Advances in Machine Learning Approaches to Heart Failure with Preserved Ejection Fraction.
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ABSTRACT:
SUBMITTER: Ahmad FS
PROVIDER: S-EPMC8983114 | biostudies-literature | 2022 Apr
REPOSITORIES: biostudies-literature
Publications
Advances in Machine Learning Approaches to Heart Failure with Preserved Ejection Fraction.
Ahmad Faraz S FS Luo Yuan Y Wehbe Ramsey M RM Thomas James D JD Shah Sanjiv J SJ
Heart failure clinics 20220304 2
Heart failure with preserved ejection fraction (HFpEF) represents a prototypical cardiovascular condition in which machine learning may improve targeted therapies and mechanistic understanding of pathogenesis. Machine learning, which involves algorithms that learn from data, has the potential to guide precision medicine approaches for complex clinical syndromes such as HFpEF. It is therefore important to understand the potential utility and common pitfalls of machine learning so that it can be a ...[more]
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Project description:AimsHeart failure (HF) with preserved ejection fraction (HFpEF) is a complex syndrome with a poor prognosis. Phenotyping is required to identify subtype-dependent treatment strategies. Phenotypes of Japanese HFpEF patients are not fully elucidated, whose obesity is much less than Western patients. This study aimed to reveal model-based phenomapping using unsupervised machine learning (ML) for HFpEF in Japanese patients.Methods and resultsWe studied 365 patients with HFpEF (left ventricular ejection fraction >50%) as a derivation cohort from the Nara Registry and Analyses for Heart Failure (NARA-HF), which registered patients with hospitalization by acute decompensated HF. We used unsupervised ML with a variational Bayesian-Gaussian mixture model (VBGMM) with common clinical variables. We also performed hierarchical clustering on the derivation cohort. We adopted 230 patients in the Japanese Heart Failure Syndrome with Preserved Ejection Fraction Registry as the validation cohort for VBGMM. The primary endpoint was defined as all-cause death and HF readmission within 5 years. Supervised ML was performed on the composite cohort of derivation and validation. The optimal number of clusters was three because of the probable distribution of VBGMM and the minimum Bayesian information criterion, and we stratified HFpEF into three phenogroups. Phenogroup 1 (n = 125) was older (mean age 78.9 ± 9.1 years) and predominantly male (57.6%), with the worst kidney function (mean estimated glomerular filtration rate 28.5 ± 9.7 mL/min/1.73 m2 ) and a high incidence of atherosclerotic factor. Phenogroup 2 (n = 200) had older individuals (mean age 78.8 ± 9.7 years), the lowest body mass index (BMI; 22.78 ± 3.94), and the highest incidence of women (57.5%) and atrial fibrillation (56.5%). Phenogroup 3 (n = 40) was the youngest (mean age 63.5 ± 11.2) and predominantly male (63.5 ± 11.2), with the highest BMI (27.46 ± 5.85) and a high incidence of left ventricular hypertrophy. We characterized these three phenogroups as atherosclerosis and chronic kidney disease, atrial fibrillation, and younger and left ventricular hypertrophy groups, respectively. At the primary endpoint, Phenogroup 1 demonstrated the worst prognosis (Phenogroups 1-3: 72.0% vs. 58.5% vs. 45%, P = 0.0036). We also successfully classified a derivation cohort into three similar phenogroups using VBGMM. Hierarchical and supervised clustering successfully showed the reproducibility of the three phenogroups.ConclusionsML could successfully stratify Japanese HFpEF patients into three phenogroups (atherosclerosis and chronic kidney disease, atrial fibrillation, and younger and left ventricular hypertrophy groups).
