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.