scholarly journals P1432 Heart failure subgroups according to left ventricular ejection fraction A latent class analysis

2020 ◽  
Vol 21 (Supplement_1) ◽  
Author(s):  
C Morbach ◽  
C Henneges ◽  
F Sahiti ◽  
M Breunig ◽  
V Cejka ◽  
...  
Keyword(s):  
Heart Failure ◽  
Ejection Fraction ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Information Criterion ◽  
Left Ventricular ◽  
Optimal Number ◽  
Component Model ◽  
Class Analysis ◽  
Number Of Components

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

scholarly journals Distribution pattern of left ventricular ejection fraction in patients with decompensated heart failure depends on sex results of a latent class analysis

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
C Morbach ◽  
C Henneges ◽  
F Sahiti ◽  
M Breunig ◽  
V Cejka ◽  
...  
Keyword(s):  
Heart Failure ◽  
Left Ventricular Ejection Fraction ◽  
Ejection Fraction ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Left Ventricular ◽  
Optimal Number ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction ◽  
Female Patients

Abstract Funding Acknowledgements Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): unrestricted grant from Boehringer Ingelheim Background & Aims Since 2016, heart failure (HF) is classified using left ventricular ejection fraction (LVEF) thresholds of 40% and 50%. However, HF phenotypes may develop across the entire LVEF spectrum depending on individual patient characteristics including the risk and comorbidity profile. Using latent class analysis, we explored the sex-specific distribution of in-hospital LVEF in patients hospitalized for acute heart failure (AHF) at a tertiary care center in Germany. Methods Consecutive patients (≥18 years) hospitalized for AHF were recruited and phenotyped prospectively on a 7/24 basis. Exclusion criteria were high output heart failure, cardiogenic shock, and being listed for high urgency cardiac transplantation. LVEF was determined by transthoracic echocardiography using Simpson´s biplane or monoplane method. First, we estimated the distribution of LVEF in both sexes using histogram and kernel density estimation methods (bandwidth was selected by biased cross-validation). Then, Gaussian Mixture Models were fitted with increasing number of components. To identify the optimal number of subgroups we calculated the Bayesian Information Criterion (BIC). The minimum of the BIC criterion suggests the optimal number of subgroups for the final model. This analysis was performed on subsets including only male and only female patients. Results Out of 629 patients (39.8% female) admitted with AHF between 09/2014 and 12/2017, 93% patients received in-hospital echocardiography, and in 79.2% LVEF could be quantitatively assessed. The BIC suggested two subgroups each for male (Fig. A) and female patients (Fig. B). In the male two-subgroup model, mean ± SD LVEF values were 30 ± 9% and 59 ± 8%, thus covering 48% and 52% of the men, respectively (Fig. C). In the female two-subgroup model, respective LVEF values were 36 ± 13% and 65 ± 8%, thus covering 47% and 53% of patients (Fig. D). The "male" model suggested 45% as cut-point, whilst the "female" model suggested 51% as cut-point differentiating between lower and higher LVEF. Conclusions Using non-parametric and parametric statistical approaches, specific subgroups of patients hospitalized with AHF were identified among male and female patients hospitalized for AHF, which each time comprised subgroups with impaired vs. more preserved LVEF. Future analyses in larger AHF cohorts as well as in populations with chronic stable HF are warranted which take also into consideration sex differences in HF aetiology. Figure A) Minimum number of components (BIC) in men. B) Minimum BIC in women. C) LVEF distribution in men (2 components). D) LVEF distribution in women (2 components). The orange line indicates the respective cut-points between low and high LVEF. Abstract Figure.

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 ◽  
...  
Keyword(s):  
Heart Failure ◽  
Ejection Fraction ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Validation Cohort ◽  
Acute Decompensated Heart Failure ◽  
Preserved Ejection Fraction ◽  
Class Analysis ◽  
Derivation Cohort ◽  
Comorbidity Burden

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.

scholarly journals Clinical phenotypes of cardiac sarcoidosis by latent class analysis

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
Q Dai ◽  
B Bose ◽  
P Li ◽  
B Liu ◽  
L Jin ◽  
...  
Keyword(s):  
Heart Failure ◽  
Latent Class Analysis ◽  
Clinical Outcomes ◽  
Ventricular Arrhythmias ◽  
Latent Class ◽  
Cardiac Sarcoidosis ◽  
Information Criterion ◽  
Cardiometabolic Syndrome ◽  
Study Population ◽  
Myocardial Involvement

