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

Abstract 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

Correlation analysis of the metabolites and the clinical tests in the severe group.

A. Spearman correlations analysis of metabolites and clinical tests. SpO2 = Oxygen saturation; LDH = Lactate dehydrogenase; Art pH = Arterial pH; PaO2 = Partial pressure of oxygen; PaCO2 = Partial pressure of CO2; Art HCO3 = Arterial bicarbonate; PaFi = PaO2/FiO2; TB = Total bilirubin; DB = Direct bilirubin; IB = Indirect bilirubin; ALT = Alanine aminotransferase; AST = Aspartate aminotransferase; AP = Alkaline phosphatase; Alb = Albumin; INR = International Normalized Ratio; CRP = C reactive protein; CPK = Creatine phosphokinase. Different intensities in blue and red numbers indicate a positive or negative association, respectively (see also the gradient scale at the right side of the figure). B. Proposed model for the main changes identified in our cohort. The graphs were constructed with normalized values. H, healthy controls; M, mild/moderate disease; S, severe disease. ‡Direct involvement of cystine over glutamic acid levels through xCT antiporting system. †Enzymatic involvement over glutamic levels through proline dehydrogenase activity. Statistical differences were calculated employing Kruskal-Wallis and Dunn tests. P*<0.05 **<0.01, ***<0.001, ****<0.0001.</p

Main demographic features of the patients included in the analysis.

Main demographic features of the patients included in the analysis.</p

p-values of Spearman´s correlation analysis in Fig 1B.

(DOCX)</p

Partial least squares-discriminant analysis of COVID-19 patients.

A. Score plot of PLS-DA analysis of healthy controls (H), mild/moderate (M), and severe (S) patients. B. Variable importance in the projection (VIP) values that better explain the differences between the groups; C. Enriched metabolite data sets (p<0.05) for the VIP metabolites involved in COVID-19 severity clustering.</p

Untargeted serum metabolomics of COVID-19 patients.

A. Heatmap and hierarchical clustering analysis of COVID-19 and control individuals with all the metabolites included in the analysis. Different intensities in red and blue colors in the heatmap and scale bar at the right side of the figure, denotes increased or reduced levels, respectively. B. Spearman correlation analysis of serum metabolites in COVID-19 and control groups. H, healthy controls, M, mild/moderate disease; S, severe disease. Blue numbers indicate a positive association. Red numbers indicate a negative association. Asterisks denote statistical significance with p-value S3 Table.</p

Principal component analysis of all patients included in the analysis.

Pooled samples (blue intense) were used as a quality control and shows a compact well-defined cluster, ensuring a good reproducibility of our GC/MS analysis. (DOCX)</p

Metabolites with significant changes between the groups.

Metabolites with significant changes between the groups.</p

List of metabolites included in our GC/MS analysis.

(DOCX)</p