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AIMS: Sex differences in long-term outcomes following hospitalization for heart failure (HF) across ejection fraction (EF) subtypes are not well described. In this study, we evaluated the risk of mortality and rehospitalization among males and females across the spectrum of EF over 5 years of follow-up following an index HF hospitalization event. METHODS AND RESULTS: Patients hospitalized with HF between 1 January 2006 and 31 December 2014 from the American Heart Associations Get With The Guidelines-Heart Failure registry with available 5-year follow-up using Medicare Part A claims data were included. The association between sex and risk of mortality and readmission over a 5-year follow-up period for each HF subtype (HF with reduced EF [HFrEF, EF ≤40%], HF with mildly reduced EF [HFmrEF, EF 41-49%], and HF with preserved EF [HFpEF, EF >50%]) was assessed using adjusted Cox models. The effect modification by the HF subtype for the association between sex and outcomes was assessed by including multiplicative interaction terms in the models. A total of 155 670 patients (median age: 81 years, 53.4% female) were included. Over 5-year follow-up, males and females had comparably poor survival post-discharge; however, females (vs. males) had greater years of survival lost to HF compared with the median age- and sex-matched US population (HFpEF: 17.0 vs. 14.6 years; HFrEF: 17.3 vs. 15.1 years; HFmrEF: 17.7 vs. 14.6 years for age group 65-69 years). In adjusted analysis, females (vs. males) had a lower risk of 5-year mortality (adjusted hazard ratio [aHR] 0.89, 95% confidence interval [CI] 0.87-0.90, p

AimsTo assess heart failure (HF) in-hospital quality of care and outcomes before and during the COVID-19 pandemic.Methods and resultsPatients hospitalized for HF with ejection fraction (EF)

AIMS: To evaluate the performance of the WATCH-DM risk score, a clinical risk score for heart failure (HF), in patients with dysglycaemia and in combination with natriuretic peptides (NPs). METHODS AND RESULTS: Adults with diabetes/pre-diabetes free of HF at baseline from four cohort studies (ARIC, CHS, FHS, and MESA) were included. The machine learning- [WATCH-DM(ml)] and integer-based [WATCH-DM(i)] scores were used to estimate the 5-year risk of incident HF. Discrimination was assessed by Harrells concordance index (C-index) and calibration by the Greenwood-Nam-DAgostino (GND) statistic. Improvement in model performance with the addition of NP levels was assessed by C-index and continuous net reclassification improvement (NRI). Of the 8938 participants included, 3554 (39.8%) had diabetes and 432 (4.8%) developed HF within 5 years. The WATCH-DM(ml) and WATCH-DM(i) scores demonstrated high discrimination for predicting HF risk among individuals with dysglycaemia (C-indices = 0.80 and 0.71, respectively), with no evidence of miscalibration (GND P ≥0.10). The C-index of elevated NP levels alone for predicting incident HF among individuals with dysglycaemia was significantly higher among participants with low/intermediate (

Aim: Left ventricular (LV) global longitudinal strain (GLS) may detect subtle abnormalities in myocardial contractility among individuals with normal LV ejection fraction (LVEF). However, the prognostic implications of GLS among healthy, community‐dwelling adults is not well‐established. Methods and results: Overall, 2234 community‐dwelling adults (56% women, 47% Black) with LVEF ≥50% without a history of cardiovascular disease (CVD) from the Dallas Heart Study who underwent cardiac magnetic resonance (CMR) with GLS assessed by feature tracking CMR (FT‐CMR) were included. The association of GLS with the risk of incident major adverse cardiovascular events (MACE; composite of incident myocardial infarction, incident heart failure [HF], hospitalization for atrial fibrillation, coronary revascularization, and all‐cause death), and incident HF or death were assessed with adjusted Cox proportional hazards models. A total of 309 participants (13.8%) had MACE during a median follow‐up duration of 17 years. Participants with the worst GLS (Q4) were more likely male and of the Black race with a history of tobacco use and diabetes with lower LVEF, higher LV end‐diastolic volume, and higher LV mass index. Cumulative incidence of MACE was higher among participants with worse (Q4 vs. Q1) GLS (20.4% vs. 9.0%). In multivariable‐adjusted Cox models that included clinical characteristics, cardiac biomarkers and baseline LVEF, worse GLS (Q4 vs. Q1) was associated with a significantly higher risk of MACE (hazard ratio [HR] 1.55, 95% confidence interval [CI] 1.07–2.24, p = 0.02) and incident HF or death (HR 1.57, 95% CI 1.03–2.38, p = 0.04). Conclusions: Impaired LV GLS assessed by FT‐CMR among adults free of cardiovascular disease is associated with a higher risk of incident MACE and incident HF or death independent of cardiovascular risk factors, cardiac biomarkers and LVEF. [ABSTRACT FROM AUTHOR]

