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.