Introduction
Endometriosis staging tools have undergone forty years of evolution and multiple iterations, however none has yet gained universal acceptance. The proposed utility of an endometriosis staging tool is also not universally agreed. It is acknowledged that endometriosis staging has an important role in stratifying the disease for research purposes (1), predicting surgical complexity and potentially having a utility with reimbursement (2). Ideally, assignment of an endometriosis stage might be useful in communicating clinically relevant disease severity. To date this has not been achieved. Survey data suggests that most users of existing endometriosis staging tools site a lack of clinical relevance as the main limitation, and would welcome a new tool (3).
The first attempt at classifying endometriosis was published in Lockyer’s book “Fibroids and allied tumors”, in 1918 (4). Since then, the most well-known and widely utilised staging system has been the revised American Society for Reproductive Medicine (rASRM) classification. The system was first published by the ASRM in 1979 (5) and has undergone two revisions, the latest in 1996 (6, 7). Endometriosis is notorious for poor correlation between disease burden and symptomatology. This phenomenon has made it difficult to develop a classification system that predicts clinical outcomes relevant to the patient, which is one of many criticisms of the rASRM staging system (8). It does not correlate with pain, quality of life, fertility or treatment outcomes (8, 9). In addition, it has been criticised for failing to address deep endometriosis and retroperitoneal structures (8, 9). It is arguably time-consuming and cumbersome to use. It’s usefulness is further challenged by the fact that poor interobserver variability has been demonstrated (10).
The three best known attempts at improving the rASRM system have been #Enzian, the Endometriosis Fertility Index (EFI) and the 2021 AAGL Endometriosis Classification. The #Enzian classification system, most recently updated in 2021 (11) after several iterations (12, 13), was originally designed to complement the rASRM system and address deep endometriosis (14). The latest edition is more comprehensive and designed to stand alone encompassing both deep disease, superficial endometriosis and adhesions (11). #Enzian does not result in a global severity stage. Rather, it maps disease in seven separate anatomical domains. It is therefore difficult to quantifiably compare #Enzian to any staging tool.
The EFI is a scoring tool that aims to predict pregnancy rates in individuals with endometriosis (15). It incorporates three components: surgical findings in the form of the rASRM, a functional score of the tubes and ovaries and clinical factors such as age, duration of infertility and previous pregnancy. A recent metanalysis of seventeen studies found the EFI performs well at predicting spontaneous pregnancy rates (16). The tool has also demonstrated good inter-observer agreement (17). Most disagreements in EFI occurred on account of differences in the rASRM score component, suggesting the tool might be amenable to improvement by replacing rASRM with another global staging system.
The “2021 AAGL Endometriosis Classification” staging system, henceforth referred to as the AAGL system, like its predecessor the rASRM, is a points-based staging system (2). A table of anatomical and pathological laparoscopic findings are listed which generate corresponding points, directly proportionate to disease severity. The total point score is then applied to thresholds that determine surgical complexity stages 1 to 4. A large prospective trial demonstrated a high concordance between the AAGL stage and surgical complexity, superior to the rASRM when compared head-to-head (2). Correlation with pain and fertility was also demonstrated, again, superior to the rASRM (2). To our knowledge, this staging system has yet to be externally validated in terms of its stated purpose as a diagnostic tool for predicting surgical complexity. Our objective is to externally validate the diagnostic test performance of the AAGL system.