Diagnosis of heart failure with preserved ejection fraction: which parameters and diagnostic strategies are more valuable?

Shuai, Xinxin; Chen, Yu-Yuan; Yongxin, Lu; Su, Guanhua; Wang, Yuhui; Zhao, Hongfeng; Han, Jin H.

doi:10.1093/eurjhf/hfr053

Cited by 44 publications

(44 citation statements)

References 31 publications

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“…Finally, our results may not generalize to other ethnicities or to normal weight individuals. However, results similar to ours have been reported previously in various settings 11, 12, 13…”

Section: Discussionsupporting

confidence: 93%

Low NT‐proBNP levels in overweight and obese patients do not rule out a diagnosis of heart failure with preserved ejection fraction

et al. 2018

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BackgroundHeart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome that presents clinicians with a diagnostic challenge. The use of natriuretic peptides to exclude a diagnosis of HFpEF has been proposed. We sought to compare HFpEF patients with N‐terminal pro‐brain natriuretic peptide (NT‐proBNP) level above and below the proposed cut‐off.MethodsStable patients (n = 30) with left ventricular (LV) ejection fraction ≥ 50% were eligible if they had a diagnosis of HF according to the European Society of Cardiology diagnostic criteria. Characteristics of patients with NT‐proBNP below (≤125 pg/mL) and above (>125 pg/mL) the diagnostic criterion were compared.ResultsThere were 19 (66%) women with median age 54 years. Half were African American (16, 53%), and most were obese. There were no significant differences in clinical characteristics or medication use between groups. LV end‐diastolic volume index was greater in high NT‐proBNP patients (P = 0.03). Left atrial volume index, E/e′ ratio, and E/e′ ratio at peak exercise were not significantly different between NT‐proBNP groups. Peak oxygen consumption (VO2), VO2 at ventilatory threshold, and ventilatory efficiency measures were impaired in all patients and were not significantly different between high and low NT‐proBNP patients.ConclusionsNT‐proBNP was below the proposed diagnostic cut‐off point of 125 pg/mL in half of this obese study cohort. Cardiac diastolic dysfunction and cardiorespiratory fitness were not significantly different between high and low NT‐proBNP patients. These data indicate that excluding the diagnosis of HFpEF based solely on NT‐proBNP levels should be discouraged.

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“…Finally, our results may not generalize to other ethnicities or to normal weight individuals. However, results similar to ours have been reported previously in various settings 11, 12, 13…”

Section: Discussionsupporting

confidence: 93%

Low NT‐proBNP levels in overweight and obese patients do not rule out a diagnosis of heart failure with preserved ejection fraction

et al. 2018

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“…Lower proBNP-108 expression in HFPEF than in HFREF also explains the low positive predictive value of BNP for the diagnosis of HFPEF. 42 Acute BNP administration was recently reported to lower diastolic LV stiffness and to increase myocardial titin phosphorylation in an old hypertensive HFPEF dog model 9 but failed to improve clinical end points in acutely decompensated HF patients with LVEF Ն40%. 43 Finally, the present study supports use of statins in HFPEF because of their antioxidative properties.…”

Section: Therapeutic Implicationsmentioning

confidence: 99%

Low Myocardial Protein Kinase G Activity in Heart Failure With Preserved Ejection Fraction

Hamdani²,

et al. 2012

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Background-Prominent features of myocardial remodeling in heart failure with preserved ejection fraction (HFPEF) are high cardiomyocyte resting tension (F passive ) and cardiomyocyte hypertrophy. In experimental models, both reacted favorably to raised protein kinase G (PKG) activity. The present study assessed myocardial PKG activity, its downstream effects on cardiomyocyte F passive and cardiomyocyte diameter, and its upstream control by cyclic guanosine monophosphate (cGMP), nitrosative/oxidative stress, and brain natriuretic peptide (BNP). To discern altered control of myocardial remodeling by PKG, HFPEF was compared with aortic stenosis and HF with reduced EF (HFREF). Methods and Results-Patients with HFPEF (nϭ36), AS (nϭ67), and HFREF (nϭ43) were free of coronary artery disease. More HFPEF patients were obese (PϽ0.05) or had diabetes mellitus (PϽ0.05). Left ventricular myocardial biopsies were procured transvascularly in HFPEF and HFREF and perioperatively in aortic stenosis. F passive was measured in cardiomyocytes before and after PKG administration. Myocardial homogenates were used for assessment of PKG activity, cGMP concentration, proBNP-108 expression, and nitrotyrosine expression, a measure of nitrosative/oxidative stress. Additional quantitative immunohistochemical analysis was performed for PKG activity and nitrotyrosine expression. Lower PKG activity in HFPEF than in aortic stenosis (PϽ0.01) or HFREF (PϽ0.001) was associated with higher cardiomyocyte F passive (PϽ0.001) and related to lower cGMP concentration (PϽ0.001) and higher nitrosative/oxidative stress (PϽ0.05). Higher F passive in HFPEF was corrected by in vitro PKG administration. Conclusions-Low myocardial PKG activity in HFPEF was associated with raised cardiomyocyte F passive and was related to increased myocardial nitrosative/oxidative stress. The latter was probably induced by the high prevalence in HFPEF of metabolic comorbidities. Correction of myocardial PKG activity could be a target for specific HFPEF treatment. (Circulation. 2012;126:830-839.)

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“…This allows us to study the variability of velocity patterns-which is our primary objective. Therefore, our method differs from other supervised attempts to characterize the HFPEF syndrome ( Shuai et al, 2011;Zordoky et al, 2015 ) since we do not compromise the learning process by involving possibly unreliable labels. In our case, the agreement with clinical labels only serves to determine the configuration and parameters of the algorithm and to check the coherence of the data spread.…”

Section: Proposed Approachmentioning

confidence: 99%

Characterization of myocardial motion patterns by unsupervised multiple kernel learning

Sanchez-Martinez

Duchateau

Erdei

et al. 2017

Medical Image Analysis

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We propose an independent objective method to characterize different patterns of functional responses to stress in the heart failure with preserved ejection fraction (HFPEF) syndrome by combining multiple temporally-aligned myocardial velocity traces at rest and during exercise, together with temporal information on the occurrence of cardiac events (valves openings/closures and atrial activation). The method builds upon multiple kernel learning, a machine learning technique that allows the combination of data of different nature and the reduction of their dimensionality towards a meaningful representation (output space). The learning process is kept unsupervised, to study the variability of the input traces without being conditioned by data labels. To enhance the physiological interpretation of the output space, the variability that it encodes is analyzed in the space of input signals after reconstructing the velocity traces via multiscale kernel regression. The methodology was applied to 2D sequences from a stress echocardiography protocol from 55 subjects (22 healthy, 19 HFPEF and 14 breathless subjects). The results confirm that characterization of the myocardial functional response to stress in the HFPEF syndrome may be improved by the joint analysis of multiple relevant features.

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Diagnosis of heart failure with preserved ejection fraction: which parameters and diagnostic strategies are more valuable?

Cited by 44 publications

References 31 publications

Low NT‐proBNP levels in overweight and obese patients do not rule out a diagnosis of heart failure with preserved ejection fraction

Low NT‐proBNP levels in overweight and obese patients do not rule out a diagnosis of heart failure with preserved ejection fraction

Low Myocardial Protein Kinase G Activity in Heart Failure With Preserved Ejection Fraction

Characterization of myocardial motion patterns by unsupervised multiple kernel learning

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