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Preoperative application of the Enzian classification for endometriosis (The cEnzian Study): A prospective
Simon-Hermann Enzelsberger1, Peter Oppelt1, Konstantinos Nirgianakis2
1Department of Gynecology, Obstetrics and Gynecological Endocrinology, Kepler University Hospital, Johannes Kepler University Linz, Linz, Austria.
Objective:
To assess the diagnostic performance of preoperative application of the Enzian classification (cEnzian) using surgical findings as reference standard.
Design:
A prospective international non-interventional study.
Setting:
Twelve endometriosis centres in four European countries (Austria, Germany, Switzerland and Czech Republic).
Population:
1062 women with endometriosis surgery.
Methods:
Extent of endometriosis was preoperatively classified using the cEnzian classification based on gynaecological examination and/or transvaginal ultrasound (TVS) and/or magnetic resonance imaging (MRI). After subsequent surgery, the surgeon classified the intraoperative findings using the Enzian classification.
Main Outcome Measures:
Sensitivity, specificity, PPV, NPV, LR+ , LR- and accuracy were calculated. Conditional frequencies of intraoperative Enzian codings and the corresponding 95% confidence intervals were computed for each preoperative coding and visualised in plots.
Results:
Although overall consistency of cEnzian and Enzian was poor (35.14%, 95% confidence interval 32.26-38.03), high specificities and negative predictive values (NPVs) of the cEnzian compartments could be demonstrated. Looking at the individual parts of the Enzian classification, the poorest diagnostic performance was detected for compartment B and the highest PPVs were found for category 3 lesions (>3 cm), independent of the compartment.
Conclusions:
Using the Enzian classification in a non-invasive setting is a useful tool providing us with an 'at a glance' summary of the diagnostic workup regarding deep endometriosis with high specificities and NPVs. An attempt to merge the two new endometriosis classification systems (#Enzian and AAGL 2021) seems reasonable taking into consideration the respective advantages of each other.
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