Literature DB >> 32594605

Cardiac mechanics in heart failure with preserved ejection fraction.

Karthik Seetharam1, Partho P Sengupta1, Christopher M Bianco1.   

Abstract

Heart failure with preserved ejection fraction (HFpEF) is a complex clinical entity associated with significant morbidity and mortality. Common comorbidities including hypertension, coronary artery disease, diabetes, chronic kidney disease, obesity, and increasing age predispose to preclinical diastolic dysfunction that often progresses to frank HFpEF. Clinical HFpEF is typically associated with some degree of diastolic dysfunction, but can occur in the absence of many conventional diastolic dysfunction indices. The exact biologic links between risk factors, structural changes, and clinical manifestations are not clearly apparent. Innovative approaches including deformation imaging have enabled deeper understanding of HFpEF cardiac mechanics beyond conventional metrics. Furthermore, predictive analytics through data-driven platforms have allowed for a deeper understanding of HFpEF phenotypes. This review focuses on the changes in cardiac mechanics that occur through preclinical myocardial dysfunction to clinically apparent HFpEF.
© 2020 Wiley Periodicals LLC.

Entities:  

Keywords:  HFpEF; diastolic dysfunction; machine learning; precision phenotyping

Mesh:

Year:  2020        PMID: 32594605     DOI: 10.1111/echo.14764

Source DB:  PubMed          Journal:  Echocardiography        ISSN: 0742-2822            Impact factor:   1.724


  3 in total

Review 1.  Artificial Intelligence and Machine Learning in Cardiovascular Imaging.

Authors:  Karthik Seetharam; James K Min
Journal:  Methodist Debakey Cardiovasc J       Date:  2020 Oct-Dec

Review 2.  Evaluating the adverse outcome of subtypes of heart failure with preserved ejection fraction defined by machine learning: A systematic review focused on defining high risk phenogroups.

Authors:  Simon W Rabkin
Journal:  EXCLI J       Date:  2022-02-22       Impact factor: 4.068

Review 3.  Applications of Machine Learning in Cardiology.

Authors:  Karthik Seetharam; Sudarshan Balla; Christopher Bianco; Jim Cheung; Roman Pachulski; Deepak Asti; Nikil Nalluri; Astha Tejpal; Parvez Mir; Jilan Shah; Premila Bhat; Tanveer Mir; Yasmin Hamirani
Journal:  Cardiol Ther       Date:  2022-07-12
  3 in total

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