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10.1007/s11886-020-01349-3

http://scihub22266oqcxt.onion/10.1007/s11886-020-01349-3
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32648130!7347676!32648130
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suck abstract from ncbi


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pmid32648130      Curr+Cardiol+Rep 2020 ; 22 (9): 82
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  • Heart Failure with Preserved Ejection Fraction-a Concise Review #MMPMID32648130
  • Adamczak DM; Oduah MT; Kiebalo T; Nartowicz S; Beben M; Pochylski M; Cieplucha A; Gwizdala A; Lesiak M; Straburzynska-Migaj E
  • Curr Cardiol Rep 2020[Jul]; 22 (9): 82 PMID32648130show ga
  • PURPOSE OF REVIEW: Heart failure with preserved ejection fraction (HFpEF) is a relatively new disease entity used in medical terminology; however, both the number of patients and its clinical significance are growing. HFpEF used to be seen as a mild condition; however, the symptoms and quality of life of the patients are comparable to those with reduced ejection fraction. The disease is much more complex than previously thought. In this article, information surrounding the etiology, diagnosis, prognosis, and possible therapeutic options of HFpEF are reviewed and summarized. RECENT FINDINGS: It has recently been proposed that heart failure (HF) is rather a heterogeneous syndrome with a spectrum of overlapping and distinct characteristics. HFpEF itself can be distilled into different phenotypes based on the underlying biology. The etiological factors of HFpEF are unclear; however, systemic low-grade inflammation and microvascular damage as a consequence of comorbidities associated with endothelial dysfunction, oxidative stress, myocardial remodeling, and fibrosis are considered to play a crucial role in the pathogenesis of a disease. The H(2)FPEF score and the HFpEF nomogram are recently validated highly sensitive tools employed for risk assessment of subclinical heart failure. Despite numerous studies, there is still no evidence-based pharmacotherapy for HFpEF and the mortality and morbidity associated with HFpEF remain high. A better understanding of the etiological factors, the impact of comorbidities, the phenotypes of the disease, and implementation of machine learning algorithms may play a key role in the development of future therapeutic strategies.
  • |*Heart Failure[MESH]
  • |*Ventricular Dysfunction, Left[MESH]
  • |Aged[MESH]
  • |Heart Ventricles[MESH]
  • |Humans[MESH]
  • |Middle Aged[MESH]
  • |Quality of Life[MESH]
  • |Stroke Volume[MESH]


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