Latent Class Analysis to Determine Patients with Advanced Heart Failure at Highest Risk of Poor Outcomes (RP314)

2020 ◽  
Vol 60 (1) ◽  
pp. 208
Author(s):  
Karen McKendrick ◽  
Laura Gelfman ◽  
Harriet Mather ◽  
Nathan Goldstein ◽  
R. Sean Morrison
2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Jessica Harman Thompson ◽  
Kenneth M. Faulkner ◽  
Christopher S. Lee

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.


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


2009 ◽  
Author(s):  
Tomoko Udo ◽  
Jennifer F. Buckman ◽  
Marsha E. Bates ◽  
Evgeny Vaschillo ◽  
Bronya Vaschillo ◽  
...  

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