The future of heart failure with preserved ejection fraction View Full Text


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Article Info

DATE

2022-06-29

AUTHORS

Frank R. Heinzel, Sanjiv J. Shah

ABSTRACT

Heart failure (HF) with preserved ejection fraction (HFpEF) is a multi-organ, systemic syndrome that involves multiple cardiac and extracardiac pathophysiologic abnormalities. Because HFpEF is a heterogeneous syndrome and resistant to a “one-size-fits-all” approach it has proven to be very difficult to treat. For this reason, several research groups have been working on methods for classifying HFpEF and testing targeted therapeutics for the HFpEF subtypes identified. Apart from conventional classification strategies based on comorbidity, etiology, left ventricular remodeling, and hemodynamic subtypes, researchers have been combining deep phenotyping with innovative analytical strategies (e.g., machine learning) to classify HFpEF into therapeutically homogeneous subtypes over the past few years. Despite the growing excitement for such approaches, there are several potential pitfalls to their use, and there is a pressing need to follow up on data-driven HFpEF subtypes in order to determine their underlying mechanisms and molecular basis. Here we provide a framework for understanding the phenotype-based approach to HFpEF by reviewing (1) the historical context of HFpEF; (2) the current HFpEF paradigm of comorbidity-induced inflammation and endothelial dysfunction; (3) various methods of sub-phenotyping HFpEF; (4) comorbidity-based classification and treatment of HFpEF; (5) machine learning approaches to classifying HFpEF; (6) examples from HFpEF clinical trials; and (7) the future of phenomapping (machine learning and other advanced analytics) for the classification of HFpEF. More... »

PAGES

308-323

References to SciGraph publications

  • 2017-06-05. Text Mining of the Electronic Health Record: An Information Extraction Approach for Automated Identification and Subphenotyping of HFpEF Patients for Clinical Trials in JOURNAL OF CARDIOVASCULAR TRANSLATIONAL RESEARCH
  • 2016-10-31. How to Develop and Implement a Specialized Heart Failure with Preserved Ejection Fraction Clinical Program in CURRENT CARDIOLOGY REPORTS
  • 2019-07-18. Approaching Higher Dimension Imaging Data Using Cluster-Based Hierarchical Modeling in Patients with Heart Failure Preserved Ejection Fraction in SCIENTIFIC REPORTS
  • 2021-05-11. Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms in NATURE COMMUNICATIONS
  • 2019-04-05. Effects of blood pressure lowering in patients with heart failure with preserved ejection fraction: a systematic review and meta-analysis in HYPERTENSION RESEARCH
  • 2017-06. Innovative Clinical Trial Designs for Precision Medicine in Heart Failure with Preserved Ejection Fraction in JOURNAL OF CARDIOVASCULAR TRANSLATIONAL RESEARCH
  • 2021-12-08. Combinatorial, additive and dose-dependent drug–microbiome associations in NATURE
  • 2017-03-03. Phenomapping for the Identification of Hypertensive Patients with the Myocardial Substrate for Heart Failure with Preserved Ejection Fraction in JOURNAL OF CARDIOVASCULAR TRANSLATIONAL RESEARCH
  • 2021-02-19. Diabetic phenotype and prognosis of patients with heart failure and preserved ejection fraction in a real life cohort in CARDIOVASCULAR DIABETOLOGY
  • 2021-03-01. Disproportionate left atrial myopathy in heart failure with preserved ejection fraction among participants of the PROMIS-HFpEF study in SCIENTIFIC REPORTS
  • 2020-03-30. Evaluation and management of heart failure with preserved ejection fraction in NATURE REVIEWS CARDIOLOGY
  • 2021-01-11. Cellular and molecular pathobiology of heart failure with preserved ejection fraction in NATURE REVIEWS CARDIOLOGY
  • 2021-05-11. A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy in NATURE COMMUNICATIONS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00059-022-05124-8

    DOI

    http://dx.doi.org/10.1007/s00059-022-05124-8

    DIMENSIONS

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    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/35767073


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