In patients with HFpEF, HR reduction with ivabradine did not improve outcomes. These findings do not support the use of ivabradine in HFpEF.

Aims A network meta‐analysis (NMA) of all recommended drug groups for the treatment of heart failure with reduced ejection fraction (HFrEF), including their combinations, was performed to assess the relative efficacy and incremental benefit. Methods and results. A search was made in biomedical databases for randomized controlled trials published between 1987 and 2017 on angiotensin‐converting enzyme inhibitors (ACEIs), beta‐blockers (BBs), angiotensin receptor blockers (ARBs), mineralocorticoid receptor antagonists (MRAs), ivabradine (IVA), or angiotensin receptor–neprilysin inhibitors (ARNI). A total of 58 relevant trials were identified. The relative efficacy of each treatment group (or combination) in terms of all‐cause mortality, cardiovascular mortality, all‐cause hospitalizations and hospitalizations for heart failure, per patient‐year of follow‐up, were combined in a random‐effects Bayesian NMA. The pairwise comparison between each regimen and for each outcome was estimated. The NMA was dominated by 15 large‐scale trials with between 1984 and 18 898 patient‐years of follow‐up. Combinations of drug groups showed incremental benefits on outcomes over single groups. The most effective combinations were ARNI+BB + MRA and ACEI+BB + MRA + IVA, showing reductions in all‐cause mortality (vs. placebo) of 62% and 59%, respectively; hazard ratios were 0.38 [credible interval (CrI) 0.20–0.65] and 0.41 (CrI 0.21–0.70); and in all‐cause hospitalizations with reductions of 42% for both. These two combinations were also the most effective for the other outcomes studied. Conclusion Our analysis shows that the incremental use of combinations of disease‐modifying therapies has resulted in the progressive improvement in mortality and hospitalization outcomes in HFrEF. Our findings support the current guideline recommendations.

The predictive probability of success of a future clinical trial is a key quantitative tool for decision‐making in drug development. It is derived from prior knowledge and available evidence, and the latter typically comes from the accumulated data on the clinical endpoint of interest in previous clinical trials. However, a surrogate endpoint could be used as primary endpoint in early development and, usually, no or limited data are collected on the clinical endpoint of interest. We propose a general, reliable, and broadly applicable methodology to predict the success of a future trial from surrogate endpoints, in a way that makes the best use of all the available evidence. The predictions are based on an informative prior, called surrogate prior, derived from the results of past trials on one or several surrogate endpoints. If available, in a Bayesian framework, this prior could be combined with data from past trials on the clinical endpoint of interest. Two methods are proposed to address a potential discordance between the surrogate prior and the data on the clinical endpoint. We investigate the patterns of behavior of the predictions in a comprehensive simulation study, and we present an application to the development of a drug in Multiple Sclerosis. The proposed methodology is expected to support decision‐making in many different situations, since the use of predictive markers is important to accelerate drug developments and to select promising drug candidates, better and earlier.