Uterine artery embolization versus surgical treatment in patients with symptomatic uterine fibroids: Protocol for a systematic review and meta-analysis of individual participant data.

OBJECTIVE

Uterine fibroids are the most common benign tumours in women of the reproductive age. Symptoms of heavy menstrual bleeding, abdominal discomfort and infertility may seriously affect a woman's quality of life. Uterine artery embolization is a safe and effective alternative treatment to hysterectomy or myomectomy for symptomatic uterine fibroids. Which treatment provides the highest quality of life, least complications, symptom reduction and least chance intervention, has not been established and might depend on strict patient selection. This study aims to identify which specific subgroups benefit most of each treatment by analyzing individual participant data derived from randomized controlled trials of women undergoing embolization or surgical treatment. This study will primarily assess the effectiveness of both treatment groups by evaluating the effect on quality of life of embolization in comparison to surgery on specific patient and fibroid characteristics and the possible need for re-intervention for fibroid-related symptoms.

DATA SOURCES

PubMed/MEDLINE, Embase and The Cochrane Library were searched up to August 2020.

STUDY ELIGIBILITY CRITERIA

We will collect individual participant data from randomized controlled trials that studied clinical and procedural outcomes of premenopausal women with symptomatic uterine fibroids, who were randomized between uterine artery embolization and surgery.

STUDY APPRAISAL AND SYNTHESIS METHODS

Individual participant data from all eligible trials will be sought and analysed according to intention-to-treat principle. Risk of Bias will be done by using version 2 of the Cochrane tool for Risk of Bias in randomized trials. Subgroup analyses to explore the effect of e.g. age, fibroid characteristics and fibroid complaints will be performed, if data is available. This individual patient data meta-analysis will be analysed according to a one-stage model.