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.

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

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

Competence Differences in Literacy, Numeracy, and Problem Solving According to Sex

2019 ◽  
Vol 69 (2) ◽  
pp. 101-119 ◽  
Author(s):  
Seher Yalcin
Keyword(s):  
Problem Solving ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Latent Classes ◽  
Class Analysis ◽  
Country Level ◽  
Successful Group ◽  
Study Population ◽  
Multilevel Latent Class Analysis ◽  
International Assessment

This study aimed to determine individual- and country-level latent classes in literacy, numeracy and problem-solving competencies of individuals participating in the Programme for the International Assessment of Adult Competencies 2015. Specifically, it sought to distinguish these classes in relation to individuals’ sex and to identify the state of prediction of the determined latent classes by each person’s level of education. The study population consisted of 116,301 adults aged 16 to 65 years in 20 countries. Multilevel latent class analysis was conducted to consider the nested data structure and determine the number of latent classes. According to the results of the multilevel latent class analysis, Turkey and Chile were in the low achievement group in all skills, while Japan was in the most successful group. Moreover, the results revealed that sex and education level had a considerable influence on certain competence levels.

scholarly journals PATIENT CHARACTERISTICS ASSOCIATED WITH VENTRICULAR ARRHYTHMIAS, HEART BLOCK, AND HEART FAILURE IN CARDIAC SARCOIDOSIS PATIENTS

CHEST Journal ◽  
2020 ◽  
Vol 158 (4) ◽  
pp. A131-A132
Author(s):  
Matthew Bocchese ◽  
David Rosenthal ◽  
Abdullah Haddad ◽  
Benjamin Rosenfeld ◽  
Crystal Chen ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Block ◽  
Ventricular Arrhythmias ◽  
Cardiac Sarcoidosis ◽  
Patient Characteristics

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.

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 Management of Cardiac Sarcoidosis in 2020

2020 ◽  
Vol 9 (4) ◽  
pp. 182-188
Author(s):  
Nisha Gilotra ◽  
David Okada ◽  
Apurva Sharma ◽  
Jonathan Chrispin
Keyword(s):  
Heart Failure ◽  
Heart Block ◽  
Ventricular Arrhythmias ◽  
Cardiac Involvement ◽  
Cardiac Sarcoidosis ◽  
Management Strategies ◽  
Clinical Manifestations ◽  
Future Directions ◽  
Inflammatory Phase

Sarcoidosis is an inflammatory granulomatous disease that can affect any organ. Up to one-quarter of patients with systemic sarcoidosis may have evidence of cardiac involvement. The clinical manifestations of cardiac sarcoidosis (CS) include heart block, atrial arrhythmias, ventricular arrhythmias and heart failure. The diagnosis of CS can be challenging given the patchy infiltration of the myocardium but, with the increased availability of advanced cardiac imaging, more cases of CS are being identified. Immunosuppression with corticosteroids remains the standard therapy for the acute inflammatory phase of CS, but there is an evolving role of steroid-sparing agents. In this article, the authors provide an update on the diagnosis of CS, including the role of imaging; review the clinical manifestations of CS, namely heart block, atrial and ventricular arrhythmias and heart failure; discuss updated management strategies, including immunosuppression, electrophysiological and heart failure therapies; and identify the current gaps in knowledge and future directions for cardiac sarcoidosis.

scholarly journals 3417 Impact of Protein-Energy Malnutrition on Outcomes of Heart Failure Hospitalizations

2019 ◽  
Vol 3 (s1) ◽  
pp. 123-123
Author(s):  
Adeyinka Charles Adejumo ◽  
Olumuyiwa Ogundipe
Keyword(s):  
Heart Failure ◽  
Clinical Outcomes ◽  
Protein Energy Malnutrition ◽  
Low Grade ◽  
Protein Energy ◽  
The Us ◽  
Study Population ◽  
Comorbidity Index ◽  
The Impact ◽  
Low Grade Inflammation

