Joanne Simpson

Is heart rate a risk marker in patients with chronic heart failure and concomitant atrial fibrillation? Results from the MAGGIC meta-analysis.

To investigate the relationship between heart rate and survival in patients with heart failure (HF) and coexisting atrial fibrillation (AF).

Barcelona Bio-HF Calculator Version 2.0: incorporation of angiotensin II receptor blocker neprilysin inhibitor (ARNI) and risk for heart failure hospitalization

Estimating risk for an individual with heart failure (HF) is routine for the practising physician. This may sometimes be done using experience and clinical acumen, or by using a risk model. A number of prediction models with broad variation in terms of validation and output have been developed, but only a few are freely available as online calculators.1 The Barcelona (BCN) Bio-HF Calculator (www.bcnbiohfcalculator.org) (Figure 1),2 developed 3 years ago and discussed in the 2016 European Society of Cardiology HF guidelines,3 incorporates three biomarkers that reflect different facets of HF pathophysiology: 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, which reflects myocardial fibrosis and remodelling. The calculator estimates the risk for all-cause death,2 has been externally validated,4 and was highlighted by Levy and Anand5 as a reference for the appropriate methodology for adding single or multiple variables to a risk model. The combination of clinical and treatment data plus routine laboratory data and biomarkers is also valuable for predicting HF-related hospitalization. Furthermore, the incorporation of novel drugs and devices into the HF armamentarium, notably sacubitril– valsartan, which have strong impacts on death and HF hospitalization,6 prompted an update of the BCN Bio-HF Calculator. The BCN Bio-HF Calculator Version 2.0 was derived from a cohort of 864 consecutive treated HF outpatients [72% men; mean age 68.2±12 years; New York Heart Association (NYHA) class I–II/III–IV 73%/27%, left ventricular ejection fraction (LVEF) 36%, ischaemic aetiology 52.2%].3 During followup of up to 5 years, 363 deaths and 210 first HF-related hospitalizations were recorded; 430 patients suffered at least one event of the composite endpoint. In the update, three new clinical variables (duration of HF in months, number of HF-related hospitalizations in the preceding year, and diabetes mellitus) and four new treatments [mineralocorticoid receptor antagonists, angiotensin II receptor blocker neprilysin inhibitors (ARNI), cardiac resynchronization therapy (CRT) and implantable cardioverter defibrillator (ICD)] were added to the original variables (age, sex, NYHA functional class, LVEF, serum sodium, estimated glomerular filtration rate, haemoglobin, loop diuretic dose, beta-blocker, angiotensin-converting enzyme inhibitor/angiotensin-II receptor blocker and statin treatments, and hs-cTnT, ST2 and NT-proBNP levels). Beta values for ARNI treatment were derived from the benefit observed in the PARADIGM-HF trial, which involved the largest and best characterized cohort of patients treated with ARNIs.6 HFrelated hospitalization was estimated taking into account competing risk for death. Model performance was evaluated using discrimination, calibration and reclassification tools. The C-statistics [area under the curve (AUC)] at 2 years for the model with biomarkers using logistic regression were 0.83 for all-cause death, 0.79 for HF-related hospitalization, and 0.80 for the composite endpoint. Discrimination was significantly better than that obtained in a model without biomarkers for risk for death (P= 0.001), risk for HF hospitalization (P< 0.05) and the composite endpoint (P= 0.001) (supplementary material online, Tables S1–S3). Calibration improved in the model with biomarkers, and reclassification with this model using continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI) was also highly significant (P< 0.001). Using NRI, the BCN Bio-HF Calculator Version 2.0 model with biomarkers reclassified in the correct direction 39% of patients for risk for death, and 42% for risk for the composite endpoint relative to the clinical model (supplementary material online, Tables S4 and S5). Validation for up to 2 years was possible in a subgroup of 1934 patients from the PARADIGM-HF study cohort6 for whom the three biomarkers were available. The C-statistics were 0.70 for both risk for death and risk for HF-related hospitalization at 2 years. Some variables and endpoints differed between the Barcelona derivation cohort and the PARADIGM-HF validation cohort. Indeed, risk prediction for the composite endpoint could not be validated because the composite endpoint in PARADIGM was cardiovascular death or HF-related hospitalization, rather than all-cause death. In a manner similar to the present efforts in a cohort of chronic ambulatory HF patients, the BIOSTAT-CHF study recently developed and validated three risk models to predict all-cause mortality, HF-related hospitalization and the composite endpoint in a cohort of worsening HF patients.7 These researchers obtained C-statistic values of 0.73, 0.69 and 0.71 for the three outcomes, respectively. Both methods are pioneers in their use of HF biomarkers and are appropriate in two different clinical scenarios. In conclusion, the updated version of the BCN Bio-HF Calculator incorporates new clinical variables and allows better individual prediction of all-cause death, HF-related hospitalization and the composite endpoint for up to 5 years. To the best of the present authors’ knowledge, this is the first online calculator to incorporate treatment with an ARNI in the prediction of risk in HF patients. Risk prediction is a cornerstone of HF management. Accurate prediction of risk for death and/or HF hospitalization may identify high-risk patients and candidates for intensified monitoring and treatment, such as drug dose increases, switches to ARNI,

