ArticleThe Australian & New Zealand journal of obstetrics & gynaecology2026
Before the Burn: Predicting Endometrial Ablation Failure.
Article in The Australian & New Zealand journal of obstetrics & gynaecology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Before the Burn: Predicting Endometrial Ablation Failure.The Australian & New Zealand journal of obstetrics & gynaecology · 2026Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWhile endometrial ablation (EA) offers a minimally invasive alternative to a hysterectomy for women suffering from abnormal uterine bleeding (AUB), clinicians currently lack reliable predictive tools to identify which patients will experience treatment failure, leaving both providers and patients to make treatment decisions with incomplete prognostic information.
aimsThe aim of this study is to identify factors that are associated with failure of EA, and use these to develop and internally validate a model predicting failure after EA. MATERIALS AND
methodsParticipants Women who have undergone an EA at a tertiary health service between the years of 2015 and 2021.
designRetrospective cohort study.
resultsOf the 646 patients who underwent an EA between 2015 and 2021, 21% required ongoing treatment. A model for predicting the failure of endometrial ablation was developed. The presence of fibroids and increasing BMI was associated with failure of EA. Increasing age and insertion of the Mirena at the time of EA made failure less likely.
conclusionsDespite many years of evidence supporting different factors that are associated with failure after an EA, this is the first study to develop a predictive model using Australian data and the first model incorporating the use of Mirena. Ongoing research is suggested to improve model performance and then validate the model externally prior to using it in a clinical context. The nomogram is a demonstration of a possible application of a predictive model.
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