| S-EPMC10192264 | biostudies-literature
Project description:BackgroundThe diagnosis of heart failure with preserved ejection fraction (HFpEF) in the clinical setting remains challenging, especially in patients with obesity.ObjectivesThis study aimed to identify novel predictors of HFpEF well suited for patients with obesity.MethodsWe performed a retrospective analysis of a well-characterized cohort of patients with obesity with HFpEF (n = 404; mean body mass index [BMI] 36.6 kg/m2) and controls (n = 67). We used the machine learning algorithm Gradient Boosting Machine to analyze the association of various parameters with the diagnosis of HFpEF and subsequently created a multivariate logistic model for the diagnosis.ResultsGradient Boosting Machine identified BMI, estimated glomerular filtration rate, left ventricular mass index, and left atrial to left ventricular volume ratio as the strongest predictors of HFpEF. These variables were used to build a model that identified HFpEF with a sensitivity of 0.83, a specificity of 0.82, and an area under the curve (AUC) of 0.88. Internal validation of the model with optimism-adjusted AUC showed an AUC of 0.87. Within the studied cohort, the novel score outperformed the H2FPEF score (AUC: 0.88 vs 0.74; P < 0.001).ConclusionsIn a HFpEF cohort with obesity, BMI, estimated glomerular filtration rate, left ventricular mass index, and left atrial to left ventricular volume ratio most correlated with the identification of HFpEF, and a score based on these variables (HFpEF-JH score) outperformed the currently used H2PEF score. Further validation of this novel score is warranted, as it may facilitate improved diagnostic accuracy of HFpEF, particularly in patients with obesity.
| S-EPMC11312345 | biostudies-literature
Project description:Heart failure with preserved ejection fraction (HFpEF) represents a heterogeneous collection of conditions that are unified by the presence of a left ventricular ejection fraction ≥50%, evidence of impaired diastolic function and elevated natriuretic peptide levels, all within the context of typical heart failure signs and symptoms. However, while HFpEF is steadily becoming the predominant form of heart failure, disease-modifying treatment options for this population remain sparse. This review provides an overview of the diagnosis, management and prevention of HFpEF for general physicians.
| S-EPMC6334030 | biostudies-literature
Project description:Purpose of reviewWith the failure of multiple trials to identify a successful therapy for heart failure with preserved ejection fraction (HFpEF), attention has shifted to defining specific phenotypes within the HFpEF spectrum in an effort to develop a targeted approach to treatment. Here we summarize the most recent studies investigating the pathophysiology and clinical features of HFpEF, and discuss recent clinical trials in the context of developing treatments that look toward the underlying cause of this disorder.Recent findingsAdvances in basic science and clinical research have further characterized HFpEF, identifying multiple pathophysiological mechanisms that ultimately lead to exercise intolerance and volume overload. The success of small studies focused on specific subsets of the HFpEF population has promoted the concept that there may not be one treatment strategy that can universally be applied to HFpEF.SummaryHFpEF is associated with significant morbidity and mortality and accounts for approximately half of patients with chronic heart failure. HFpEF is a complex disease, encompassing a diverse cohort of patients and marked by the presence of multiple etiological mechanisms. The failure to develop successful therapies for the management of HFpEF may be because of inadequate standardization of the HFpEF diagnosis, overly broad inclusion criteria and inadequate differentiation of disease subtypes. Given the heterogeneity among patients with HFpEF, much of the current research is focused on understanding of pathophysiology and identifying disease phenotypes that may respond to a targeted treatment approach. Several newer approaches, including neprilysin inhibition and device therapy, offer promise for a new era of HFpEF treatment.
