Phenotyping of acute decompensated heart failure with preserved ejection fraction

Heart ◽  
2022 ◽  
pp. heartjnl-2021-320270
Author(s):  
Yohei Sotomi ◽  
Shungo Hikoso ◽  
Sho Komukai ◽  
Taiki Sato ◽  
Bolrathanak Oeun ◽  
...  

ObjectiveThe pathophysiological heterogeneity of heart failure with preserved ejection fraction (HFpEF) makes the conventional ‘one-size-fits-all’ treatment approach difficult. We aimed to develop a stratification methodology to identify distinct subphenotypes of acute HFpEF using the latent class analysis.MethodsWe established a prospective, multicentre registry of acute decompensated HFpEF. Primary candidates for latent class analysis were patient data on hospital admission (160 features). The patient subset was categorised based on enrolment period into a derivation cohort (2016–2018; n=623) and a validation cohort (2019–2020; n=472). After excluding features with significant missingness and high degree of correlation, 83 features were finally included in the analysis.ResultsThe analysis subclassified patients (derivation cohort) into 4 groups: group 1 (n=215, 34.5%), characterised by arrythmia triggering (especially atrial fibrillation) and a lower comorbidity burden; group 2 (n=77, 12.4%), with substantially elevated blood pressure and worse classical HFpEF echocardiographic features; group 3 (n=149, 23.9%), with the highest level of GGT and total bilirubin and frequent previous hospitalisation for HF and group 4 (n=182, 29.2%), with infection-triggered HF hospitalisation, high C reactive protein and worse nutritional status. The primary end point—a composite of all-cause death and HF readmission—significantly differed between the groups (log-rank p<0.001). These findings were consistent in the validation cohort.ConclusionsThis study indicated the feasibility of clinical application of the latent class analysis in a highly heterogeneous cohort of patients with acute HFpEF. Patients can be divided into 4 phenotypes with distinct patient characteristics and clinical outcomes.Trial registration numberUMIN000021831.

Author(s):  
Matthias Unterhuber ◽  
Karl-Philipp Rommel ◽  
Karl-Patrik Kresoja ◽  
Julia Lurz ◽  
Jelena Kornej ◽  
...  

Abstract Background Heart failure with preserved ejection fraction (HFpEF) is a rapidly growing global health problem. To date, diagnosis of HFpEF is based on clinical, invasive and laboratory examinations. Electrocardiographic findings may vary, and there are no known typical ECG features for HFpEF. Methods This study included two patient cohorts. In the derivation cohort, we included n = 1884 patients who presented with exertional dyspnea or equivalent and preserved ejection fraction (≥50%) and clinical suspicion for coronary artery disease. The ECGs were divided in segments, yielding a total of 77.558 samples. We trained a convolutional neural network (CNN) to classify HFpEF and control patients according to ESC criteria. An external group of 203 volunteers in a prospective heart failure screening program served as validation cohort of the CNN. Results The external validation of the CNN yielded an AUC of 0.80 (95% CI 0.74–0.86) for detection of HFpEF according to ESC criteria, with a sensitivity of 0.99 (CI 0.98–0.99) and a specificity of 0.60 (95% CI 0.56–0.64), with a positive predictive value of 0.68 (95%CI 0.64–0.72) and a negative predictive value of 0.98 (95% CI 0.95–0.99). Conclusion In this study, we report the first deep learning-enabled CNN for identifying patients with HFpEF according to ESC criteria including NT-proBNP measurements in the diagnostic algorithm among patients at risk. The suitability of the CNN was validated on an external validation cohort of patients at risk for developing heart failure, showing a convincing screening performance.


Circulation ◽  
2020 ◽  
Vol 142 (21) ◽  
pp. 2029-2044
Author(s):  
Sandra Sanders-van Wijk ◽  
Jasper Tromp ◽  
Lauren Beussink-Nelson ◽  
Camilla Hage ◽  
Sara Svedlund ◽  
...  

Background: A systemic proinflammatory state has been hypothesized to mediate the association between comorbidities and abnormal cardiac structure/function in heart failure with preserved ejection fraction (HFpEF). We conducted a proteomic analysis to investigate this paradigm. Methods: In 228 patients with HFpEF from the multicenter PROMIS-HFpEF study (Prevalence of Microvascular Dysfunction in Heart Failure With Preserved Ejection Fraction), 248 unique circulating proteins were quantified by a multiplex immunoassay (Olink) and used to recapitulate systemic inflammation. In a deductive approach, we performed principal component analysis to summarize 47 proteins known a priori to be involved in inflammation. In an inductive approach, we performed unbiased weighted coexpression network analyses of all 248 proteins to identify clusters of proteins that overrepresented inflammatory pathways. We defined comorbidity burden as the sum of 8 common HFpEF comorbidities. We used multivariable linear regression and statistical mediation analyses to determine whether and to what extent inflammation mediates the association of comorbidity burden with abnormal cardiac structure/function in HFpEF. We also externally validated our findings in an independent cohort of 117 HFpEF cases and 30 comorbidity controls without heart failure. Results: Comorbidity burden was associated with abnormal cardiac structure/function and with principal components/clusters of inflammation proteins. Systemic inflammation was also associated with increased mitral E velocity, E/e′ ratio, and tricuspid regurgitation velocity; and worse right ventricular function (tricuspid annular plane systolic excursion and right ventricular free wall strain). Inflammation mediated the association between comorbidity burden and mitral E velocity (proportion mediated 19%–35%), E/e′ ratio (18%–29%), tricuspid regurgitation velocity (27%–41%), and tricuspid annular plane systolic excursion (13%) ( P <0.05 for all), but not right ventricular free wall strain. TNFR1 (tumor necrosis factor receptor 1), UPAR (urokinase plasminogen activator receptor), IGFBP7 (insulin-like growth factor binding protein 7), and GDF-15 (growth differentiation factor-15) were the top individual proteins that mediated the relationship between comorbidity burden and echocardiographic parameters. In the validation cohort, inflammation was upregulated in HFpEF cases versus controls, and the most prominent inflammation protein cluster identified in PROMIS-HFpEF was also present in HFpEF cases (but not controls) in the validation cohort. Conclusions: Proteins involved in inflammation form a conserved network in HFpEF across 2 independent cohorts and may mediate the association between comorbidity burden and echocardiographic indicators of worse hemodynamics and right ventricular dysfunction. These findings support the comorbidity-inflammation paradigm in HFpEF.


