scholarly journals Prognostic Value of Multiple Circulating Biomarkers for 2-Year Death in Acute Heart Failure With Preserved Ejection Fraction

2021 ◽  
Vol 8 ◽  
Author(s):  
Yan Gao ◽  
Xueke Bai ◽  
Jiapeng Lu ◽  
Lihua Zhang ◽  
Xiaofang Yan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Ejection Fraction ◽  
Risk Stratification ◽  
Supervised Machine Learning ◽  
Machine Learning Method ◽  
Learning Method ◽  
Preserved Ejection Fraction ◽  
Circulating Biomarkers ◽  
Svm Model

Background: Heart failure with preserved ejection fraction (HFpEF) is increasingly recognized as a major global public health burden and lacks effective risk stratification. We aimed to assess a multi-biomarker model in improving risk prediction in HFpEF.Methods: We analyzed 18 biomarkers from the main pathophysiological domains of HF in 380 patients hospitalized for HFpEF from a prospective cohort. The association between these biomarkers and 2-year risk of all-cause death was assessed by Cox proportional hazards model. Support vector machine (SVM), a supervised machine learning method, was used to develop a prediction model of 2-year all-cause and cardiovascular death using a combination of 18 biomarkers and clinical indicators. The improvement of this model was evaluated by c-statistics, net reclassification improvement (NRI), and integrated discrimination improvement (IDI).Results: The median age of patients was 71-years, and 50.5% were female. Multiple biomarkers independently predicted the 2-year risk of death in Cox regression model, including N-terminal pro B-type brain-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T (hs-TnT), growth differentiation factor-15 (GDF-15), tumor necrosis factor-α (TNFα), endoglin, and 3 biomarkers of extracellular matrix turnover [tissue inhibitor of metalloproteinases (TIMP)-1, matrix metalloproteinase (MMP)-2, and MMP-9) (FDR < 0.05). The SVM model effectively predicted the 2-year risk of all-cause death in patients with acute HFpEF in training set (AUC 0.834, 95% CI: 0.771–0.895) and validation set (AUC 0.798, 95% CI: 0.719–0.877). The NRI and IDI indicated that the SVM model significantly improved patient classification compared to the reference model in both sets (p < 0.05).Conclusions: Multiple circulating biomarkers coupled with an appropriate machine-learning method could effectively predict the risk of long-term mortality in patients with acute HFpEF. It is a promising strategy for improving risk stratification in HFpEF.

Galectin-3 in Heart Failure – Linking Fibrosis, Remodelling and Progression

2010 ◽  
Vol 6 (2) ◽  
pp. 33 ◽  
Author(s):  
Christopher R deFilippi ◽  
G Michael Felker ◽  
◽  
Keyword(s):  
Heart Failure ◽  
Ejection Fraction ◽  
Risk Stratification ◽  
Cardiac Fibrosis ◽  
The Elderly ◽  
Preserved Ejection Fraction ◽  
Heart Failure Patients ◽  
Large Populations ◽  
Galectin 3

For many with heart failure, including the elderly and those with a preserved ejection fraction, both risk stratification and treatment are challenging. For these large populations and others there is increasing recognition of the role of cardiac fibrosis in the pathophysiology of heart failure. Galectin-3 is a novel biomarker of fibrosis and cardiac remodelling that represents an intriguing link between inflammation and fibrosis. In this article we review the biology of galectin-3, recent clinical research and its application in the management of heart failure patients.