Abstract Background Sarcoidosis is a systemic granulomatous disease with cardiac involvement reported in 20–27% of patients [1]. Cardiac sarcoidosis (CS) can lead to atrial or ventricular arrhythmias, various conduction system disorders, heart failure or sudden cardiac death, depending on the location of myocardial involvement [2]. Previous studies have investigated the possible types of CS based on the distribution of myocardial involvement on imaging as well as the role of genetic factors [3,4]. However, there are no studies describing the clinical heterogeneity of CS patients. Purpose In order to determine if clinical clusters exist in CS, we carried out a latent class analysis (LCA) to explore potential phenotypes in a large sample of CS patients from the National Inpatient Sample (NIS). Methods We identified 848 patients with a diagnosis of CS from the NIS in 2016–2018. A LCA was performed based on comorbidities. Utilizing the Bayesian information criterion and Akaike's information criterion we divided our study population into 3 cohorts. We subsequently applied the LCA model for our study population to fit each patient into one of the 3 cohorts. Finally, we compared the clinical outcomes among the 3 groups. Results Following LCA, patients in cohort 3 were strongly associated with a cardiometabolic syndrome profile with the highest prevalence of congestive heart failure (CHF, 95.1%), chronic kidney disease (CKD, 69.7%), diabetes mellitus (68.9%), hyperlipidemia (52.5%) and obesity (45.1%). Patients in cohort 2 had an intermediate prevalence of cardiometabolic syndrome with a universal diagnosis of hypertension (100%) but with the lowest number of CHF (32.5%) patients and none with CKD. Finally, patients in cohort 1 had the least comorbidities in comparison to the other groups but there was a higher prevalence of CHF (71.7%). There was no significant difference in mortality among the 3 groups, but acute respiratory failure was the highest in cohort 3. However, ventricular arrhythmias were more prevalent in cohort 1 patients (Table). Conclusion We identified 3 different types of CS based on their clinical phenotype. The clinical outcomes varied among the cohorts with ventricular arrhythmias being the most prevalent in patients with the least cardiometabolic comorbidities. FUNDunding Acknowledgement Type of funding sources: None.

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
Keyword(s):  
Heart Failure ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Advanced Heart Failure ◽  
Class Analysis ◽  
Poor Outcomes

scholarly journals A Multibiomarker Latent Class Analysis in Moderate to Advanced Heart Failure

2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Jessica Harman Thompson ◽  
Kenneth M. Faulkner ◽  
Christopher S. Lee
Keyword(s):  
Heart Failure ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Advanced Heart Failure ◽  
Class Analysis

scholarly journals Heterogeneity of Learners’ Behavioral Patterns of Watching Videos and Completing Assessments in Massive Open Online Courses (MOOCs): A Latent Class Analysis

2020 ◽  
Vol 21 (4) ◽  
pp. 222-237
Author(s):  
In Gu Kang
Keyword(s):  
Latent Class Analysis ◽  
Low Income ◽  
Latent Class ◽  
Online Courses ◽  
Optimal Number ◽  
Massive Open Online Courses ◽  
Behavioral Patterns ◽  
Class Analysis ◽  
Massive Open Online ◽  
Latent Subgroups

Massive open online courses (MOOCs) have been touted as an effective way to make higher education accessible for free or for only a small fee, thus addressing the problem of unequal access and providing new opportunities to young people in middle and low income groups. However, many critiques of MOOCs have indicated that low completion rates are a major concern. Using a latent class analysis (LCA), a more advanced methodology to identify latent subgroups, this study examined the heterogeneity of learners’ behavioral patterns in a MOOC, categorized them into distinctive subgroups, and ultimately determined the optimal number of latent subgroups in a MOOC. The five subgroups identified in this study were: completing (6.6%); disengaging (4.8%); auditing (4.6%); sampling (21.1%); and enrolling (62.8%). Results indicated this was the optimal number of subgroups. Given the characteristics of the three at-risk subgroups (disengaging, sampling, and enrolling), tailored instructional strategies and interventions to improve behavioral engagement are discussed.

