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Aims Heart failure (HF) patients have a high risk of mortality due to sudden cardiac death (SCD) and non-SCD, including pump failure death (PFD). Anaemia predicts more severe symptomatic burden and higher morbidity, as noted by markedly increased hospitalizations and readmission rates, and mortality, underscoring its importance in HF management. Herein, we aimed to determine whether haemoglobin (Hb) level at discharge affects the mode of death and influences SCD risk prediction. Methods We evaluated the data of 3020 consecutive acute HF patients from a Japanese prospective multicentre registry. Patients were divided into four groups based on discharge Hb levels. SCD was defined as an unexpected and otherwise unexplained death in a previously stable patient or death due to documented or presumed cardiac arrhythmia without a clear non-cardiovascular cause. The mode of death (SCD, PFD or other cause) was adjudicated by a central committee. Finally, we investigated whether adding Hb level to the Seattle Proportional Risk Model (SPRM; established risk score utilized to estimate ‘proportion’ of SCD among death events) would affect its performance. Results The mean age of studied patients was 74.3 ± 12.9 years, and 59.8% were male. The mean Hb level was 12.0 ± 2.1 g/dL (61.3% of patients had anaemia defined by World Health Organization criteria). During the 2-year follow-up, 474 deaths (15.7%) occurred, including 93 SCDs (3.1%), 171 PFDs (5.7%) and 210 other deaths (7.0%; predominantly non-cardiac death). Absolute risk of PFD (P < 0.001) or other death (P < 0.001) increased along with the severity of anaemia, whereas the incidence of SCD was low but remained consistent across all four groups (P = 0.440). As a proportion of total deaths in each Hb level group, the contributions from non-SCD increased and from SCD decreased along with anaemia severity (P = 0.007). Adding Hb level to the SPRM improved the overall discrimination (c-index: 0.62 [95% confidence interval (CI) 0.56–0.69] to 0.65 [95% CI 0.59–0.71]), regardless of the baseline ejection fraction (EF) (c-index: 0.64 [95% CI 0.55–0.73] to 0.67 [95% CI 0.58–0.75] for reduced EF and 0.55 [95% CI 0.45–0.66] to 0.61 [95% CI 0.52–0.70] for preserved EF). Conclusions The discharge Hb level provides information about both absolute and proportional risks for each mode of death in acute HF patients, and the addition of Hb level improves the performance of SPRM by identifying more non-SCD cases. Future ‘proportional’ SCD risk models should incorporate Hb level as a covariate to meet this high performance. Keywords Heart failure; Haemoglobin level; Anaemia; Sudden cardiac death; Risk prediction model. [ABSTRACT FROM AUTHOR]

Background: To evaluate whether planar 123I-MIBG myocardial scintigraphy predicts risk of death in heart failure (HF) patients up to 5 years after imaging.Methods and Results: Subjects from ADMIRE-HF were followed for approximately 5 years after imaging (964 subjects, median follow-up 62.7 months). Subjects were stratified according to the heart/mediastinum (H/M) ratio (< 1.60 vs ≥ 1.60) on planar 123I-MIBG scintigraphic images obtained at baseline in ADMIRE-HF. Cox proportional hazards models and Kaplan-Meier analyses were used to evaluate time to death, cardiac death, or arrhythmic events for subjects stratified by H/M ratio, baseline left ventricular ejection fraction (LVEF: < 25% and 25 to ≤ 35%), and by H/M strata within LVEF strata. All-cause mortality was 38.4% vs 20.9% and cardiac mortality was 16.8% vs 4.5%, in subjects with H/M < 1.60 vs ≥ 1.60, respectively (P < 0.05 for both comparisons). Subjects with preserved sympathetic innervation of the myocardium (H/M ≥ 1.60) were at significantly lower risk of all-cause and cardiac death, arrhythmic events, sudden cardiac death, or potentially life-threatening arrhythmias. Within LVEF strata, a trend toward a higher mortality for subjects with H/M < 1.60 was observed reaching significance for LVEF 25 to ≤ 35% only.Conclusions: During a median follow-up of 62.7 months, patients with H/M ≥ 1.60 were at significantly lower risk of death and arrhythmic events independently of LVEF values. [ABSTRACT FROM AUTHOR]

Background: Prognostic evaluation in heart failure (HF) is important to predict future events and decide timely management. Many HF patients are treated with the use of an implantable cardioverter-defibrillator (ICD). This study aimed to validate a meta-analytically derived prognostic score to predict survival in ICD-HF patients.Methods and Results: The HF Meta-score includes 14 independent mortality predictors identified in a meta-analysis, including age, sex, ethnicity, diabetes, chronic obstructive pulmonary disease, peripheral vascular disease, atrial fibrillation, ischemic cardiomyopathy, history of HF admission, New York Heart Association functional class, left ventricular ejection fraction, renal function, QRS duration, secondary prevention indication, and ICD shocks. The HF Meta-score performance was evaluated in comparison with the Seattle Heart Failure Model (SHFM) and the SHOCKED predictors in a cohort of 9860 ambulatory ICD patients from the Ontario provincial database for 2007-2011. During 3-year follow-up, 1816 patients died. The HF Meta-score showed excellent calibration, very good discrimination (c-statistic 0.74) and enhanced risk classification compared with the SHOCKED predictors, with better reclassifying in 19% and 56% of patients for 1- and 3-year survival, respectively. HF Meta-score performance was similar to the SHFM.Conclusions: The HF Meta-score is an evidence-based derived model that provides an accurate prognosis assessment in HF patients with ICDs. Its development strategy permits further incorporation of new predictors when evidence becomes available. [ABSTRACT FROM AUTHOR]

Background: The timing of transplant listing after implantation of a left ventricular assist device (LVAD) remains uncertain, given high device complication rates and apparent stability of some LVAD-supported patients. This investigation quantifies the effect of delayed transplant listing and transplantation rates on medium-term survival and LVAD complications.Methods and Results: A Markov model was used to simulate the effects of delaying initial transplant listing after LVAD implantation. Modeled parameters were derived from the Standard Transplant Analysis and Research file. When transplant listing was delayed and 5-year results were examined, fewer persons underwent transplantation (53% in base model vs 51% in 180-day-delay model) and the fraction of deaths while waiting increased (17% in base model vs 21% in 180-day delay model). Life expectancy changed minimally from the base model (3.50 y) when initial listing was delayed by 180 days (3.51 y).Conclusions: Delaying initial transplant listing increased the likelihood of death while waiting for a transplant and decreased the likelihood of transplantation. In aggregate, life expectancy was unchanged by delays in listing. This study suggests that delaying transplant listing with the expectation of providing additional life expectancy is not likely with current LVAD technology. [ABSTRACT FROM AUTHOR]

Purpose of Review: While prediction models incorporating biomarkers are used in heart failure, these have shown wide-ranging discrimination and calibration. This review will discuss externally validated biomarker-based risk models in chronic heart failure patients assessing their quality and relevance to clinical practice.Recent Findings: Biomarkers may help in determining prognosis in chronic heart failure patients as they reflect early pathologic processes, even before symptoms or worsening disease. We present the characteristics and describe the performance of 10 externally validated prediction models including at least one biomarker among their predictive factors. Very few models report adequate discrimination and calibration. Some studies evaluated the additional predictive value of adding a biomarker to a model. However, these have not been routinely assessed in subsequent validation studies.Summary: New and existing prediction models should include biomarkers, which improve model performance. Ongoing research is needed to assess the performance of models in contemporary patients. [ABSTRACT FROM AUTHOR]