Aims: To evaluate the contribution of baseline and longitudinal changes in cardiometabolic health (CMH) towards heart failure (HF) risk among adults with type 2 diabetes (T2D). Methods and results: Participants of the Look AHEAD trial with T2D and without prevalent HF were included. Adjusted Cox models were used to create a CMH score incorporating target levels of parameters weighted based on relative risk for HF. The associations of baseline and changes in the CMH score with risk of overall HF, HF with preserved (HFpEF) and reduced ejection fraction (HFrEF) were assessed using Cox models. Among the 5080 participants, 257 incident HF events occurred over 12.4 years of follow‐up. The CMH score included 2 points each for target levels of waist circumference, glomerular filtration rate, urine albumin‐to‐creatinine ratio, and 1 point each for blood pressure and glycated haemoglobin at target. High baseline CMH score (6–8) was significantly associated with lower overall HF risk (adjusted hazard ratio [HR], ref = low score (0–3): 0.31, 95% confidence interval [CI] 0.21–0.47) with similar associations observed for HFpEF and HFrEF. Improvement in CMH was significantly associated with lower risk of overall HF (adjusted HR per 1‐unit increase in score at 4 years: 0.80, 95% CI 0.70–0.91). In the ACCORD validation cohort, the baseline CMH score performed well for predicting HF risk with adequate discrimination (C‐index 0.70), calibration (chi‐square 5.53, p = 0.70), and risk stratification (adjusted HR [high (6–8) vs. low score (0–3)]: 0.35, 95% CI 0.26–0.46). In the Look AHEAD subgroup with available biomarker data, incorporating N‐terminal pro‐B‐type natriuretic peptide to the baseline CMH score improved model discrimination (C‐index 0.79) and risk stratification (adjusted HR [high (8–10) vs. low score (0–4)]: 0.18, 95% CI 0.09–0.35). Conclusions: Achieving target levels of more CMH parameters at baseline and sustained improvements were associated with lower HF risk in T2D. [ABSTRACT FROM AUTHOR]

Aims: We aimed to investigate the impact of obstructive coronary artery disease (CAD) in patients with acute decompensated heart failure (ADHF), and examine potential differences in prognostic utility for heart failure with reduced (HFrEF) versus preserved ejection fraction (HFpEF). Methods and results: The Atherosclerosis Risk in Communities study conducted hospital surveillance of ADHF from 2005 to 2014. Obstructive CAD was defined as ≥50% or ≥75% stenosis, respectively, for the left main and other major epicardial arteries. Adjusted associations between obstructive CAD and 30‐, 60‐, and 90‐day mortality were analysed. A total of 934 (4146 weighted) patients admitted with ADHF (mean age 72 years, 46% women, 30% Black, 30% HFpEF) had available angiography (61% performed in hospital). Obstructive CAD was more prevalent with HFrEF than HFpEF, whether at the left main (15% vs. 11%), left anterior descending (LAD) (48% vs. 30%), left circumflex (37% vs. 32%), right coronary (42% vs. 32%), or multiple coronary arteries (45% vs. 33%). In‐hospital revascularization was performed in 25% and 22% of patients with HFrEF and HFpEF, respectively. Obstructive CAD was associated with higher adjusted mortality, particularly with left main or LAD involvement, and had a more pronounced association with 90‐day mortality in HFrEF (odds ratio [OR] 2.77; 95% confidence interval [CI] 1.53–5.02) than HFpEF (OR 0.94; 95% CI 0.36–2.41) (p‐interaction = 0.05). Conclusion: Patients hospitalized with ADHF and coexisting obstructive CAD have higher short‐term mortality, warranting the need for effective interventions and secondary prevention. [ABSTRACT FROM AUTHOR]

Aim: To identify distinct phenotypic subgroups in a highly-dimensional, mixed-data cohort of individuals with heart failure (HF) with preserved ejection fraction (HFpEF) using unsupervised clustering analysis.Methods and Results: The study included all Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) participants from the Americas (n = 1767). In the subset of participants with available echocardiographic data (derivation cohort, n = 654), we characterized three mutually exclusive phenogroups of HFpEF participants using penalized finite mixture model-based clustering analysis on 61 mixed-data phenotypic variables. Phenogroup 1 had higher burden of co-morbidities, natriuretic peptides, and abnormalities in left ventricular structure and function; phenogroup 2 had lower prevalence of cardiovascular and non-cardiac co-morbidities but higher burden of diastolic dysfunction; and phenogroup 3 had lower natriuretic peptide levels, intermediate co-morbidity burden, and the most favourable diastolic function profile. In adjusted Cox models, participants in phenogroup 1 (vs. phenogroup 3) had significantly higher risk for all adverse clinical events including the primary composite endpoint, all-cause mortality, and HF hospitalization. Phenogroup 2 (vs. phenogroup 3) was significantly associated with higher risk of HF hospitalization but a lower risk of atherosclerotic event (myocardial infarction, stroke, or cardiovascular death), and comparable risk of mortality. Similar patterns of association were also observed in the non-echocardiographic TOPCAT cohort (internal validation cohort, n = 1113) and an external cohort of patients with HFpEF [Phosphodiesterase-5 Inhibition to Improve Clinical Status and Exercise Capacity in Heart Failure with Preserved Ejection Fraction (RELAX) trial cohort, n = 198], with the highest risk of adverse outcome noted in phenogroup 1 participants.Conclusions: Machine learning-based cluster analysis can identify phenogroups of patients with HFpEF with distinct clinical characteristics and long-term outcomes. [ABSTRACT FROM AUTHOR]

