Katerina Konakli

Aim: To explore changes in body weight and cardiometabolic risk factors after treatment withdrawal in the STEP 1 trial extension.Materials and Methods: STEP 1 (NCT03548935) randomized 1961 adults with a body mass index ≥ 30 kg/m² (or ≥ 27 kg/m² with ≥ 1 weight‐related co‐morbidity) without diabetes to 68 weeks of once‐weekly subcutaneous semaglutide 2.4 mg (including 16 weeks of dose escalation) or placebo, as an adjunct to lifestyle intervention. At week 68, treatments (including lifestyle intervention) were discontinued. An off‐treatment extension assessed for a further year a representative subset of participants who had completed 68 weeks of treatment. This subset comprised all eligible participants from any site in Canada, Germany and the UK, and sites in the United States and Japan with the highest main phase recruitment. All analyses in the extension were exploratory.Results: Extension analyses included 327 participants. From week 0 to week 68, mean weight loss was 17.3% (SD: 9.3%) with semaglutide and 2.0% (SD: 6.1%) with placebo. Following treatment withdrawal, semaglutide and placebo participants regained 11.6 (SD: 7.7) and 1.9 (SD: 4.8) percentage points of lost weight, respectively, by week 120, resulting in net losses of 5.6% (SD: 8.9%) and 0.1% (SD: 5.8%), respectively, from week 0 to week 120. Cardiometabolic improvements seen from week 0 to week 68 with semaglutide reverted towards baseline at week 120 for most variables.Conclusions: One year after withdrawal of once‐weekly subcutaneous semaglutide 2.4 mg and lifestyle intervention, participants regained two‐thirds of their prior weight loss, with similar changes in cardiometabolic variables. Findings confirm the chronicity of obesity and suggest ongoing treatment is required to maintain improvements in weight and health.

Preposterior analysis can be used to assess the potential of an experiment to enhance decision-making by providing information on parameters of the decision problem that are surrounded by epistemic uncertainties. The present paper describes a framework for preposterior analysis for support of decisions related to maintenance of structural systems. In this context, experiments may refer to inspections or structural health monitoring. The value-of-information concept comprises a powerful tool for determining whether the experimental cost is justified by the expected gained benefit during the lifecycle of a structural system and for identifying the optimal among different possible experimental schemes. This concept is herein elaborated through case studies that involve individual structural components subject to deterioration as well as systems with interdependencies. Extensive numerical investigations demonstrate how the decision problem is influenced by the assumed probabilistic models, including t...

International audienceEvaluation of tail probabilities in reliability analysis faces challenges in cases when complex models with high-dimensional random input are considered. To address such problems, we herein propose the use of surrogate models developed with low-rank tensor approximations. In this approach, the response quantity of interest is expressed as a sum of a few rank-one tensors. We first describe a non-intrusive method for building low-rank approximations using a greedy algorithm, which relies on the solution of minimization problems of only small size. In the sequel, we demonstrate the efficiency of meta-models built in this way in reliability applications involving the deflections of structural systems under static loads and the temperature in stationary heat conduction with spatially varying diffusion coefficient. Furthermore, we show that the proposed approach outperforms the popular meta-modeling technique of polynomial chaos expansions

Pre-posterior analysis can be used to assess the potential of an experiment to enhance decision-making by providing information on parameters characterized by uncertainty. The present paper describes a framework for pre-posterior analysis for support of decisions related to maintenance of structural systems. In this context, experiments may refer to inspections or techniques of structural health monitoring. The Value of Information concept provides a powerful tool for determining whether the experimental cost is justified by the expected benefit and for identifying the optimal among different possible experimental schemes. This concept is elaborated through principal examples for structural components and system models. Sensitivity analyses are performed to investigate how the decision problem is influenced by the level of uncertainty that characterizes the structural properties, the amount and quality of information and the probabilistic dependencies between components of a system.