Phenomapping Heart Failure with Preserved Ejection Fraction Using Machine Learning Cluster Analysis

2021 ◽  
Vol 17 (3) ◽  
pp. 499-518
Author(s):  
Elena Galli ◽  
Corentin Bourg ◽  
Wojciech Kosmala ◽  
Emmanuel Oger ◽  
Erwan Donal
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Cluster Analysis ◽  
Ejection Fraction ◽  
Preserved Ejection Fraction

Circulating Biomarkers in Patients with Heart Failure and Preserved Ejection Fraction

2013 ◽  
Vol 10 (4) ◽  
pp. 350-358 ◽  
Author(s):  
Eileen O’Meara ◽  
Simon de Denus ◽  
Jean-Lucien Rouleau ◽  
Akshay Desai
Keyword(s):  
Heart Failure ◽  
Ejection Fraction ◽  
Preserved Ejection Fraction ◽  
Circulating Biomarkers ◽  
Patients With Heart Failure

scholarly journals Machine Learning Prediction of Mortality and Hospitalization in Heart Failure With Preserved Ejection Fraction

2020 ◽  
Vol 8 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Suveen Angraal ◽  
Bobak J. Mortazavi ◽  
Aakriti Gupta ◽  
Rohan Khera ◽  
Tariq Ahmad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Ejection Fraction ◽  
Preserved Ejection Fraction ◽  
Prediction Of Mortality

3057Relation of atrial remodeling to circulating biomarkers of myocardial fibrosis and apoptotic microparticles in patients with atrial fibrillation and heart failure with preserved ejection fraction

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
M S Dzeshka ◽  
E Shantsila ◽  
V A Snezhitskiy ◽  
G Y H Lip
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Ejection Fraction ◽  
Matrix Metalloproteinase ◽  
Myocardial Fibrosis ◽  
Univariate Analysis ◽  
Volume Index ◽  
Atrial Remodeling ◽  
Preserved Ejection Fraction ◽  
Circulating Biomarkers

Abstract Introduction Left atrial (LA) remodeling is a mainstay for atrial fibrillation (AF) occurrence. AF further promotes structural changes in LA, as fibrosis and stretching, followed by AF progression to its permanent form. Many profibrotic pathways have been studied, and circulating microparticles (MPs) may have a role. MPs are extracellular submicron anucleoid phospholipid vesicles released from different cells. Annexin V-binding (AnV+) MPs were suggested as a marker of apoptosis. Purpose To evaluate association of circulating biomarkers of myocardial fibrosis and MPs subsets with LA remodeling in patients with AF and heart failure with preserved ejection fraction. Methods We studied 274 patients (median age 62 years, 37% females). Paroxysmal AF was diagnosed in 150 patients (55%) and non-paroxysmal AF (persistent or permanent) in 124 (45%). Median CHA2DS2-VASc score was 3 in males and 4 in females. Patients with valvular AF, recent (<6 months) thromboembolic or hemorrhagic event, advanced chronic kidney or hepatic dysfunction, malignancy or active inflammatory disorders were excluded. Transthoracic echocardiography was performed. LA maximum volume index (LAVi) was measured as an index of LA structural remodeling in AF. Average values from ten consecutive cardiac cycles were calculated. Blood levels of galectin 3, interleukin-1 receptor-like 1 (ST2), transforming growth factor beta 1 (TGF-β1), procollagen type III aminoterminal propeptide (PIIINP), matrix metalloproteinase 9 (MMP-9), tissue inhibitor of matrix metalloproteinase 1 (TIMP-1), angiotensin II and aldosterone were assayed as surrogate biomarkers of myocardial fibrosis with ELISA. Using microflow cytometry (Figure), numbers of platelet-derived (CD42b+), monocyte-derived (CD14+), endothelial (CD144+), and apoptotic MPs (AnV+) were quantified in plasma samples. Linear regression was used to reveal parameters associated with LAVi. Raw data were normalized with Box-Cox transformation. Results Median LAVi in studied patients was 48 (39–59) ml/m2 and increased from patients with paroxysmal AF (42 [35–51] ml/m2) to persistent AF (53 [43–62] ml/m2) and permanent AF (57 [46–69] ml/m2), p<0.001. On univariate analysis male gender (β=0.11, p=0.04); history of hypertension (β=0.18, p=0.03); AF type, i.e. progression from paroxysmal to permanent (β=0.38, p<0.001); AnV+ MPs (β=0.19, p=0.005); ST2 (β=0.15, p=0.02); and early mitral inflow velocity (E)/early mitral annular diastolic velocity (E/E') averaged for LV septal and lateral basal regions (β=0.18, p=0.005) were associated with LAVi. Using stepwise multivariate regression AnV+ MPs (β=0.14, p=0.03); AF type (β=0.35, p<0.001); and E/E' ratio (β=0.11, p=0.04) remained significant predictors of LAVi (adjusted for age and gender). Apoptotic MPs detection with microFCM Conclusion Level of circulating apoptotic MPs is associated with LAVi in AF patients with HFpEF, and may be involved in remodeling process or could represent surrogate markers of myocardial damage in AF. Acknowledgement/Funding ESC Research Grant, EHRA Academic Research Fellowship Programme

