Abstract

Currently, three crucial questions regarding the reliability of ovarian reserve measures in women with ovarian endometrioma during the reproductive age are being discussed. Firstly, the effects of endometriotic cystectomy on short and long-term ovarian reserve. Secondly, the accuracy of serum anti-Müllerian hormone (AMH) and antral follicle count (AFC) in estimating ovarian reserve in these cases. Thirdly, the impact of endometrioma itself on the ovarian reserve over time in such cases. The purpose of the present review is to critically assess available systematic reviews and meta-analyses that have explored these questions. Nine eligible reviews were found following a systematic search on PubMed.com and similarly assessed. These reviews varied considerably regarding the level of evidence, as per an identical comprehensive scoring system. Moderate to high-quality evidence demonstrates that endometriotic cystectomy, by the stripping technique, adversely affects ovarian reserve in the short and long term, up to 9-18 months post-surgery. Damage to ovarian reserve was considerable but more pronounced in bilateral cases than unilateral cases, equivalent to 39.5% and 57.0%, respectively. Repeat endometriotic cystectomy is detrimental to ovarian reserve. The impact of endometrioma diameter on ovarian reserve before or after surgery is still unclear. Moderate to high-quality evidence, relying on simultaneous assessment of both ovarian reserve measures, shows that AMH is sensitive while AFC is not in cases undergoing ovarian cystectomy. AMH should be the biomarker of choice for counseling and managing women with endometrioma in their reproductive age, especially before surgery. While there is some evidence to show that endometrioma per se may harm ovarian reserve, this evidence is not robust, and there is good-quality evidence to challenge this notion. It is necessary to conduct further targeted RCTs, systematic reviews, and meta-analyses based on solid methodological grounds to increase the level of evidence, refine quantitative estimates, investigate open questions, and decrease heterogeneity.

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