| S-EPMC5391977 | biostudies-literature
Project description:AimsCertain critical risk factors of heart failure with preserved ejection fraction (HFpEF) patients were significantly different from those of heart failure with reduced ejection fraction (HFrEF) patients, resulting in the limitations of existing predictive models in real-world situations. This study aimed to develop a machine learning model for predicting 90 day readmission for HFpEF patients.Methods and resultsData were extracted from electronic health records from 1 August 2020 to 1 August 2021 and follow-up records of patients with HFpEF within 3 months after discharge. Feature extraction was performed by univariate analysis combined with the least absolute shrinkage and selection operator (LASSO) algorithms. Machine learning models like eXtreme Gradient Boosting (XGBoost), random forest, neural network and logistic regression were adopted to construct models. The discrimination and calibration of each model were compared, and the Shapley Additive exPlanations (SHAP) method was used to explore the interpretability of the model. The cohort included 746 patients, of whom 103 (13.8%) were readmitted within 90 days. XGBoost owned the best performance [area under the curve (AUC) = 0.896, precision-recall area under the curve (PR-AUC) = 0.868, sensitivity = 0.817, specificity = 0.837, balanced accuracy = 0.827]. The Kolmogorov-Smirnov (KS) statistic was 0.694 at 0.468 in the XGBoost model. SHAP identified the top 12 risk features, including activities of daily living (ADL), left atrial dimension (LAD), left ventricular end-diastolic diameter (LVDD), shortness, nitrates, length of stay, nutritional risk, fall risk, accompanied by other symptoms, educational level, anticoagulants and edema.ConclusionsOur model could help medical agencies achieve the early identification of 90 day readmission risk in HFpEF patients and reveal risk factors that provide valuable insights for treatments.
| S-EPMC11631356 | biostudies-literature
Project description:AimsThe lack of effective therapies for patients with heart failure with preserved ejection fraction (HFpEF) is often ascribed to the heterogeneity of patients with HFpEF. We aimed to identify distinct pathophysiologic clusters of HFpEF based on circulating biomarkers.Methods and resultsWe performed an unsupervised cluster analysis using 363 biomarkers from 429 patients with HFpEF. Relative differences in expression profiles of the biomarkers between clusters were assessed and used for pathway over-representation analyses. We identified four distinct patient subgroups based on their biomarker profiles: cluster 1 with the highest prevalence of diabetes mellitus and renal disease; cluster 2 with oldest age and frequent age-related comorbidities; cluster 3 with youngest age, largest body size, least symptoms and lowest N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels; and cluster 4 with highest prevalence of ischaemic aetiology, smoking and chronic lung disease, most symptoms, as well as highest NT-proBNP and troponin levels. Over a median follow-up of 21 months, the occurrence of death or heart failure hospitalization was highest in clusters 1 and 4 (62.1% and 62.8%, respectively) and lowest in cluster 3 (25.6%). Pathway over-representation analyses revealed that the biomarker profile of patients in cluster 1 was associated with activation of inflammatory pathways while the biomarker profile of patients in cluster 4 was specifically associated with pathways implicated in cell proliferation regulation and cell survival.ConclusionUnsupervised cluster analysis based on biomarker profiles identified mutually exclusive subgroups of patients with HFpEF with distinct biomarker profiles, clinical characteristics and outcomes, suggesting different underlying pathophysiological pathways.
| S-EPMC8360080 | biostudies-literature
Project description:Heart failure with preserved ejection fraction (HFpEF) is increasing in prevalence as the general population ages. Poorly managed heart failure symptoms of decompensated HFpEF is one of the most common reasons for prolonged hospital admission. The high rate of morbidity and mortality associated with HFpEF is compounded by a poor understanding of the underpinning pathophysiology. Randomized controlled trials have so far been unable to identify an evidence base for reducing morbidity and mortality in patients with HFpEF, although there is some evidence to support quality of life (QOL) improvement. In this review, we described the recent advances on the pathophysiological understanding of HFpEF, the current and emerging treatment strategies, and what this may mean for individual patients. Potential treatments for HFpEF were divided into their relative management strategies and the current evidence assessed for effect on HFpEF mortality, hospital admission frequency, and QOL improvement. Overall, the understanding of HFpEF pathophysiology is improving and has been made a priority in identifying potential therapeutic targets. There is growing evidence that patients with ejection fractions (EF) of less than 60% may obtain a mortality benefit from ACE-inhibitors, angiotensin-neprilysin inhibitors, Angiotensin Receptor Blockers, and Mineralocorticoid Receptor Antagonists. However, this covers only a small proportion of the HFpEF spectrum. Therefore, currently there are no universal treatment strategies recommended for HFpEF, and management should focus on an individualised approach and this should take into account the comorbidities of each patient.
