Outcomes reported in randomised trials of surgical prehabilitation: a scoping review

BACKGROUND: Heterogeneity of reported outcomes can impact the certainty of evidence for prehabilitation. The objective of this scoping review was to systematically map outcomes and assessment tools used in trials of surgical prehabilitation. METHODS: MEDLINE, EMBASE, PsychInfo, Web of Science, CINAHL, and Cochrane were searched in February 2023. Randomised controlled trials of unimodal or multimodal prehabilitation interventions (nutrition, exercise, psychological support) lasting at least 7 days in adults undergoing elective surgery were included. Reported outcomes were classified according to the International Society for Pharmacoeconomics and Outcomes Research framework. RESULTS: We included 76 trials, mostly focused on abdominal or orthopaedic surgeries. A total of 50 different outcomes were identified, measured using 184 outcome assessment tools. Observer-reported outcomes were collected in 86% of trials (n=65), with hospital length of stay being most common. Performance outcomes were reported in 80% of trials (n=61), most commonly as exercise capacity assessed by cardiopulmonary exercise testing. Clinician-reported outcomes were included in 78% (n=59) of trials and most frequently included postoperative complications with Clavien-Dindo classification. Patient-reported outcomes were reported in 76% (n=58) of trials, with health-related quality of life using the 36- or 12-Item Short Form Survey being most prevalent. Biomarker outcomes were reported in 16% of trials (n=12) most commonly using inflammatory markers assessed with C-reactive protein. CONCLUSIONS: There is substantial heterogeneity in the reporting of outcomes and assessment tools across surgical prehabilitation trials. Identification of meaningful outcomes, and agreement on appropriate assessment tools, could inform the development of a prehabilitation core outcomes set to harmonise outcome reporting and facilitate meta-analyses

QAPgrid: A Two Level QAP-Based Approach for Large-Scale Data Analysis and Visualization

Background: The visualization of large volumes of data is a computationally challenging task that often promises rewarding new insights. There is great potential in the application of new algorithms and models from combinatorial optimisation. Datasets often contain “hidden regularities” and a combined identification and visualization method should reveal these structures and present them in a way that helps analysis. While several methodologies exist, including those that use non-linear optimization algorithms, severe limitations exist even when working with only a few hundred objects. Methodology/Principal Findings: We present a new data visualization approach (QAPgrid) that reveals patterns of similarities and differences in large datasets of objects for which a similarity measure can be computed. Objects are assigned to positions on an underlying square grid in a two-dimensional space. We use the Quadratic Assignment Problem (QAP) as a mathematical model to provide an objective function for assignment of objects to positions on the grid. We employ a Memetic Algorithm (a powerful metaheuristic) to tackle the large instances of this NP-hard combinatorial optimization problem, and we show its performance on the visualization of real data sets. Conclusions/Significance: Overall, the results show that QAPgrid algorithm is able to produce a layout that represents the relationships between objects in the data set. Furthermore, it also represents the relationships between clusters that are feed into the algorithm. We apply the QAPgrid on the 84 Indo-European languages instance, producing a near-optimal layout. Next, we produce a layout of 470 world universities with an observed high degree of correlation with the score used by the Academic Ranking of World Universities compiled in the The Shanghai Jiao Tong University Academic Ranking of World Universities without the need of an ad hoc weighting of attributes. Finally, our Gene Ontology-based study on Saccharomyces cerevisiae fully demonstrates the scalability and precision of our method as a novel alternative tool for functional genomics

A Critical Element-Guided Perturbation Strategy for Iterated Local Search

Abstract. In this paper, we study the perturbation operator of Iterated Local Search. To guide more efficiently the search to move towards new promising regions of the search space, we introduce a Critical Element-Guided Perturbation strategy (CEGP). This perturbation approach consists of the identification of critical elements and then focusing on these critical elements within the perturbation operator. Computational experiments on two case studies—graph coloring and course timetabling—give evidence that this critical element-guided perturbation strategy helps reinforce the performance of Iterated Local Search