OBJECTIVES/SPECIFIC AIMS: Chronically elevated cytokines from un-abating low-grade inflammation in heart failure (HF) results in Protein-Energy Malnutrition (PEM). However, the impact of PEM on clinical outcomes of admissions for HF exacerbations has not been evaluated in a national data. METHODS/STUDY POPULATION: From the 2012-2014 Nationwide Inpatient Sample (NIS) patient’s discharge records for primary HF admissions, we identified patients with concomitant PEM, and their demographic and comorbid factors. We propensity-matched PEM cohorts (32,771) to no-PEM controls (1:1) using a greedy algorithm-based methodology and estimated the effect of different clinical outcomes (SAS 9.4). RESULTS/ANTICIPATED RESULTS: There were 32,771 (~163,885) cases of PEM among the 541,679 (~2,708,395) primary admissions for HF between 2012 and 2014 in the US. PEM cases were older (PEM:76 vs. no-PEM:72 years), Whites (70.75% vs. 67.30%), and had higher comorbid burden, with Deyo-comorbidity index >3 (31.61% vs. 26.30%). However, PEM cases had lower rates of obesity, hyperlipidemia and diabetes. After propensity-matching, PEM was associated with higher mortality (AOR:2.48[2.31-2.66]), cardiogenic shock (3.11[2.79-3.46]), cardiac arrest (2.30[1.96-2.70]), acute kidney failure (1.49[1.44-1.54]), acute respiratory failure (1.57[1.51-1.64]), mechanical ventilation (2.72[2.50-2.97]). PEM also resulted in higher non-routine discharges (2.24[2.17-2.31]), hospital cost ($80,534[78,496-82,625] vs. $43,226[42,376-44,093]) and longer duration of admission (8.61[8.49-8.74] vs. 5.28[5.23-5.34] days). DISCUSSION/SIGNIFICANCE OF IMPACT: In the US, PEM is a common comorbidity among hospitalized HF subjects, and results in devastating health outcomes. Early identification and prevention of PEM in heart failure subjects during clinic visits and prompt treatment of PEM both in the clinic and during hospitalization are essential to decrease the excess burden of PEM.

scholarly journals Differences in Clinical Outcomes of Adults Referred to a Homeless Transitional Care Program Based on Multimorbid Health Profiles: A Latent Class Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Colin M. Smith ◽  
Jacob Feigal ◽  
Richard Sloane ◽  
Donna J. Biederman
Keyword(s):  
Health Care ◽  
Substance Use ◽  
Latent Class Analysis ◽  
Clinical Outcomes ◽  
Substance Use Disorder ◽  
Latent Class ◽  
Transitional Care ◽  
Medical Illness ◽  
Class Analysis ◽  
High Comorbidity

Background: People experiencing homelessness face significant medical and psychiatric illness, yet few studies have characterized the effects of multimorbidity within this population. This study aimed to (a) delineate unique groups of individuals based on medical, psychiatric, and substance use disorder profiles, and (b) compare clinical outcomes across groups.Methods: We extracted administrative data from a health system electronic health record for adults referred to the Durham Homeless Care Transitions program from July 2016 to June 2020. We used latent class analysis to estimate classes in this cohort based on clinically important medical, psychiatric and substance use disorder diagnoses and compared health care utilization, overdose, and mortality at 12 months after referral.Results: We included 497 patients in the study and found 5 distinct groups: “low morbidity” (referent), “high comorbidity,” “high tri-morbidity,” “high alcohol use,” and “high medical illness.” All groups had greater number of admissions, longer mean duration of admissions, and more ED visits in the 12 months after referral compared to the “low morbidity” group. The “high medical illness” group had greater mortality 12 months after referral compared to the “low morbidity” group (OR, 2.53, 1.03–6.16; 95% CI, 1.03–6.16; p = 0.04). The “high comorbidity” group (OR, 5.23; 95% CI, 1.57–17.39; p < 0.007) and “high tri-morbidity” group (OR, 4.20; 95% CI, 1.26–14.01; p < 0.02) had greater 12-month drug overdose risk after referral compared to the referent group.Conclusions: These data suggest that distinct groups of people experiencing homelessness are affected differently by comorbidities, thus health care programs for this population should address their risk factors accordingly.

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