Aims Methods Conclusions Compared with those without obesity, patients with obesity‐related heart failure with preserved ejection fraction (HFpEF) have worse symptoms, haemodynamics, and outcomes. Current weight loss strategies (diet, drug, and surgical) work through decreased energy intake rather than increased expenditure and cause significant loss of skeletal muscle mass in addition to adipose tissue. This may have adverse implications for patients with HFpEF, who already have reduced skeletal muscle mass and function and high rates of physical frailty. Mitochondrial uncoupling agents may have unique beneficial effects by producing weight loss via increased catabolism rather than reduced caloric intake, thereby causing loss of adipose tissue while sparing skeletal muscle. HU6 is a controlled metabolic accelerator that is metabolized to the mitochondrial uncoupling agent 2,4‐dinotrophenol. HU6 selectively increases carbon oxidation from fat and glucose while also decreasing toxic reactive oxygen species (ROS) production. In addition to sparing skeletal muscle loss, HU6 may have other benefits relevant to obesity‐related HFpEF, including reduced specific tissue depots contributing to HFpEF; improved glucose utilization; and reduction in systemic inflammation via both decreased ROS production from mitochondria and decreased cytokine elaboration from excess, dysfunctional adipose.We describe the rationale and design of HuMAIN‐HFpEF, a Phase 2a randomized, double‐blind, placebo‐controlled, dose‐titration, parallel‐group trial in patients with obesity‐related HFpEF to evaluate the effects of HU6 on weight loss, body composition, exercise capacity, cardiac structure and function, metabolism, and inflammation, and identify optimal dosage for future Phase 3 trials.HuMAIN will test a promising novel agent for obesity‐related HFpEF. [ABSTRACT FROM AUTHOR]

Aims Methods and results Conclusion Abnormalities in specific echocardiographic parameters and cardiac biomarkers have been reported among individuals with diabetes. However, a comprehensive characterization of diabetic cardiomyopathy (DbCM), a subclinical stage of myocardial abnormalities that precede the development of clinical heart failure (HF), is lacking. In this study, we developed and validated a machine learning‐based clustering approach to identify the high‐risk DbCM phenotype based on echocardiographic and cardiac biomarker parameters.Among individuals with diabetes from the Atherosclerosis Risk in Communities (ARIC) cohort who were free of cardiovascular disease and other potential aetiologies of cardiomyopathy (training, n = 1199), unsupervised hierarchical clustering was performed using echocardiographic parameters and cardiac biomarkers of neurohormonal stress and chronic myocardial injury (total 25 variables). The high‐risk DbCM phenotype was identified based on the incidence of HF on follow‐up. A deep neural network (DeepNN) classifier was developed to predict DbCM in the ARIC training cohort and validated in an external community‐based cohort (Cardiovascular Health Study [CHS]; n = 802) and an electronic health record (EHR) cohort (n = 5071). Clustering identified three phenogroups in the derivation cohort. Phenogroup‐3 (n = 324, 27% of the cohort) had significantly higher 5‐year HF incidence than other phenogroups (12.1% vs. 4.6% [phenogroup 2] vs. 3.1% [phenogroup 1]) and was identified as the high‐risk DbCM phenotype. The key echocardiographic predictors of high‐risk DbCM phenotype were higher NT‐proBNP levels, increased left ventricular mass and left atrial size, and worse diastolic function. In the CHS and University of Texas (UT) Southwestern EHR validation cohorts, the DeepNN classifier identified 16% and 29% of participants with DbCM, respectively. Participants with (vs. without) high‐risk DbCM phenotype in the external validation cohorts had a significantly higher incidence of HF (hazard ratio [95% confidence interval] 1.61 [1.18–2.19] in CHS and 1.34 [1.08–1.65] in the UT Southwestern EHR cohort).Machine learning‐based techniques may identify 16% to 29% of individuals with diabetes as having a high‐risk DbCM phenotype who may benefit from more aggressive implementation of HF preventive strategies. [ABSTRACT FROM AUTHOR]