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: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: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: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:Aims: The aim of the study was to determine the associations of weight loss or gain with all-cause mortality risk in heart failure with preserved ejection fraction (HFpEF). Methods and Results: Non-lean patients from the Americas from the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist study were analyzed (n = 1,515). Weight loss and weight gain were defined as a decrease or increase in weight ≥5% between baseline and 1 year. To determine the associations of weight change and mortality risk, we used adjusted Cox proportional hazards models and restricted cubic spline models. The mean age was 71.5 (9.6) years. Weight loss and gain were witnessed in 19.3 and 15.9% patients, respectively. After multivariable adjustment, weight loss was associated with higher risk of mortality (HR 1.42, 95% CI 1.06-1.89, P = 0.002); weight gain had similar risk of mortality (HR 0.98, 95% CI 0.68-1.42, P = 0.932) compared with weight stability. There was linear relationship between weight change and mortality risk. The association of weight loss and mortality was different for patients with and without diabetes mellitus (interaction p = 0.009). Conclusion: Among patients with HFpEF, weight loss was independently associated with higher risk of all-cause mortality, and weight gain was not associated with better survival. Clinical Trial Registration: https://clinicaltrials.gov, Identifier: NCT00094302.
| S-EPMC8213199 | biostudies-literature
Project description:Heart failure is defined as a clinical syndrome and is known to present with a number of different pathophysiological patterns. There is a remarkable degree of variation in measures of left ventricular systolic emptying and this has been used to categorise heart failure into two separate types: low ejection fraction (EF) heart failure or HF-REF and high EF heart failure or HF-PEF. Here we review the pathophysiology, epidemiology and management of HF-PEF and argue that sharp separation of heart failure into two forms is misguided and illogical, and the present scarcity of clinical trial evidence for effective treatment for HF-PEF is a problem of our own making; we should never have excluded patients from major trials on the basis of EF in the first place. Whilst as many heart failure patients have preserved EFs as reduced we have dramatically under-represented HF-PEF patients in trials. Only four trials have been performed in HF-PEF specifically, and another two trials that recruited both HF-PEF and HF-REF can be considered. When we consider the similarity in outcomes and neurohormonal activation between HF-REF and HF-REF, the vast corpus of trial data that we have to attest to the efficacy of various treatment (angiotensin-converting-enzyme [ACE] inhibitors, angiotensin receptor blockers [ARBs], beta-blockers and aldosterone antagonists) in HF-REF, and the much more limited number of trials of similar agents showing near statistically significant benefits in HF-PEF the time has come rethink our management of HF-PEF, and in particular our selection of patients for trials.
| S-EPMC5490948 | biostudies-other
Project description:ObjectiveHeart failure with mildly reduced ejection fraction (HFmrEF) has been recently recognized as a unique phenotype of heart failure (HF) in current practical guideline. However, risk stratification models for mortality and HF re-hospitalization are still lacking. This study aimed to develop and validate a novel machine learning (ML)-derived model to predict the risk of mortality and re-hospitalization for HFmrEF patients.MethodsWe assessed the risks of mortality and HF re-hospitalization in HFmrEF (45-49%) patients enrolled in the TOPCAT trial. Eight ML-based models were constructed, including 72 candidate variables. The Harrell concordance index (C-index) and DeLong test were used to assess discrimination and the improvement in discrimination between models, respectively. Calibration of the HF risk prediction model was plotted to obtain bias-corrected estimates of predicted versus observed values.ResultsLeast absolute shrinkage and selection operator (LASSO) Cox regression was the best-performing model for 1- and 6-year mortality, with a highest C-indices at 0.83 (95% CI: 0.68-0.94) over a maximum of 6 years of follow-up and 0.77 (95% CI: 0.64-0.89) for the 1-year follow-up. The random forest (RF) showed the best discrimination for HF re-hospitalization, scoring 0.80 (95% CI: 0.66-0.94) and 0.85 (95% CI: 0.71-0.99) at the 6- and 1-year follow-ups, respectively. For risk assessment analysis, Kansas City Cardiomyopathy Questionnaire (KCCQ) subscale scores were the most important predictor of readmission outcome in the HFmrEF patients.ConclusionML-based models outperformed traditional models at predicting mortality and re-hospitalization in patients with HFmrEF. The results of the risk assessment showed that KCCQ score should be paid increasing attention to in the management of HFmrEF patients.
| S-EPMC9748556 | biostudies-literature