2020 ◽  
Vol 21 (Supplement_1) ◽  
Author(s):  
C Morbach ◽  
C Henneges ◽  
F Sahiti ◽  
M Breunig ◽  
V Cejka ◽  
...  

Abstract Funding Acknowledgements German Research Foundation (BMBF 01EO1004 and 01EO1504) OnBehalf AHF Background & Aims Heart failure (HF) is classified according to left ventricular (LV) ejection fraction (EF) into heart failure with reduced (HFrEF) and heart failure with preserved EF (HFpEF). In 2016, a third subgroup, heart failure with mid-range EF (HFmrEF), has been introduced by the ESC. We aimed to identify the number of naturally occurring heart failure subgroups according to LVEF using latent class analysis. Methods The AHF registry is a monocentric prospective follow-up study that comprehensively phenotypes consecutive patients hospitalized for acute heart failure (AHF). Echocardiography was performed within 72 hours prior to discharge. We first estimated the distribution of LVEF using histogram and kernel density estimation methods (bandwidth was selected by biased cross-validation). We then fitted Gaussian Mixture Models with increasing number of components to the data. To select the optimal number of components we calculated the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). The minimum of each criterion suggests the optimal number of components for the final model. The BIC requires more data to select more components than the AIC and hence is more conservative. Finally, for each criterion the optimal model was determined. Results Out of 629 patients, 585 (93%) patients received echocardiography and in 498 (79.2%) the LVEF could be calculated using Simpson´s biplane or monoplane method. The BIC suggested two (panel B), the AIC three components (panel A). In the two-component model, mean ± SD LVEF values were 60.2 ± 8.7% and 30.8 ± 9.6%, thus covering 56% and 44% of patients, respectively (panel D). In the three-component model, respective LVEF values were 64.9 ± 6.2%, 50.2 ± 6.9%, and 28.4 ± 8.1%, thus covering 35%, 27%, and 38% of patients (panel C). Conclusions Our analysis suggests that LVEF in patients with AHF is not a continuum, but clusters in two or three subgroups. In line with the HFrEF and HFpEF classification, the more conservative model suggested two subgroups of LVEF. The less restrictive model allowed for a third subgroup, compatible with HFmrEF. Future analyses will better characterize the identified subgroups. Abstract P1432 Figure


2020 ◽  
Vol 60 (1) ◽  
pp. 208
Author(s):  
Karen McKendrick ◽  
Laura Gelfman ◽  
Harriet Mather ◽  
Nathan Goldstein ◽  
R. Sean Morrison

Heart ◽  
2017 ◽  
Vol 104 (6) ◽  
pp. 525-532 ◽  
Author(s):  
Ki Hong Choi ◽  
Ga Yeon Lee ◽  
Jin-Oh Choi ◽  
Eun-Seok Jeon ◽  
Hae-Young Lee ◽  
...  

ObjectiveThere are conflicting results among previous studies regarding the prognosis of heart failure with preserved ejection fraction (HFpEF) compared with heart failure with reduced ejection fraction (HFrEF). This study aimed to compare the outcomes of patients with de novo acute heart failure (AHF) or acute decompensated HF (ADHF) according to HFpEF (EF≥50%), or HFrEF (EF<40%) and to define the prognosis of patients with HF with mid-range EF (HFmrEF, 40≤EF<50%).MethodsBetween March 2011 and February 2014, 5625 consecutive patients with AHF were recruited from 10 university hospitals. A total of 5414 (96.2%) patients with EF data were enrolled, which consisted of 2867 (53.0%) patients with de novo and 2547 (47.0%) with ADHF. Each of the enrolled group was stratified by EF.ResultsIn de novo, all-cause death rates were not significantly different between HFpEF and HFrEF (HFpEF vs HFrEF, 206/744 (27.7%) vs 438/1631 (26.9%), HRadj 1.15, 95% CI 0.96 to 1.38, p=0.14). However, among patients with ADHF, HFrEF had a significantly higher mortality rate compared with HFpEF (HFpEF vs HFrEF, 245/613 (40.0%) vs 694/1551 (44.7%), HRadj 1.25, 95% CI 1.06 to 1.47, p=0.007). Also, in ADHF, HFmrEF was associated with a significantly lower mortality rate within 1 year compared with HFrEF (HFmrEF vs HFrEF, 88/383 (23.0%) vs 430/1551 (27.7%), HRadj 1.31, 95% CI 1.03 to 1.65, p=0.03), but a significantly higher mortality rate after 1 year compared with HFpEF (HFmrEF vs HFpEF, 83/295 (28.1%) vs 101/469 (21.5%), HRadj 0.70, 95% CI 0.52 to 0.96, p=0.02).ConclusionsHFpEF may indicate a better prognosis compared with HFrEF in ADHF, but not in de novo AHF. For patients with ADHF, the prognosis associated with HFmrEF was similar to that of HFpEF within the first year following hospitalisation and similar to HFrEF 1  year after hospitalisation.


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