Objective To identify a diagnostic microbial signature for endometriosis. Design Prospective cohort study Setting Nepean Hospital and UNSW Microbiome Research Centre, St George Hospital, Australia Population 64 age- and sex-matched subjects ( n=19 HC; n=24 N-ENDO and n=21 ENDO). All study participants, besides healthy controls, underwent laparoscopic surgical assessment for endometriosis, and histology was performed on excised lesions. Methods Oral, stool, and vaginal samples were self-collected at a single time point for healthy controls, and pre-operatively for patients undergoing laparoscopy. Samples underwent 16S rRNA amplicon sequencing, followed by bioinformatics analysis. Main Outcome Measures Compositional differences between cohorts as identified by diversity analyses, and differentially abundant microbial taxa, as identified by LEfSE analysis. Results The composition of the oral, stool, and vaginal microbiota is different between healthy controls and patients with and without endometriosis. Differentially abundant taxa are present within each cohort. Particularly , Fusobacterium was enriched in the oral samples from patients with moderate/severe endometriosis. Conclusions Distinct taxonomic and compositional differences were found between the microbiota in the mouth, gut and vagina of patients with and without endometriosis and healthy controls. Fusobacterium is noted as a key pathogen in periodontal disease, a common comorbidity in endometriosis. These findings support a role for the oral, vaginal, and stool microbiome in endometriosis, and present potential for microbial-based treatments and the design of a diagnostic swab.

Objective To externally validate the “2021 AAGL Endometriosis Classification” staging system. Design Retrospective, diagnostic accuracy study Setting Multicentre Population or Sample Two hundred and seventy-two endometriosis patients (January 2016 - October 2021) Methods Three independent observers analysed coded surgical data to assign an AAGL surgical stage (1 to 4) as the index test, and surgical complexity level (A to D) as the reference standard. Main Outcome Measures The diagnostic accuracy of each AAGL stage to predict corresponding AAGL surgical complexity level was determined. Receiver operating characteristic curves used to determine the accuracy of cut off points used in the AAGL staging system to discriminate between surgical complexity levels. Results 272 cases were analysed. Diagnostic accuracy (sensitivity, specificity, PPV and NPV) for three observers were: stage 1 to predict level A 97.9-98.7%, 60.2-64.2%, 75.0-76.9%, and 96.3-97.5%; stage 2 to predict level B 26.1-30.4%, 93.2-95.6%, 26.3-35.3%, and 92.9-93.6%; stage 3 to predict level C 7.5-10.0%, 93.8-94.8%, 33.3-42.1%, and 70.9-71.5%; stage 4 to predict level D 90.-95.0%, 90.1-91.7% &, 41.9-47.5%, and 99.1-99.6%. For three observers AUROC for A vs B/C/D (cut-point 9) 0.75-0.88, A/B vs C/D (cut-point 16) 0.81 and A/B/C vs D (cut-point 22) 0.95-0.96. Conclusions This external validation study demonstrates that the AAGL Endometriosis Classification performs poorly overall for the prediction of surgical complexity. The results from this external validation study suggest that the system in its current form is not generalizable to all endometriosis patients and should be reviewed before its universal implementation. Funding Nil Keywords Endometriosis, staging, laparoscopy

Objective: To assess the general population’s knowledge regarding the utility and availability of tools to diagnosis endometriosis, with focus on ultrasound. Design: An international cross-sectional online survey study was performed between August and October 2019. Setting and Population: 5301 respondents, representing 73 countries. Methods: 23 questions survey focused on knowledge of endometriosis diagnosis distributed globally via patient- and community-endometriosis groups using social media. Main outcomes and measures: Descriptive data of the knowledge of diagnostic tools for diagnosing endometriosis, including details about diagnosis using ultrasound. Results: 84.0% of respondents had been previously diagnosed with endometriosis, 71.5% of which were diagnosed at the time of surgery. Ultrasound and MRI were the methods of diagnosis in 6.5% and 1.8%, respectively. 91.8%, 28.8%, and 16.6% of respondents believed surgery, ultrasound and MRI could diagnose endometriosis, respectively (more than one answer allowed). In those diagnosed by surgery, 21.7% knew about ultrasound as a diagnosis method compared to 51.5% knowing in those diagnosed non-surgically (p<0.001). 14.7%, 31.1%, and 18.2% stated superficial, ovarian, and deep endometriosis could be diagnosed with ultrasound (32.9% stated they did not know which phenotypes of endometriosis could be diagnosed). 58.4% of respondents do not believe they could access an advanced ultrasound in their region. Conclusions: There are significant gaps in the understanding of diagnosing endometriosis using non-surgical tools in this study population.

The COVID-19 pandemic has led to a dramatic shift in the clinical practice of women’s health and routine care for endometriosis has been severely disrupted. Endometriosis is defined as an inflammatory disease characterised by lesions of endometrial-like tissue outside the uterus that is associated with pelvic pain and/or infertility.1 It affects approximately 10% of reproductive age women worldwide, is diagnosed by surgically visualisation or by radiological imaging, and is treated with hormone treatments or by laparoscopic removal of lesions.2,3Under the guidance of international gynaecological organisations4–6, many centres have temporarily ceased offering outpatient appointments, diagnostic imaging for non-acute pelvic pain, surgery for endometriosis, and fertility treatments. This means that endometriosis sufferers are be feeling particularly vulnerable and that resultant stress and anxiety may contribute to a worsening of symptoms. The pandemic poses several important questions for healthcare providers on how best to deliver care with these restrictions. Herein, we present clinical guidance on the management of endometriosis during the COVID-19 pandemic (Box 1).