| S-EPMC7186731 | biostudies-literature
Project description:In contrast to the wealth of proven therapies for heart failure with reduced ejection fraction (HFrEF), therapeutic efforts in the past have failed to improve outcomes in heart failure with preserved ejection fraction (HFpEF). Moreover, to this day, diagnosis of HFpEF remains controversial. However, there is growing appreciation that HFpEF represents a heterogeneous syndrome with various phenotypes and comorbidities which are hardly to differentiate solely by LVEF and might benefit from individually tailored approaches. These hypotheses are supported by the recently presented PARAGON-HF trial. Although treatment with LCZ696 did not result in a significantly lower rate of total hospitalizations for heart failure and death from cardiovascular causes among HFpEF patients, subanalyses suggest beneficial effects in female patients and those with an LVEF between 45 and 57%. In the future, prospective randomized trials should focus on dedicated, well-defined subgroups based on various information such as clinical characteristics, biomarker levels, and imaging modalities. These could clarify the role of LCZ696 in selected individuals. Furthermore, sodium-glucose cotransporter-2 inhibitors have just proven efficient in HFrEF patients and are currently also studied in large prospective clinical trials enrolling HFpEF patients. In addition, several novel disease-modifying drugs that pursue different strategies such as targeting cardiac inflammation and fibrosis have delivered preliminary optimistic results and are subject of further research. Moreover, innovative device therapies may enhance management of HFpEF, but need prospective adequately powered clinical trials to confirm safety and efficacy regarding clinical outcomes. This review highlights the past, present, and future therapeutic approaches in HFpEF.
| S-EPMC7449942 | biostudies-literature
Project description:The aim of this study was to determine whether left atrial ejection fraction (LAEF) quantified with cardiovascular magnetic resonance (CMR) was different between heart failure with preserved ejection fraction (HFpEF) and controls, and its relation to prognosis. As part of our single-centre, prospective, observational study, 188 subjects (HFpEF n = 140, controls n = 48) underwent phenotyping with contrast-enhanced CMR, transthoracic echocardiography, blood sampling and six-minute walk testing. LAEF was calculated using the biplane method. Atrial fibrillation (AF) was present in 43 (31%) of HFpEF subjects. Overall, LAEF (%) was lower in HFpEF patients inclusive of AF (32 ± 16) or those in sinus rhythm alone (41 ± 12) compared to controls (51 ± 11), p < 0.0001. LAEF correlated inversely with maximal and minimal left atrial volumes indexed (r = - 0.602, r = - 0.762), and plasma N-terminal pro-atrial natriuretic peptide (r = - 0.367); p < 0.0001. During median follow-up (1429 days), there were 67 composite events of all-cause death or hospitalization for heart failure (22 deaths, 45 HF hospitalizations) in HFpEF. Lower LAEF (below median) was associated with an increased risk of composite endpoints (Log-Rank: all p = 0.028; sinus p = 0.036). In multivariable Cox regression analysis, LAEF (adjusted hazard ratio [HR] 0.767, 95% confidence interval [CI] 0.591-0.996; p = 0.047) and indexed extracellular volume (HR 1.422, CI 1.015-1.992; p = 0.041) were the only parameters that remained significant when added to a base prognostic model comprising age, prior HF hospitalization, diastolic blood pressure, lung disease, NYHA, six-minute-walk-test-distance, haemoglobin, creatinine and B-type natriuretic peptide. CMR-derived LAEF is lower in HFpEF compared to healthy controls and is a strong prognostic biomarker.
| S-EPMC6942575 | biostudies-literature
Project description: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.
| S-EPMC8019773 | biostudies-literature