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    The objective of this study was to investigate the utility of using serum gonadotropin levels to predict optimal luteinizing hormone (LH) response to gonadotropin releasing hormone agonist (GnRHa) trigger. A retrospective cohort study was performed of all GnRH-antagonist controlled ovarian hyperstimulation (COH) cycles at an academic fertility center from 2017-2020. Cycles that utilized GnRHa alone or in combination with human chorionic gonadotropin (hCG) for trigger were included. Patient and cycle characteristics were collected from the electronic medical record. Optimal LH response was defined as a serum LH ≥ 40 mIU/mL on the morning after trigger. Total sample size was 3865 antagonist COH cycles, of which 91% had an optimal response to GnRHa trigger. Baseline FSH (B-FSH) and earliest in-cycle LH (EIC-LH) were significantly higher in those with optimal response. Multivariable logistic regression affirmed association of optimal response with EIC-LH, total gonadotropin dosage, age, BMI and Asian race. There was no difference in the number of oocytes retrieved (p = 0.14), maturity rate (p = 0.40) or fertilization rates (p = 0.49) based on LH response. There was no difference in LH response based on use of combination vs. GnRHa alone trigger (p = 0.21) or GnRHa trigger dose (p = 0.46). The EIC-LH was more predictive of LH trigger response than B-FSH (p < 0.005).The optimal B-FSH and EIC-LH values to yield an optimal LH response was ≥ 5.5 mIU/mL and ≥ 1.62 mIU/mL, respectively. In an era of personalized medicine, utilizing cycle and patient characteristics, such as early gonadotropin levels, may improve cycle outcomes and provide further individualized care. © 2022. The Author(s), under exclusive licence to Society for Reproductive Investigation.

    Citation

    Ashley Wiltshire, Jessica Tozour, Dina Hamer, Meredith Akerman, David H McCulloh, James A Grifo, Jennifer Blakemore. Serum Gonadotropin Levels Predict Post-Trigger Luteinizing Hormone Response in Antagonist Controlled Ovarian Hyperstimulation Cycles. Reproductive sciences (Thousand Oaks, Calif.). 2023 Apr;30(4):1335-1342

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    PMID: 36289171

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