Chloe HICKS

and 7 more

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.

Jason Mak

and 9 more

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