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Background Data on the benefits of cardiac resynchronization therapy (CRT) in patients with severe heart failure symptoms are limited. We investigated the relative effects of CRT in patients with ambulatory New York Heart Association (NYHA) IV versus III functional class at the time of device implantation. Methods and Results In this meta‐analysis, we pooled patient‐level data from the MIRACLE (Multicenter InSync Randomized Clinical Evaluation), MIRACLE‐ICD (Multicenter InSync Implantable Cardioversion Defibrillation Randomized Clinical Evaluation), and COMPANION (Comparison of Medical Therapy, Pacing, and Defibrillation in Heart Failure) trials. Outcomes evaluated were time to the composite end point of the first heart failure hospitalization or all‐cause mortality, and time to all‐cause mortality alone. The association between CRT and outcomes was evaluated using a Bayesian hierarchical Weibull survival regression model. We assessed if this association differed between NYHA III and IV groups by adding an interaction term between CRT and NYHA class as a random effect. A sensitivity analysis was performed by including data from RAFT (Resynchronization‐Defibrillation for Ambulatory Heart Failure). Our pooled analysis included 2309 patients. Overall, CRT was associated with a longer time to heart failure hospitalization or all‐cause mortality (adjusted hazard ratio [aHR], 0.79 [95% credible interval [CI], 0.64–0.99]; posterior probability or P=0.044), with a similar association with time to all‐cause mortality (aHR, 0.78 [95% CI, 0.59–1.03]; P=0.083). Associations of CRT with outcomes were not significantly different for those in NYHA III and IV classes (ratio of aHR, 0.72 [95% CI, 0.30–1.27]; P=0.23 for heart failure hospitalization/mortality; ratio of aHR, 0.70 [95% CI, 0.35–1.34]; P=0.27 for all‐cause mortality alone). The sensitivity analysis, including RAFT data, did not show a significant relative CRT benefit between NYHA III and IV classes. Conclusions Overall, there was no significant difference in the association of CRT with either outcome for patients in NYHA functional class III compared with functional class IV.

Background We recently showed that, in patients with heart failure, lower high‐density lipoprotein (HDL) cholesterol concentration was a strong predictor of death or hospitalization for heart failure. In a follow‐up study, we suggested that this association could be partly explained by HDL proteome composition. However, whether the emerging concept of HDL function contributes to the prognosis of patients with heart failure has not been addressed. Methods and Results We measured 3 key protective HDL function metrics, namely, cholesterol efflux, antioxidative capacity, and anti‐inflammatory capacity, at baseline and after 9 months in 446 randomly selected patients with heart failure from BIOSTAT‐CHF (A Systems Biology Study to Tailored Treatment in Chronic Heart Failure). Additionally, the relationship between HDL functionality and HDL proteome composition was determined in 86 patients with heart failure. From baseline to 9 months, HDL cholesterol concentrations were unchanged, but HDL cholesterol efflux and anti‐inflammatory capacity declined (both P

BackgroundTo address the need for personalized prevention, we conducted a subject‐level meta‐analysis within the framework of the Heart “OMics” in AGEing (HOMAGE) study to develop a risk prediction model for heart failure (HF) based on routinely available clinical measurements. Methods and ResultsThree studies with elderly persons (Health Aging and Body Composition [Health ABC], Valutazione della PREvalenza di DIsfunzione Cardiaca asinTOmatica e di scompenso cardiaco [PREDICTOR], and Prospective Study of Pravastatin in the Elderly at Risk [PROSPER]) were included to develop the HF risk function, while a fourth study (Anglo‐Scandinavian Cardiac Outcomes Trial [ASCOT]) was used as a validation cohort. Time‐to‐event analysis was conducted using the Cox proportional hazard model. Incident HF was defined as HF hospitalization. The Cox regression model was evaluated for its discriminatory performance (area under the receiver operating characteristic curve) and calibration (Grønnesby‐Borgan χ2 statistic). During a follow‐up of 3.5 years, 470 of 10 236 elderly persons (mean age, 74.5 years; 51.3% women) developed HF. Higher age, BMI, systolic blood pressure, heart rate, serum creatinine, smoking, diabetes mellitus, history of coronary artery disease, and use of antihypertensive medication were associated with increased HF risk. The area under the receiver operating characteristic curve of the model was 0.71, with a good calibration (χ2 7.9, P=0.54). A web‐based calculator was developed to allow easy calculations of the HF risk. ConclusionsSimple measurements allow reliable estimation of the short‐term HF risk in populations and patients. The risk model may aid in risk stratification and future HF prevention strategies.