Publication:

Assessing the predictive limitations of anti-Müllerian hormone in ovarian response: insights from controlled ovarian stimulation

Background Anti-Müllerian hormone (AMH) is widely used to assess ovarian reserve and predict ovarian response during controlled ovarian stimulation (COS) in in vitro fertilization (IVF) cycles. This study aims to evaluate the accuracy of AMH in predicting ovarian response, embryo quality, and clinical outcomes in IVF/ICSI cycles using a gonadotropin-releasing hormone (GnRH) antagonist protocol with a standardized gonadotropin dose of 150 IU for high and suboptimal responders. Additionally, the study examines the predictive value of alternative markers, such as age and antral follicle count (AFC), especially for suboptimal responders.

Methods This retrospective, single-centre study analysed data from 158 women aged 21-35 years with AMH ≥1.5 ng/ml undergoing their first IVF cycle from July 2022 to July 2023. Patients were categorized into poor responders (<4 oocytes), suboptimal responders (4-9 oocytes), and high responders (≥10 oocytes) based on oocyte retrieval. AMH levels, AFC, and age were assessed as predictors of ovarian response, embryo quality, and implantation rates. Statistical analyses included linear and logistic regression, and receiver operating characteristic (ROC) curves were used to evaluate predictive accuracy.

Results Among 158 patients meeting the inclusion criteria, AMH showed a significant correlation with the number of oocytes retrieved (p=0.0036). High responders had better embryo quality and clinical pregnancy rates compared to suboptimal responders. AMH had a higher predictive value for high response (AUC=0.682) compared to suboptimal response (AUC=0.378), where age was a better predictor (AUC=0.522).

Conclusions AMH is a reliable predictor of high ovarian response in GnRH antagonist protocols but is less effective for suboptimal responders. Comprehensive evaluations incorporating AMH, age, and AFC are crucial for individualized COS strategies to optimize outcomes in assisted reproductive technologies.