JMIR Publications

ABSTRACT

Background:

Endometriosis is a chronic inflammatory condition affecting 6-10% of women of reproductive age and defined by the presence of endometrial-like tissue outside the uterus (‘lesions’), commonly affecting the pelvis and ovaries. It is associated with debilitating pelvic pain, infertility and fatigue and often devastating effects on quality of life. Although as common as back pain, it is little understood and treatment and diagnosis are often delayed, leading to unnecessary suffering. Endometriosis has no ‘cure’ . Surgery is one of several management possibilities. Quantifying a woman’s probability of successful surgery is important to guide clinical decisions and treatment strategies. Factors predicting success through pain reduction after endometriosis surgery have not yet been adequately identified.

Objective:

We aim to determine which women with confirmed endometriosis benefit from surgery and see improvement in pain and quality of life and whether these women could be identified from clinical symptoms, measured before laparoscopy.

Methods:

Firstly, we will carry out a systematic search, review and, if appropriate, meta-analysis of observational cohort and case-control studies reporting one or more risk factors for endometriosis and postsurgical treatment success. We will search PubMed, Embase and Cochrane databases from inception without language restrictions, and supplement the reference lists by manual searches. Secondly, we will develop separate clinical prediction models for women with a confirmed or suspected diagnosis of endometriosis. Three suitable databases have been identified for development and external validation; access has been guaranteed. The models will be developed using a linear regression approach linking candidate factors to outcome. Thirdly, we will hold two stakeholder co-design workshops involving eight clinicians and eight women with endometriosis separately and then bring all 16 participants together. Participants will discuss the implementation, delivery, usefulness and sustainability of the prediction models. Clinicians will also focus on ease of use and access to the clinical prediction tool.

Results:

This project was funded in March 2018, approved by the Institutional Research Ethics Board in December 2019, was in the phase of data analysis at the time that final revisions of this article were made, and with results expected to be available in December 2020.

Conclusions:

This study will be the first to predict who will benefit most from laparoscopic surgery through reduction of pain and/or increased quality of life. The models will give clinicians robustly developed and externally validated support tool(s), improving decision making in diagnosis and treatment for women. Clinical Trial: PROSPERO 2018 CRD42018108604