EFFECT OF SACUBITRIL/VALSARTAN COMPARED WITH ENALAPRIL, ACCORDING TO ETIOLOGY IN PARADIGM-HF

Background: Heart failure with reduced ejection fraction (HFREF) has many different causes and etiology varies by age, gender, geography and race/ethnicity. Clinical outcome may also vary by etiology. Controversially, it has been suggested that the effectiveness of certain treatments varies by

Biomarkers in mechanical circulatory support

Heart failure is a complex multifaceted syndrome occurring as a result of impaired cardiac function. Understanding the neurohormonal, inflammatory and molecular pathways involved in the pathophysiology of this syndrome has led to the development of effective and widely used pharmacological treatments. Despite this, mortality and hospitalization rates associated with this condition remain high. The natural course of this illness is usually progressive, often leading inexorably to end stage heart failure, for which orthotopic heart transplant is a treatment option but one with limited resource. In the past decade, mechanical circulatory support has emerged as a potential therapy for certain patients with advanced heart failure. This article reviews the published data regarding biomarkers in the setting of mechanical circulatory support, and highlights areas of ongoing work and potential future areas of interest.

Non-invasive versus invasive management in patients with prior coronary artery bypass surgery with a non-ST segment elevation acute coronary syndrome: study design of the pilot randomised controlled trial and registry (CABG-ACS)

Introduction There is an evidence gap about how to best treat patients with prior coronary artery bypass grafts (CABGs) presenting with non-ST segment elevation acute coronary syndromes (NSTE-ACS) because historically, these patients were excluded from pivotal randomised trials. We aim to undertake a pilot trial of routine non-invasive management versus routine invasive management in patients with NSTE-ACS with prior CABG and optimal medical therapy during routine clinical care. Our trial is a proof-of-concept study for feasibility, safety, potential efficacy and health economic modelling. We hypothesise that a routine invasive approach in patients with NSTE-ACS with prior CABG is not superior to a non-invasive approach with optimal medical therapy. Methods and analysis 60 patients will be enrolled in a randomised clinical trial in 4 hospitals. A screening log will be prospectively completed. Patients not randomised due to lack of eligibility criteria and/or patient or physician preference and who give consent will be included in a registry. We will gather information about screening, enrolment, eligibility, randomisation, patient characteristics and adverse events (including post-discharge). The primary efficacy outcome is the composite of all-cause mortality, rehospitalisation for refractory ischaemia/angina, myocardial infarction and hospitalisation for heart failure. The primary safety outcome is the composite of bleeding, stroke, procedure-related myocardial infarction and worsening renal function. Health status will be assessed using EuroQol 5 Dimensions (EQ-5D) assessed at baseline and 6 monthly intervals, for at least 18 months. Trial registration number NCT01895751 (ClinicalTrials.gov).

Improving outcomes in peripartum cardiomyopathy

Peripartum cardiomyopathy (PPCM) is a rare condition with a diverse spectrum of potential outcomes, ranging from frequent complete recovery to fulminant heart failure and death. The pathogenesis of PPCM is not well understood, and relatively little is known about its incidence and prevalence. PPCM is often under-recognised in the clinical setting. Early investigation and diagnosis with subsequent expert management may improve outcomes. The development of registries will allow this condition to be better characterised and may help answer crucial questions regarding its optimal medical and surgical management. This paper reviews the potential approaches to improve outcomes in patients with PPCM.