scholarly journals Machine learning based on biomarker profiles identifies distinct subgroups of heart failure with preserved ejection fraction

2021 ◽  
Author(s):  
Rebecca J. Woolley ◽  
Daan Ceelen ◽  
Wouter Ouwerkerk ◽  
Jasper Tromp ◽  
Sylwia M. Figarska ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Ejection Fraction ◽  
Preserved Ejection Fraction

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

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

scholarly journals Identification of novel pheno-groups in heart failure with preserved ejection fraction using machine learning

Heart ◽  
2020 ◽  
Vol 106 (5) ◽  
pp. 342-349 ◽  
Author(s):  
Åsa K Hedman ◽  
Camilla Hage ◽  
Anil Sharma ◽  
Mary Julia Brosnan ◽  
Leonard Buckbinder ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Ejection Fraction ◽  
Stable Condition ◽  
Chronic Obstructive ◽  
Preserved Ejection Fraction ◽  
Obstructive Pulmonary Disease ◽  
Model Based Clustering ◽  
Applied Model ◽  
End Point

ObjectiveHeart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome. We aimed to derive HFpEF phenotype-based groups ('phenogroups') based on clinical and echocardiogram data using machine learning, and to compare clinical characteristics, proteomics and outcomes across the phenogroups.MethodsWe applied model-based clustering to 32 echocardiogram and 11 clinical and laboratory variables collected in stable condition from 320 HFpEF outpatients in the Karolinska-Rennes cohort study (56% female, median 78 years (IQR: 71–83)). Baseline proteomics and the composite end point of all-cause mortality or heart failure (HF) hospitalisation were used in secondary analyses.ResultsWe identified six phenogroups, for which significant differences in the prevalence of concomitant atrial fibrillation (AF), anaemia and kidney disease were observed (p<0.05). Fifteen out of 86 plasma proteins differed between phenogroups (false discovery rate, FDR<0.05), including biomarkers of HF, AF and kidney function. The composite end point was significantly different between phenogroups (log-rank p<0.001), at short-term (100 days), mid-term (18 months) and longer-term follow-up (1000 days). Phenogroup 2 was older, with poorer diastolic and right ventricular function and higher burden of risk factors as AF (85%), hypertension (83%) and chronic obstructive pulmonary disease (30%). In this group a third experienced the primary outcome to 100 days, and two-thirds to 18 months (HR (95% CI) versus phenogroups 1, 3, 4, 5, 6: 1.5 (0.8–2.9); 5.7 (2.6–12.8); 2.9 (1.5–5.6); 2.7 (1.6–4.6); 2.1 (1.2–3.9)).ConclusionsUsing machine learning we identified distinct HFpEF phenogroups with differential characteristics and outcomes, as well as differential levels of inflammatory and cardiovascular proteins.

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 &lt; 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 &lt; 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.

scholarly journals Risk Prediction in Patients With Heart Failure With Preserved Ejection Fraction Using Gene Expression Data and Machine Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Liye Zhou ◽  
Zhifei Guo ◽  
Bijue Wang ◽  
Yongqing Wu ◽  
Zhi Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Heart Failure ◽  
High Risk ◽  
Ejection Fraction ◽  
Gene Expression Data ◽  
Risk Groups ◽  
Support Vector ◽  
Expression Data ◽  
Preserved Ejection Fraction

Heart failure with preserved ejection fraction (HFpEF) has become a major health issue because of its high mortality, high heterogeneity, and poor prognosis. Using genomic data to classify patients into different risk groups is a promising method to facilitate the identification of high-risk groups for further precision treatment. Here, we applied six machine learning models, namely kernel partial least squares with the genetic algorithm (GA-KPLS), the least absolute shrinkage and selection operator (LASSO), random forest, ridge regression, support vector machine, and the conventional logistic regression model, to predict HFpEF risk and to identify subgroups at high risk of death based on gene expression data. The model performance was evaluated using various criteria. Our analysis was focused on 149 HFpEF patients from the Framingham Heart Study cohort who were classified into good-outcome and poor-outcome groups based on their 3-year survival outcome. The results showed that the GA-KPLS model exhibited the best performance in predicting patient risk. We further identified 116 differentially expressed genes (DEGs) between the two groups, thus providing novel therapeutic targets for HFpEF. Additionally, the DEGs were enriched in Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways related to HFpEF. The GA-KPLS-based HFpEF model is a powerful method for risk stratification of 3-year mortality in HFpEF patients.

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