Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Hardness of optimal spaced seed design

Speeding up approximate pattern matching is a line of research in stringology since the 80’s. Practically fast approaches belong to the class of filtration algorithms, in which text regions dissimilar to the pattern are first excluded, and the remaining regions are then compared to the pattern by dynamic programming. Among the conditions used to test similarity between the regions and the pattern, many require a minimum number of common substrings between them. When only substitutions are taken into account for measuring dissimilarity, counting spaced subwords instead of substrings improves the filtration efficiency. However, a preprocessing step is required to design one or more patterns, called spaced seeds (or gapped seeds), for the subwords, depending on the search parameters. Two distinct lines of research appear the literature: one with probabilistic formulations of seed design problems, in which one wishes for instance to compute a seed with the highest probability to detect the desired similarities (lossy filtration), a second line with combinatorial formulations, where the goal is to find a seed that detects all or a maximum numbe

Functional responses can unify invasion ecology

We contend that invasion ecology requires a universal, measurable trait of species and their interactions with resources that predicts key elements of invasibility and ecological impact; here, we advocate that functional responses can help achieve this across taxonomic and trophic groups, among habitats and contexts, and can hence help unify disparate research interests in invasion ecology

Prediction of Relapse After Anti-Tumor Necrosis Factor Cessation in Crohn's Disease: Individual Participant Data Meta-analysis of 1317 Patients From 14 Studies.

BACKGROUND & AIMS: Tools for stratification of relapse risk of Crohn's disease (CD) after anti-tumor necrosis factor (TNF) therapy cessation are needed. We aimed to validate a previously developed prediction model from the diSconTinuation in CrOhn's disease patients in stable Remission on combined therapy with Immunosuppressants (STORI) trial, and to develop an updated model. METHODS: Cohort studies were selected that reported on anti-TNF cessation in 30 or more CD patients in remission. Individual participant data were requested for luminal CD patients and anti-TNF treatment duration of 6 months or longer. The discriminative ability (concordance-statistic [C-statistic]) and calibration (agreement between observed and predicted risks) were explored for the STORI model. Next, an updated prognostic model was constructed, with performance assessment by cross-validation. RESULTS: This individual participant data meta-analysis included 1317 patients from 14 studies in 11 countries. Relapses after anti-TNF cessation occurred in 632 of 1317 patients after a median of 13 months. The pooled 1-year relapse rate was 38%. The STORI prediction model showed poor discriminative ability (C-statistic, 0.51). The updated model reached a moderate discriminative ability (C-statistic, 0.59), and included clinical symptoms at cessation (hazard ratio [HR], 2.2; 95% CI, 1.2-4), younger age at diagnosis (HR, 1.5 for A1 (age at diagnosis ≤16 years) vs A2 (age at diagnosis 17 - 40 years); 95% CI, 1.11-1.89), no concomitant immunosuppressants (HR, 1.4; 95% CI, 1.18-172), smoking (HR, 1.4; 95% CI, 1.15-1.67), second line anti-TNF (HR, 1.3; 95% CI, 1.01-1.69), upper gastrointestinal tract involvement (HR, 1.3 for L4 vs non-L4; 95% CI, 0.96-1.79), adalimumab (HR, 1.22 vs infliximab; 95% CI, 0.99-1.50), age at cessation (HR, 1.2 per 10 years younger; 95% CI, 1-1.33), C-reactive protein (HR, 1.04 per doubling; 95% CI, 1.00-1.08), and longer disease duration (HR, 1.07 per 5 years; 95% CI, 0.98-1.17). In subanalysis, the discriminative ability of the model improved by adding fecal calprotectin (C-statistic, 0.63). CONCLUSIONS: This updated prediction model showed a reasonable discriminative ability, exceeding the performance of a previously published model. It might be useful to guide clinical decisions on anti-TNF therapy cessation in CD patients after further validation