Effect of the COVID-19 pandemic on surgery for indeterminate thyroid nodules (THYCOVID): a retrospective, international, multicentre, cross-sectional study

Background Since its outbreak in early 2020, the COVID-19 pandemic has diverted resources from non-urgent and elective procedures, leading to diagnosis and treatment delays, with an increased number of neoplasms at advanced stages worldwide. The aims of this study were to quantify the reduction in surgical activity for indeterminate thyroid nodules during the COVID-19 pandemic; and to evaluate whether delays in surgery led to an increased occurrence of aggressive tumours.Methods In this retrospective, international, cross-sectional study, centres were invited to participate in June 22, 2022; each centre joining the study was asked to provide data from medical records on all surgical thyroidectomies consecutively performed from Jan 1, 2019, to Dec 31, 2021. Patients with indeterminate thyroid nodules were divided into three groups according to when they underwent surgery: from Jan 1, 2019, to Feb 29, 2020 (global prepandemic phase), from March 1, 2020, to May 31, 2021 (pandemic escalation phase), and from June 1 to Dec 31, 2021 (pandemic decrease phase). The main outcomes were, for each phase, the number of surgeries for indeterminate thyroid nodules, and in patients with a postoperative diagnosis of thyroid cancers, the occurrence of tumours larger than 10 mm, extrathyroidal extension, lymph node metastases, vascular invasion, distant metastases, and tumours at high risk of structural disease recurrence. Univariate analysis was used to compare the probability of aggressive thyroid features between the first and third study phases. The study was registered on ClinicalTrials.gov, NCT05178186.Findings Data from 157 centres (n=49 countries) on 87 467 patients who underwent surgery for benign and malignant thyroid disease were collected, of whom 22 974 patients (18 052 [78 center dot 6%] female patients and 4922 [21 center dot 4%] male patients) received surgery for indeterminate thyroid nodules. We observed a significant reduction in surgery for indeterminate thyroid nodules during the pandemic escalation phase (median monthly surgeries per centre, 1 center dot 4 [IQR 0 center dot 6-3 center dot 4]) compared with the prepandemic phase (2 center dot 0 [0 center dot 9-3 center dot 7]; p<0 center dot 0001) and pandemic decrease phase (2 center dot 3 [1 center dot 0-5 center dot 0]; p<0 center dot 0001). Compared with the prepandemic phase, in the pandemic decrease phase we observed an increased occurrence of thyroid tumours larger than 10 mm (2554 [69 center dot 0%] of 3704 vs 1515 [71 center dot 5%] of 2119; OR 1 center dot 1 [95% CI 1 center dot 0-1 center dot 3]; p=0 center dot 042), lymph node metastases (343 [9 center dot 3%] vs 264 [12 center dot 5%]; OR 1 center dot 4 [1 center dot 2-1 center dot 7]; p=0 center dot 0001), and tumours at high risk of structural disease recurrence (203 [5 center dot 7%] of 3584 vs 155 [7 center dot 7%] of 2006; OR 1 center dot 4 [1 center dot 1-1 center dot 7]; p=0 center dot 0039).Interpretation Our study suggests that the reduction in surgical activity for indeterminate thyroid nodules during the COVID-19 pandemic period could have led to an increased occurrence of aggressive thyroid tumours. However, other compelling hypotheses, including increased selection of patients with aggressive malignancies during this period, should be considered. We suggest that surgery for indeterminate thyroid nodules should no longer be postponed even in future instances of pandemic escalation.Funding None.Copyright (c) 2023 Published by Elsevier Ltd. All rights reserved

Global wealth disparities drive adherence to COVID-safe pathways in head and neck cancer surgery

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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

Utilizing graph classes for community detection in social and complex networks

Social network analysis is a cross-disciplinary study of interest to mathematicians, physicists, computer scientists and sociologists. It deals with looking at large networks of interactions and extracting useful or meaningful information from them. One attribute of interest is that of identifying social communities within a network: how such a substructure should be defined is a widely-studied problem in itself. With each new definition, there is a need to study in what applications or context such a definition is appropriate, and develop algorithms and complexity results for the computation of these clusterings. This thesis studies problems related to graph clustering, motivated by the social networking problem of community detection. One main contribution of this thesis is a new definition of a specific kind of family-like community, accompanied by theoretical and computational justifications. Additional results in this thesis include proofs of hardness for the quasi-threshold editing problem and the diameter augmentation problem, as well as improved exact algorithms for cograph and quasi-threshold edge deletion and vertex deletion problems.Graduate Studies, College of (Okanagan)Graduat