Predicting factors of ovarian responses in infertile women with polycystic ovary syndrome undergoing IVF/ICSI

PURPOSE: Women with polycystic ovary syndrome (PCOS) show greater heterogeneity in ovarian responses during ovarian stimulation. We aimed to investigate the potential predicting factors among individualized basic parameters that affect poor or hyper ovarian responses in PCOS patients. METHODS: We retrospectively screened 2058 women with PCOS who underwent their first cycle of in vitro fertilization/intracytoplasmic sperm injection. Spearman correlation analysis and multivariable linear regression model were applied to screen potential variables impacting the number of oocyte retrieved. Further, women with PCOS were divided into poor, sub-optimal, optimal, and hyper responders based on oocyte-retrieved numbers. Logistic regression model and receiver operating characteristic (ROC) curve were used to testify the predicting effect of screened parameters on ovarian response. RESULTS: Multivariable linear regression showed that body mass index (BMI) and follicle-stimulating hormone (FSH) were significantly negatively correlated with oocyte numbers, while luteinizing hormone and anti-Müllerian hormone (AMH) showed a positive correlation. Logistic regression model showed that high BMI (RR: 1.141, 95% CI: 1.090, 1.195) and FSH (RR: 1.161, 95% CI: 1.043, 1.293) were risk factors for poor and sub-optimal ovarian response, but not for hyper response. High AMH level was a risk factor (RR: 1.118, 95% CI: 1.075, 1.163) for hyper ovarian response. The optimal cutoff value was BMI = 23.25 kg/cm2, FSH = 6.375 IU/L, and AMH = 9.8 ng/mL, respectively. CONCLUSIONS: Individualized basic parameters including BMI, FSH, and AMH are crucial for predicting ovarian response of women with PCOS, providing valuable information for formulating personalized diagnosis and treatment plans.