Development and Validation of Clinical Prediction Models for Surgical Success in Patients With Endometriosis: Protocol for a Mixed Methods Study (Preprint)

Nadine Marlin, Carol Rivas, John Allotey, Julie Dodds, Andrew W. Horne, Elizabeth Ball
2020 · doi:10.2196/preprints.20986 · W4239267237
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⚙ AI-generated summary by claude@2026-06, 2026-06-12 ⓘ

This study aims to develop and validate clinical prediction models to identify women with endometriosis who will benefit most from surgical pain reduction and improved quality of life.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-12 · read from full text ⓘ

This mixed-methods study protocol describes the planned development and external validation of clinical prediction models to estimate surgical success in patients with confirmed endometriosis, focusing on pain reduction and quality-of-life improvement after laparoscopy. The investigators will first conduct a systematic review (and possible meta-analysis) of observational studies to identify risk factors associated with postsurgical treatment success, then develop separate linear regression-based prediction models for confirmed and suspected endometriosis using three databases with guaranteed access, and hold stakeholder workshops with clinicians and people with endometriosis to guide implementation and usability; results were expected in December 2020, with analysis underway at revision. A key limitation acknowledged by the protocol is that it is a study design document without reported model performance results yet, so predictive accuracy is not provided. This paper is centrally about endometriosis — it is a protocol to build and validate prediction tools for who benefits most from laparoscopic surgery in endometriosis.

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Abstract

BACKGROUND Endometriosis is a chronic inflammatory condition affecting 6%-10% of women of reproductive age and is 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 has devastating effects on the quality of life (QoL). Although it is as common as back pain, it is poorly understood, and treatment and diagnosis are often delayed, leading to unnecessary suffering. Endometriosis has no cure. Surgery is one of several management options. Quantifying the probability of successful surgery is important for guiding clinical decisions and treatment strategies. Factors predicting success through pain reduction after endometriosis surgery have not yet been adequately identified. OBJECTIVE This study aims to determine which women with confirmed endometriosis benefit from surgical improvement in pain and QoL and whether these women could be identified from clinical symptoms measured before laparoscopy. METHODS First, we will carry out a systematic search and 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. Second, we will develop separate clinical prediction models for women with confirmed and suspected diagnoses of endometriosis. A total of three suitable databases have been identified for development and external validation (the MEDAL [ISRCTN13028601] and LUNA [ISRCTN41196151] studies, and the BSGE database), and access has been guaranteed. The models will be developed using a linear regression approach that links candidate factors to outcomes. Third, we will hold 2 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 the ease of use and access to clinical prediction tools. RESULTS This project was funded in March 2018 and approved by the Institutional Research Ethics Board in December 2019. At the time of writing, this study was in the data analysis phase, and the results are expected to be available in April 2021. CONCLUSIONS This study is the first to aim to predict who will benefit most from laparoscopic surgery through the reduction of pain or increased QoL. The models will provide clinicians with robustly developed and externally validated support tools, improving decision making in the diagnosis and treatment of women. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/20986
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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 Citation Request queued. Please wait while the file is being generated. It may take some time. Copyright © The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.

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Condition tags

endometriosisinfertility

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Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

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