investigation is needed to completely define the role of UF in patients with ADHF. This review summarizes available data on the use of both diuretics and UF in ADHF patients and identifies challenges and unresolved questions for each approach.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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