ArticleJournal of assisted reproduction and genetics2025
Predicting factors of ovarian responses in infertile women with polycystic ovary syndrome undergoing IVF/ICSI.
Article in Journal of assisted reproduction and genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- An explainable machine learning model for identifying the risk of suboptimal oocyte maturation in patients with polycystic ovary syndrome undergoing GnRH-antagonist protocol.Journal of ovarian research · 2026Article
- Living with PCOS: A Narrative of its Biology, Diagnosis, and Evolving Treatment.Endocrine, metabolic & immune disorders drug targets · 2026Review
- The management of slow ovarian response in PCOS patients and its impact on clinical pregnancy outcomes.Frontiers in endocrinology · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
purposeWomen 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.
methodsWe 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.
resultsMultivariable 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/cm
conclusionsIndividualized 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.
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