Individualized ovarian stimulation in IVF/ICSI treatment: it is time to stop using high FSH doses in predicted low responders. Issue 9 (15th December 2019)

• Record Type: Journal Article
• Authors: Leijdekkers, Jori A; Torrance, Helen L; Schouten, Nienke E; van Tilborg, Theodora C; Oudshoorn, Simone C; Mol, Ben Willem J; Eijkemans, Marinus J C; Broekmans, Frank J M
• Is Part Of: Human reproduction. Volume 35:Issue 9(2020)
• Page Start: 1954
• Page End: 1963
• Publication Date: 2019-12-15
• Subjects: FSH dosing -- predicted low responder -- oocyte number -- live birth -- IVF/ICSI; Human reproduction -- Periodicals; 618
• DOI: 10.1093/humrep/dez184
• Languages: English
• ISSNs: 0268-1161

Abstract

ABSTRACT: In IVF/ICSI treatment, the FSH starting dose is often increased in predicted low responders from the belief that it improves the chance of having a baby by maximizing the number of retrieved oocytes. This intervention has been evaluated in several randomized controlled trials, and despite a slight increase in the number of oocytes—on average one to two more oocytes in the high versus standard dose group—no beneficial impact on the probability of a live birth has been demonstrated (risk difference, −0.02; 95% CI, −0.11 to 0.06). Still, many clinicians and researchers maintain a highly ingrained belief in 'the more oocytes, the better'. This is mainly based on cross-sectional studies, where the positive correlation between the number of retrieved oocytes and the probability of a live birth is interpreted as a direct causal relation. If the latter would be present, indeed, maximizing the oocyte number would benefit our patients. The current paper argues that the use of high FSH doses may not actually improve the probability of a live birth for predicted low responders undergoing IVF/ICSI treatment and exemplifies the flaws of directly using cross-sectional data to guide FSH dosing in clinical practice. Also, difficulties in the de-implementation of the increased FSH dosing strategy are discussed, which include the prioritization of intermediate outcomes (such as cycle cancellations) and the potential biases in the interpretation of study findings (such as confirmation or rescue bias).