Literature DB >> 30718020

Contribution of Cardiovascular Reserve to Prognostic Categories of Heart Failure With Preserved Ejection Fraction: A Classification Based on Machine Learning.

Monika Przewlocka-Kosmala1, Thomas H Marwick2, Andrzej Dabrowski3, Wojciech Kosmala4.   

Abstract

BACKGROUND: The authors used cluster analysis of data from cardiovascular domains associated with exercise intolerance to help define prognostic phenotypes of patients with heart failure with preserved ejection fraction (HFpEF).
METHODS: Resting and postexercise echocardiography was performed in 177 patients with HFpEF and 51 asymptomatic control subjects sharing a common clinical profile. Patterns of features that determine exercise capacity were sought from automated hierarchical clustering of left ventricular (LV) diastolic and systolic function, left atrial function, right ventricular function, ventricular-arterial coupling, chronotropic reserve and myocardial fibrosis.
RESULTS: Automated clustering separated a distinct subgroup characterized by a relatively isolated impairment of LV systolic reserve. The clinical factors identified by this process were used to define two phenotypes of patients with symptomatic HFpEF: those with reduced chronotropic and/or diastolic reserve (abnormal CR/DR; n = 137) and those with preserved heart rate reserve and exertional E/e' ratio < 14 (normal CR/DR; n = 40). Change in global LV strain rate from rest to exercise was similar in patients with abnormal CR/DR (0.16 ± 0.18 sec-1) and those with normal CR/DR (0.21 ± 0.17 sec-1) and significantly lower than in asymptomatic subjects (0.54 ± 0.20 sec-1; P < .001 for all). However, although the former group also showed abnormal longitudinal deformation, ventricular-arterial coupling, and cardiac output responses to exercise, the latter group showed only reduced LV systolic reserve. The normal CR/DR group had a lower incidence of cardiovascular hospitalization or death (P = .003) and heart failure hospitalization (P = .002) than the abnormal CR/DR group during 2-year follow-up.
CONCLUSIONS: Diminished LV systolic reserve may represent the major identifiable cardiac functional abnormality associated with exercise intolerance in some patients with HFpEF. Despite significant functional limitation, these patients are characterized by a better prognosis than subjects with HFpEF with more physiologic abnormalities.
Copyright © 2019 American Society of Echocardiography. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Chronotropic reserve; Diastolic reserve; Exercise capacity; HFpEF; Heart failure with preserved ejection fraction; Systolic reserve

Mesh:

Substances:

Year:  2019        PMID: 30718020     DOI: 10.1016/j.echo.2018.12.002

Source DB:  PubMed          Journal:  J Am Soc Echocardiogr        ISSN: 0894-7317            Impact factor:   5.251


  14 in total

1.  On the search for the right definition of heart failure with preserved ejection fraction.

Authors:  Agnieszka Kapłon-Cieślicka; Karolina Kupczyńska; Piotr Dobrowolski; Błażej Michalski; Miłosz J Jaguszewski; Waldemar Banasiak; Paweł Burchardt; Łukasz Chrzanowski; Szymon Darocha; Justyna Domienik-Karłowicz; Jarosław Drożdż; Marcin Fijałkowski; Krzysztof J Filipiak; Marcin Gruchała; Ewa A Jankowska; Piotr Jankowski; Jarosław D Kasprzak; Wojciech Kosmala; Piotr Lipiec; Przemysław Mitkowski; Katarzyna Mizia-Stec; Piotr Szymański; Agnieszka Tycińska; Wojciech Wańha; Maciej Wybraniec; Adam Witkowski; Piotr Ponikowski; On Behalf Of "Club 30" Of The Polish Cardiac Society
Journal:  Cardiol J       Date:  2020-09-28       Impact factor: 2.737

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Review 4.  The future of heart failure with preserved ejection fraction : Deep phenotyping for targeted therapeutics.

Authors:  Frank R Heinzel; Sanjiv J Shah
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5.  Association of Machine Learning-Derived Phenogroupings of Echocardiographic Variables with Heart Failure in Stable Coronary Artery Disease: The Heart and Soul Study.

Authors:  Rakesh K Mishra; Geoffrey H Tison; Qizhi Fang; Rebecca Scherzer; Mary A Whooley; Nelson B Schiller
Journal:  J Am Soc Echocardiogr       Date:  2020-01-14       Impact factor: 5.251

6.  20th Annual Feigenbaum Lecture: Echocardiography for Precision Medicine-Digital Biopsy to Deconstruct Biology.

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8.  Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility.

Authors:  Amitava Banerjee; Suliang Chen; Ghazaleh Fatemifar; Mohamad Zeina; R Thomas Lumbers; Johanna Mielke; Simrat Gill; Dipak Kotecha; Daniel F Freitag; Spiros Denaxas; Harry Hemingway
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Review 9.  Artificial Intelligence, Machine Learning, and Cardiovascular Disease.

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Journal:  Clin Med Insights Cardiol       Date:  2020-09-09

10.  Identification of distinct phenotypic clusters in heart failure with preserved ejection fraction.

Authors:  Alicia Uijl; Gianluigi Savarese; Ilonca Vaartjes; Ulf Dahlström; Jasper J Brugts; Gerard C M Linssen; Vanessa van Empel; Hans-Peter Brunner-La Rocca; Folkert W Asselbergs; Lars H Lund; Arno W Hoes; Stefan Koudstaal
Journal:  Eur J Heart Fail       Date:  2021-05-01       Impact factor: 15.534

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