scholarly journals PSO-Based Support Vector Machine with Cuckoo Search Technique for Clinical Disease Diagnoses

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaoyong Liu ◽  
Hui Fu
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Cuckoo Search ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Svm Model ◽  
Two Stages ◽  
Best Parameters

Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM), particle swarm optimization (PSO), and cuckoo search (CS). The new method consists of two stages: firstly, a CS based approach for parameter optimization of SVM is developed to find the better initial parameters of kernel function, and then PSO is applied to continue SVM training and find the best parameters of SVM. Experimental results indicate that the proposed CS-PSO-SVM model achieves better classification accuracy and F-measure than PSO-SVM and GA-SVM. Therefore, we can conclude that our proposed method is very efficient compared to the previously reported algorithms.

scholarly journals MMMELD-XI Score Is Associated With Short-Term Adverse Events in Patients With Heart Failure With Preserved Ejection Fraction

2021 ◽  
Vol 8 ◽  
Author(s):  
Sunying Wang ◽  
Yuwei Wang ◽  
Manqing Luo ◽  
Kaiyang Lin ◽  
Xiaoxu Xie ◽  
...  
Keyword(s):  
Heart Failure ◽  
Adverse Events ◽  
Ejection Fraction ◽  
Risk Stratification ◽  
Cardiovascular Mortality ◽  
Nyha Class ◽  
Preserved Ejection Fraction ◽  
Short Term ◽  
All Cause Mortality ◽  
Discriminatory Capacity

Aim: Accumulating evidence suggests that MELD-XI score holds the ability to predict the prognosis of congestive heart failure. However, most of the evidence is based on the end-stage heart failure population; thus, we aim to explore the association between the MELD-XI score and the prognosis in heart failure with preserved ejection fraction (HFpEF).Methods: A total of 30,096 patients hospitalized for HFpEF in Fujian Provincial Hospital between January 1, 2014 and July 17, 2020 with available measures of creatinine and liver function were enrolled. The primary endpoint was 60-day in-hospital all-cause mortality. Secondary endpoints were 60-day in-hospital cardiovascular mortality and 30-day rehospitalization for heart failure.Results: A total of 222 patients died within 60 days after admission, among which 75 deaths were considered cardiogenic. And 73 patients were readmitted for heart failure within 30 days after discharge. Generally, patients with an elevated MELD-XI score tended to have more comorbidities, higher NYHA class, and higher inflammatory biomarkers levels. Meanwhile, the MELD-XI score was positively correlated with NT-pro BNP, left atrial diameter, E/e' and negatively correlated with LVEF. After adjusting for conventional risk factors, the MELD-XI score was independently associated with 60-day in-hospital all-cause mortality [hazard ratio(HR) = 1.052, 95% confidential interval (CI) 1.022–1.083, P = 0.001], 60-day in-hospital cardiovascular mortality (HR = 1.064, 95% CI 1.013–1.118, P = 0.014), and 30-day readmission for heart failure (HR = 1.061, 95% CI 1.015–1.108, P = 0.009). Furthermore, the MELD-XI score added an incremental discriminatory capacity to risk stratification models developed based on this cohort.Conclusion: The MELD-XI score was associated with short-term adverse events and provided additional discriminatory capacity to risk stratification models in patients hospitalized for HFpEF.

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