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Aims Significant recovery of left ventricular ejection fraction (LVEF) occurs in a proportion of patients with heart failure (HF) and reduced ejection fraction (HFrEF). We analysed outcomes, including mortality [all‐cause, cardiovascular (CV), HF‐related, and sudden death], and HF‐related hospitalizations in this HF‐recovered group. The primary endpoint was a composite of CV death or HF hospitalization. Methods and results LVEF was assessed at baseline and at 1 year in 1057 consecutive HF patients. Patients were classified into three groups: (i) HF‐recovered: LVEF <45% at baseline and ≥45% at 1 year (n = 233); (ii) HF with preserved EF (HFpEF): LVEF ≥45% throughout follow‐up (n = 117); and (iii) HFrEF: LVEF <45% throughout follow‐up (n = 707). Mean follow‐up was 5.6 ± 3.1 years. HF‐recovered patients differed from HFrEF and HFpEF groups in demographic and clinical characteristics. The mean LVEF increase was 21.1 ± 10 points in HF‐recovered patients. Using the HF‐recovered group as a reference, the risks for the primary composite endpoint (n = 376), with non‐CV death as competing risk, for HFpEF and HFrEF groups were: hazard ratio (HR) 2.33 [95% confidence interval (CI) 1.60–3.39], P < 0.001 and HR 1.99 (95% CI 1.50–2.65), P < 0.001, respectively. All‐cause (n = 429), CV (n = 245), HF‐related (n = 127), and sudden death (n = 60) were significantly lower in HF‐recovered subjects relative to HFrEF (all P < 0.01). HF‐recovered patients also experienced less recurrent HF hospitalizations (P < 0.001). Conclusion One in four treated patients with HFrEF showed recovery of systolic function. HF‐recovered patients had significantly improved mortality and morbidity relative to HFpEF and HFrEF subjects. Further research is needed to identify optimal medications and device indications for HF‐recovered patients.

LVEF trajectories vary in HF depending on a number of disease modifiers, but an inverted U-shaped pattern with lower LVEF at both ends of the distribution emerged. A declining LVEF in the preceding period was associated with higher mortality.

Head-to-head comparison of fibrosis biomarkers ST2 and Gal-3 in chronic HF revealed superiority of ST2 over Gal-3 in risk stratification. The incremental predictive contribution of Gal-3 to existing clinical risk factors was trivial.

Identification of circulating neprilysin in HF patients and the positive association of neprilysin with cardiovascular mortality and morbidity further support the importance of NEP inhibition for augmenting natriuretic peptides as a therapeutic target.

BackgroundA combination of clinical and routine laboratory data with biomarkers reflecting different pathophysiological pathways may help to refine risk stratification in heart failure (HF). A novel calculator (BCN Bio-HF calculator) incorporating N-terminal pro B-type natriuretic peptide (NT-proBNP, a marker of myocardial stretch), high-sensitivity cardiac troponin T (hs-cTnT, a marker of myocyte injury), and high-sensitivity soluble ST2 (ST2), (reflective of myocardial fibrosis and remodeling) was developed.MethodsModel performance was evaluated using discrimination, calibration, and reclassification tools for 1-, 2-, and 3-year mortality. Ten-fold cross-validation with 1000 bootstrapping was used.ResultsThe BCN Bio-HF calculator was derived from 864 consecutive outpatients (72% men) with mean age 68.2±12 years (73%/27% New York Heart Association (NYHA) class I-II/III-IV, LVEF 36%, ischemic etiology 52.2%) and followed for a median of 3.4 years (305 deaths). After an initial evaluation of 23 variables, eight independent models were developed. The variables included in these models were age, sex, NYHA functional class, left ventricular ejection fraction, serum sodium, estimated glomerular filtration rate, hemoglobin, loop diuretic dose, β-blocker, Angiotensin converting enzyme inhibitor/Angiotensin-2 receptor blocker and statin treatments, and hs-cTnT, ST2, and NT-proBNP levels. The calculator may run with the availability of none, one, two, or the three biomarkers. The calculated risk of death was significantly changed by additive biomarker data. The average C-statistic in cross-validation analysis was 0.79.ConclusionsA new HF risk-calculator that incorporates available biomarkers reflecting different pathophysiological pathways better allowed individual prediction of death at 1, 2, and 3 years.

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