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AimsIn heart failure (HF) with preserved ejection fraction (HFpEF), excessive redistribution of blood volume into the central circulation leads to elevations of intracardiac pressures with exercise limitations. Splanchnic ablation for volume management (SAVM) has been proposed as a therapeutic intervention. Here we present preliminary safety and efficacy data from the initial roll-in cohort of the REBALANCE-HF trial.Methods and resultsThe open-label (roll-in) arm of REBALANCE-HF will enrol up to 30 patients, followed by the randomized, sham-controlled portion of the trial (up to 80 additional patients). Patients with HF, left ventricular ejection fraction (LVEF) ≥50%, and invasive peak exercise pulmonary capillary wedge pressure (PCWP) ≥25 mmHg underwent SAVM. Baseline and follow-up assessments included resting and exercise PCWP, New York Heart Association (NYHA) class, Kansas City Cardiomyopathy Questionnaire (KCCQ), 6-min walk test, and N-terminal pro-B-type natriuretic peptide (NT-proBNP). Efficacy and safety were assessed at 1 and 3 months. Here we report on the first 18 patients with HFpEF that have been enrolled into the roll-in, open-label arm of the study across nine centres; 14 (78%) female; 16 (89%) in NYHA class III; and median (interquartile range) age 75.2 (68.4-81) years, LVEF 61.0 (56.0-63.2)%, and average (standard deviation) 20 W exercise PCWP 36.4 (±8.6) mmHg. All 18 patients were successfully treated. Three non-serious moderate device/procedure-related adverse events were reported. At 1-month, the mean PCWP at 20 W exercise decreased from 36.4 (±8.6) to 28.9 (±7.8) mmHg (p

AIMS: Prognostic enrichment strategies can make trials more efficient, although potentially at the cost of diminishing external validity. Whether using a risk score to identify a population at increased mortality risk could improve trial efficiency is uncertain. We aimed to assess whether Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a previously validated risk score, could improve clinical trial efficiency. METHODS AND RESULTS: Mortality rates and association of MARKER-HF with all-cause death by 1 year were evaluated in four community-based heart failure (HF) and five HF clinical trial cohorts. Sample size required to assess effects of an investigational therapy on mortality was calculated assuming varying underlying MARKER-HF risk and proposed treatment effect profiles. Patients from community-based HF cohorts (n = 11 297) had higher observed mortality and MARKER-HF scores than did clinical trial patients (n = 13 165) with HF with either reduced ejection fraction (HFrEF) or preserved ejection fraction (HFpEF). MARKER-HF score was strongly associated with risk of 1-year mortality both in the community (hazard ratio [HR] 1.48, 95% confidence interval [CI] 1.44-1.52) and clinical trial cohorts with HFrEF (HR 1.41, 95% CI 1.30-1.54), and HFpEF (HR 1.74, 95% CI 1.53-1.98), per 0.1 increase in MARKER-HF. Using MARKER-HF to identify patients for a hypothetical clinical trial assessing mortality reduction with an intervention, enabled a reduction in sample size required to show benefit. CONCLUSION: Using a reliable predictor of mortality such as MARKER-HF to enrich clinical trial populations provides a potential strategy to improve efficiency by requiring a smaller sample size to demonstrate a clinical benefit.

AimsHeart failure (HF) treatment guided by physicians with access to real-time pressure measurement from a wireless implantable pulmonary artery pressure (PAP) sensor (CardioMEMS), has previously been shown to reduce HF-related hospital admissions in the CHAMPION trial. However, uncertainty remains regarding the value of CardioMEMS in European health systems where healthcare costs are significantly lower than in the USA.Methods and resultsA Markov model was developed to estimate the cost-effectiveness of PAP-guided treatment of HF using the CardioMEMS™ HF system compared with usual care. Cost-effectiveness was measured as the incremental cost per quality-adjusted life year (QALY) gained. In the base case analysis over a time horizon of 10 years, PAP-guided HF therapy increased cost compared with usual care by £10 916 (€14 030). QALYs per patient for usual care and PAP-guided patients were 2.57 and 3.14, respectively, reflecting an increase of 0.57 QALYs with PAP-guided treatment. The resultant incremental cost-effectiveness ratio (ICER) is £19 274 (€24 772) per QALY gained. The base case analysis did not include staff time, due to a lack of data concerning this variable. Running the model with estimated staff time included resulted in an increased ICER of between £22 342 and £25 464 per QALY gained (€28 709-32 721).ConclusionThe analysis indicates that integrating wireless PAP monitoring into the management of UK HF patients is likely to be a cost-effective addition to the HF treatment pathway for appropriate patients.

Background: Predicting mortality is important in patients with heart failure (HF). However, current strategies for predicting risk are only modestly successful, likely because they are derived from statistical analysis methods that fail to capture prognostic information in large data sets containing multi-dimensional interactions.Methods and Results: We used a machine learning algorithm to capture correlations between patient characteristics and mortality. A model was built by training a boosted decision tree algorithm to relate a subset of the patient data with a very high or very low mortality risk in a cohort of 5822 hospitalized and ambulatory patients with HF. From this model we derived a risk score that accurately discriminated between low and high-risk of death by identifying eight variables (diastolic blood pressure, creatinine, blood urea nitrogen, haemoglobin, white blood cell count, platelets, albumin, and red blood cell distribution width). This risk score had an area under the curve (AUC) of 0.88 and was predictive across the full spectrum of risk. External validation in two separate HF populations gave AUCs of 0.84 and 0.81, which were superior to those obtained with two available risk scores in these same populations.Conclusions: Using machine learning and readily available variables, we generated and validated a mortality risk score in patients with HF that was more accurate than other risk scores to which it was compared. These results support the use of this machine learning approach for the evaluation of patients with HF and in other settings where predicting risk has been challenging. [ABSTRACT FROM AUTHOR]