Abstract-Unilateral primary aldosteronism is the most common surgically correctable form of endocrine hypertension and is usually differentiated from bilateral forms by adrenal venous sampling (AVS) or computed tomography (CT

Background There remains uncertainty regarding the second‐best conduit after the internal thoracic artery in coronary artery bypass grafting. Few studies directly compared the clinical results of the radial artery ( RA ), right internal thoracic artery ( RITA ), and saphenous vein ( SV ). No network meta‐analysis has compared these 3 strategies. Methods and Results MEDLINE and EMBASE were searched for adjusted observational studies and randomized controlled trials comparing the RA , SV , and/or RITA as the second conduit for coronary artery bypass grafting. The primary end point was all‐cause long‐term mortality. Secondary end points were operative mortality, perioperative stroke, perioperative myocardial infarction, and deep sternal wound infection ( DSWI ). Pairwise and network meta‐analyses were performed. A total of 149 902 patients (4 randomized, 31 observational studies) were included ( RA , 16 201, SV , 112 018, RITA, 21 683). At NMA , the use of SV was associated with higher long‐term mortality compared with the RA (incidence rate ratio, 1.23; 95% CI , 1.12–1.34) and RITA (incidence rate ratio, 1.26; 95% CI , 1.17–1.35). The risk of DSWI for SV was similar to RA but lower than RITA (odds ratio, 0.71; 95% CI , 0.55–0.91). There were no differences for any outcome between RITA and RA , although DSWI trended higher with RITA (odds ratio, 1.39; 95% CI , 0.92–2.1). The risk of DSWI in bilateral internal thoracic artery studies was higher when the skeletonization technique was not used. Conclusions The use of the RA or the RITA is associated with a similar and statistically significant long‐term clinical benefit compared with the SV . There are no differences in operative risk or complications between the 2 arterial conduits, but DSWI remains a concern with bilateral ITA when skeletonization is not used.

Durable and biodegradable polymer stents along with BRS report a similar rate of MACE irrespective of DAPT length. Fewer MI are observed with EES/ZES with DAPT > 12 m, while a higher rate of ST is reported for BRS when compared to EES/ZES, independently from DAPT length. Stent type may partially affect the outcome together with DAPT length.

Quantitative methodologies have been proposed to support decision making in drug development and monitoring. In particular, multicriteria decision analysis (MCDA) and stochastic multicriteria acceptability analysis (SMAA) are useful tools to assess the benefit-risk ratio of medicines according to the performances of the treatments on several criteria, accounting for the preferences of the decision makers regarding the relative importance of these criteria. However, even in its probabilistic form, MCDA requires the exact elicitations of the weights of the criteria by the decision makers, which may be difficult to achieve in practice. SMAA allows for more flexibility and can be used with unknown or partially known preferences, but it is less popular due to its increased complexity and the high degree of uncertainty in its results. In this paper, we propose a simple model as a generalization of MCDA and SMAA, by applying a Dirichlet distribution to the weights of the criteria and by making its parameters vary. This unique model permits to fit both MCDA and SMAA, and allows for a more extended exploration of the benefit-risk assessment of treatments. The precision of its results depends on the precision parameter of the Dirichlet distribution, which could be naturally interpreted as the strength of confidence of the decision makers in their elicitation of preferences.

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.