2020
DOI: 10.1016/j.jchf.2019.09.009
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Clinical Phenogroups in Heart Failure With Preserved Ejection Fraction

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Cited by 226 publications
(146 citation statements)
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References 30 publications
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“…Phenotypic presentations of HFpEF may vary widely across patients and determine the choice of diagnostic tests and targeted management plan [ 39 ••, 43 46 ]. There are four clinically distinct phenotypes of HFpEF that have been recognized [ 47 ]: Aging phenotype Obesity phenotype Pulmonary hypertension (PH) phenotype Coronary artery disease (CAD) phenotype …”
Section: Hfpef Phenotypesmentioning
confidence: 99%
“…Phenotypic presentations of HFpEF may vary widely across patients and determine the choice of diagnostic tests and targeted management plan [ 39 ••, 43 46 ]. There are four clinically distinct phenotypes of HFpEF that have been recognized [ 47 ]: Aging phenotype Obesity phenotype Pulmonary hypertension (PH) phenotype Coronary artery disease (CAD) phenotype …”
Section: Hfpef Phenotypesmentioning
confidence: 99%
“…Lack of evidence-based treatment and diverse phenotypes remain challenging issues in HFpEF management. So far, the approach is mostly individualized and heavily focused on phenotypes and comorbidities as presenting features [ 2 , 11 , 20 , 21 ]. While the pathophysiology-based phenotyping appeared to be a promising approach, its clinical application is restricted by the mixed-mechanism nature of HFpEF [ 2 ].…”
Section: Introductionmentioning
confidence: 99%
“…While the pathophysiology-based phenotyping appeared to be a promising approach, its clinical application is restricted by the mixed-mechanism nature of HFpEF [ 2 ]. Another pragmatic perspective is to focus on clinical variables, such as comorbidity, which were not only easily spotted by physicians but also associated with different long-term outcomes [ 2 , 21 ]. As common phenotypes were observed across population, cardiology experts proposed specific treatment approach and distinct therapeutic response for those frequently presented phenotypes [ 2 , 20 , 21 ].…”
Section: Introductionmentioning
confidence: 99%
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“…To overcome this problem, the precise calculating ability of artificial intelligence helped to stratify HFpEF. Indeed, using several machine-learning algorithms, previous studies clarified the phenotypes and therapeutic strategies for HFpEF; however, the features of heart failure with mid-range ejection fraction may influence the features of unknown phenotypes and RV diastolic function was not taught in previous studies (9)(10)(11)(12)(13). Although RV function plays an important role in the pathophysiology of HFpEF (14), there is a lack of guidance for the assessment and quantification of RV diastolic function (15).…”
Section: Introductionmentioning
confidence: 99%