ArticleJournal of assisted reproduction and genetics2024
Constructing a predictive model for live birth following fresh embryo transfer in antagonist protocol for polycystic ovary syndrome.
Article in Journal of assisted reproduction and genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Association of pre-pregnancy abnormal glucose metabolism and insulin resistance with assisted reproductive technology outcomes and pregnancy complications in women with PCOS: a retrospective propensity score matching study.BMC pregnancy and childbirth · 2026Article
- Associations of lipid metabolic parameters with cumulative live birth rate in PCOS patients undergoing IVF/ICSI and development of a nomogram-based predictive model.Frontiers in endocrinology · 2026Article
- Development and temporal validation of a prediction model for live birth in infertile women with polyendocrine metabolic ovarian syndrome: a retrospective cohort study.Frontiers in reproductive health · 2026Article
- Development and validation of a LASSO logistic regression based nomogram for predicting live births in women with polycystic ovary syndrome: a retrospective cohort study.Frontiers in endocrinology · 2025Article
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6 authors.
Funding
Abstract
objectiveThe present research aims to assess the factors that influence live birth outcomes following fresh embryo transfers using antagonist protocols in individuals diagnosed with polycystic ovary syndrome (PCOS). Furthermore, it seeks to develop a predictive nomogram model to facilitate clinical decision-making and provide personalized treatment strategies.
methodsThis retrospective cohort research analyzed the clinical data of 1242 individuals having PCOS who went through fresh embryo transfers employing antagonist protocols and in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) at Fujian Provincial Maternal and Child Health Hospital between January 2018 and December 2022. Individuals were assigned randomly to a modeling group (869 cases) and a validation group (373 cases) in a 7:3 ratio. The Boruta algorithm and multivariable logistic regression were utilized to identify independent risk factors linked to live births after transfer. A predictive nomogram was subsequently developed. The discriminatory power of the model and its accuracy were monitored by utilizing receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis.
resultsMultivariable logistic regression analysis identified several independent factors that influence live birth rates in fresh embryo transfer cycles for individuals having PCOS using antagonist protocols, including female age, body mass index (BMI), infertility duration, serum testosterone levels, progesterone levels at the time of human chorionic gonadotropin (hCG) injection, number of high-quality cleavage-stage embryos, type of embryo transferred, and the total number of embryos transferred. Based on these findings, a predictive nomogram was developed. The area under the ROC curve stood at 0.804 (95% confidence interval (CI), 0.775-0.833) for the modeling group and 0.807 (95% CI, 0.762-0.851) for the validation group. Calibration curves confirmed that the predictions of the nomogram closely matched the actual live birth outcomes. Decision curve analysis highlighted that the model provides significant net benefits for predicting live birth rates, with optimal performance across a probability range of 16.5 to 88.6%.
conclusionIndependent factors, including female age, infertility duration, BMI, serum testosterone levels, progesterone levels on the day of hCG injection, and the number and type of high-quality cleavage-stage embryos transferred are pivotal in influencing live birth outcomes in fresh embryo transfer cycles under antagonist protocols in individuals with PCOS undergoing IVF/ICSI treatments. The predictive nomogram developed from these factors offers substantial predictive accuracy and clinical utility, providing a reliable basis for clinical prognosis, targeted interventions, and the development of personalized treatment plans.
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