Patterns of fatigue and association with disease activity and clinical manifestations in systemic lupus erythematosus

Rheumatology ◽  
2020 ◽  
Author(s):  
Laurent Arnaud ◽  
Philippe Mertz ◽  
Zahir Amoura ◽  
Reinhard E Voll ◽  
Andreas Schwarting ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Latent Class Analysis ◽  
Disease Activity ◽  
Lupus Erythematosus ◽  
Latent Class ◽  
Clinical Manifestations ◽  
Optimal Number ◽  
Class Analysis ◽  
Systemic Lupus ◽  
Low Disease Activity

Abstract Objective The prevalence of fatigue is high in patients with systemic lupus erythematosus (SLE). In this study, we used latent class analysis to reveal patterns of fatigue, anxiety, depression and organ involvement in a large international cohort of SLE patients. Methods We used the Lupus BioBank of the upper Rhein to analyse patterns of fatigue using latent class analysis (LCA). After determining the optimal number of latent classes, patients were assigned according to model generated probabilities, and characteristics of classes were compared. Results A total of 502 patients were included. Significant fatigue, anxiety and depression were reported by 341 (67.9%), 159 (31.7%) and 52 (10.4%) patients, respectively. LCA revealed a first cluster (67.5% of patients) with low disease activity [median (25th–75th percentile interquartile range) Safety of Estrogens in Lupus Erythematosus National Assessment (SELENA)-SLEDAI: 2 (0–4)], significant fatigue (55.5%, P &lt; 0.0001), low anxiety (11.8%, P &lt; 0.0001) and depression (0.9%, P &lt; 0.0001). Cluster 2 (25.3%) also comprised patients with low disease activity [SELENA-SLEDAI: 2 (0–6)], but those patients had a very high prevalence of fatigue (100%, P &lt; 0.0001), anxiety (89%, P &lt; 0.0001) and depression (38.6%, P &lt; 0.0001). Cluster 3 (7.2%) comprised patients with high disease activity [SELENA-SLEDAI: 12 (8–17), P &lt; 0.0001] and high fatigue (72.2%, P &lt; 0.0001) with low levels of anxiety (16.7%, P &lt; 0.0001) and no depression (0%, P &lt; 0.0001). Conclusion LCA revealed three patterns of fatigue with important practical implications. Based on these, it is crucial to distinguish patients with active disease (in whom remission will be achieved) from those with no or mild activity but high levels of fatigue, depression and anxiety, for whom psychological counselling should be prioritized.

Abstract 15919: Latent Trajectory Patterns of Left Ventricle Ejection Fraction Among Heart Failure Patients With Chronic Inflammatory Disease: Longitudinal Analysis of Electronic Health Records

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Adovich S Rivera ◽  
Arjun Sinha ◽  
Anna Pawlowski ◽  
Donald M Lloyd-jones ◽  
Matthew J Feinstein
Keyword(s):  
Heart Failure ◽  
Ejection Fraction ◽  
Lupus Erythematosus ◽  
Latent Class ◽  
Medical Center ◽  
Academic Medical Center ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Multinomial Regression ◽  
Ventricular Ejection Fraction

Background: Immune regulation and inflammation play a role in the pathogenesis and progression of acute and chronic heart failure (HF). While overt inflammatory cardiomyopathy is a well-described clinical entity marked by acute cardiac dysfunction and relatively high rates of recovery, trajectories in cardiac function among people with chronically heightened systemic inflammation are less clear. We hypothesized that there are differences in trajectories of left ventricular ejection fraction among HF patients with different chronic inflammatory diseases (CIDs): human immunodeficiency virus (HIV), systemic lupus erythematosus (SLE), systemic sclerosis (SSc), rheumatoid arthritis (RA), inflammatory bowel disease (IBD), or psoriasis. Methods: We analyzed serial echocardiographic data from people with CIDs and HF who had at least three echocardiograms (n=974) at a large academic medical center. We identified latent trajectories patterns of LVEF using latent class trajectory models, then described clinical differences across the different trajectories. We then used multinomial regression to test if CID type and other baseline variables were associated with different trajectories. Results: We observed three major LVEF trajectories which paralleled known HF subtypes: preserved/intermediate EF (HFp/iEF, 687, 70.5%), reduced EF (HFrEF, 255, 26.2%), and recovered EF (HFrecEF, 32, 3.3%). These trajectories corresponded closely to accepted clinical definitions. For example, 30/32 (94%) patients in the HFrecEF trajectory had LVEF <40% at baseline that increased ≥15% on ≥1 follow-up. We observed significant differences in associations of CID type, age, sex, and diabetes with a specific LVEF trajectory (Figure) that remained even after regression. Conclusions: Among people with HF and CIDs, different trajectories of LVEF are associated with different CIDs and clinical characteristics. This may have implications for therapy and prognosis of HF in CIDs.

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