Risk Prediction in Patients With Heart Failure With Preserved Ejection Fraction Using Gene Expression Data and Machine Learning.
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ABSTRACT:
SUBMITTER: Zhou L
PROVIDER: S-EPMC8019773 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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: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 applied and interpreted appropriately. Although machine learning holds considerable promise for HFpEF, it is subject to several potential pitfalls, which are important factors to consider when interpreting machine learning studies.
| S-EPMC8983114 | 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: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:AimsThis study aims to develop the first race-specific and sex-specific risk prediction models for heart failure with preserved (HFpEF) and reduced ejection fraction (HFrEF).Methods and resultsWe created a cohort of 1.8 million individuals who had an outpatient clinic visit between 2002 and 2007 within the Veterans Affairs (VA) Healthcare System and obtained information on HFpEF, HFrEF, and several risk factors from electronic health records (EHR). Variables were selected for the risk prediction models in a 'derivation cohort' that consisted of individuals with baseline date in 2002, 2003, or 2004 using a forward stepwise selection based on a change in C-index threshold. Discrimination and calibration were assessed in the remaining participants (internal 'validation cohort'). A total of 66 831 individuals developed HFpEF, and 92 233 developed HFrEF (52 679 and 71 463 in the derivation cohort) over a median of 11.1 years of follow-up. The HFpEF risk prediction model included age, diabetes, BMI, COPD, previous MI, antihypertensive treatment, SBP, smoking status, atrial fibrillation, and estimated glomerular filtration rate (eGFR), while the HFrEF model additionally included previous CAD. For the HFpEF model, C-indices were 0.74 (SE = 0.002) for white men, 0.76 (0.005) for black men, 0.79 (0.015) for white women, and 0.77 (0.026) for black women, compared with 0.72 (0.002), 0.72 (0.004), 0.77 (0.017), and 0.75 (0.028), respectively, for the HFrEF model. These risk prediction models were generally well calibrated in each race-specific and sex-specific stratum of the validation cohort.ConclusionsOur race-specific and sex-specific risk prediction models, which used easily obtainable clinical variables, can be a useful tool to implement preventive strategies or subtype-specific prevention trials in the nine million users of the VA healthcare system and the general population after external validation.
| S-EPMC8712836 | biostudies-literature
Project description:BackgroundLifetime risk of heart failure has been estimated to range from 20% to 46% in diverse sex and race groups. However, lifetime risk estimates for the 2 HF phenotypes, HF with preserved ejection fraction (HFpEF) and HF with reduced ejection fraction (HFrEF), are not known.MethodsParticipant-level data from 2 large prospective cohort studies, the CHS (Cardiovascular Health Study) and MESA (Multiethnic Study of Atherosclerosis), were pooled, excluding individuals with prevalent HF at baseline. Remaining lifetime risk estimates for HFpEF (EF ≥45%) and HFrEF (EF <45%) were determined at different index ages with the use of a modified Kaplan-Meier method with mortality and the other HF subtype as competing risks.ResultsWe included 12 417 participants >45 years of age (22.2% blacks, 44.8% men) who were followed up for median duration of 11.6 years with 2178 overall incident HF events with 561 HFrEF events and 726 HFpEF events. At the index age of 45 years, the lifetime risk for any HF through 90 years of age was higher in men than women (27.4% versus 23.8%). Among HF subtypes, the lifetime risk for HFrEF was higher in men than women (10.6% versus 5.8%). In contrast, the lifetime risk for HFpEF was similar in men and women. In race-stratified analyses, lifetime risk for overall HF was higher in nonblacks than blacks (25.9% versus 22.4%). Among HF subtypes, the lifetime risk for HFpEF was higher in nonblacks than blacks (11.2% versus 7.7%), whereas that for HFrEF was similar across the 2 groups. Among participants with antecedent myocardial infarction before HF diagnosis, the remaining lifetime risks for HFpEF and HFrEF were up to 2.5-fold and 4-fold higher, respectively, compared with those without antecedent myocardial infarction.ConclusionsLifetime risks for HFpEF and HFrEF vary by sex, race, and history of antecedent myocardial infarction. These insights into the distribution of HF risk and its subtypes could inform the development of targeted strategies to improve population-level HF prevention and control.
| S-EPMC6417883 | biostudies-literature
Project description:Background Machine-learning-based prediction models (MLBPMs) have shown satisfactory performance in predicting clinical outcomes in patients with heart failure with reduced and preserved ejection fraction. However, their usefulness has yet to be fully elucidated in patients with heart failure with mildly reduced ejection fraction. This pilot study aims to evaluate the prediction performance of MLBPMs in a heart failure with mildly reduced ejection fraction cohort with long-term follow-up data. Methods and Results A total of 424 patients with heart failure with mildly reduced ejection fraction were enrolled in our study. The primary outcome was all-cause mortality. Two feature selection strategies were introduced for MLBPM development. The "All-in" (67 features) strategy was based on feature correlation, multicollinearity, and clinical significance. The other strategy was the CoxBoost algorithm with 10-fold cross-validation (17 features), which was based on the selection result of the "All-in" strategy. Six MLBPMs with 5-fold cross-validation based on the "All-in" and the CoxBoost algorithm with 10-fold cross-validation strategy were developed by the eXtreme Gradient Boosting, random forest, and support vector machine algorithms. The logistic regression model with 14 benchmark predictors was used as a reference model. During a median follow-up of 1008 (750, 1937) days, 121 patients met the primary outcome. Overall, MLBPMs outperformed the logistic model. The "All-in" eXtreme Gradient Boosting model had the best performance, with an accuracy of 85.4% and a precision of 70.3%. The area under the receiver-operating characteristic curve was 0.916 (95% CI, 0.887-0.945). The Brier score was 0.12. Conclusions The MLBPMs could significantly improve outcome prediction in patients with heart failure with mildly reduced ejection fraction, which would further optimize the management of these patients.
| S-EPMC10356044 | 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