P-603 multivariate analysis and prediction model construction of fresh cycle embryo transfer live birth in patients with polycystic ovary syndrome

Abstract Study question To analyze the factors affecting the live birth rate of fresh embryo transfer (ET) in patients with PCOS and to construct a nomogram prediction model. Summary answer The nomogram prediction model can predict the live birth rate of fresh ET in PCOS patients. What is known already At present, there are many predictive models for assisted reproduction in infertile women. In order to improve the live birth rate of fresh embryo transfer cycles in PCOS patients and optimize treatment plans, it is necessary to conduct in-depth analysis of the factors that affect the live birth rate of fresh embryo transfer cycles in PCOS patients. Constructing a Nomogram prediction model can predict the live birth outcomes of PCOS patients, which can help improve assisted reproductive treatment decisions and increase the chances of successful live birth. Study design, size, duration Univariate logistic regression analysis was performed with the live birth of patients as the dependent variable.Variables with P < 0.05 in the univariate analysis were included in the multivariate logistic regression model. Backward stepwise regression was used for variable selection, and a nomogram prediction model was constructed using independent risk factors related to live birth after transfer. The discrimination and calibration of the nomogram prediction model were evaluated using the receiver operating characteristic curve, calibration curve. Participants/materials, setting, methods A retrospective analysis was conducted on the clinical data of 1920 PCOS patients who underwent in vitro fertilization (IVF) treatment and fresh ET after controlled ovarian stimulation (COS) with a long-acting long protocol/antagonist protocol at the Reproductive Health Hospital of the Third Affiliated Hospital of Zhengzhou University from January 2016 to December 2022. Patients were randomly divided into a modeling group (1344 cases) and a validation group (576 cases) in a 7:3 ratio. Main results and the role of chance Univariate logistic regression analysis showed that the duration of infertility, baseline androgen levels, COS protocol, gonadotropin (Gn) starting dose, total days of Gn, endometrial thickness on the trigger day, high-quality embryo rate, number of embryos transferred, and human chorionic gonadotropin (HCG) levels 14 days after transfer were significantly associated with live birth (P < 0.05). Multivariate backward stepwise logistic regression showed that the COS protocol, endometrial thickness on the trigger day, number of embryos transferred, and HCG levels 14 days after transfer were independent factors related to live birth. A nomogram prediction model was constructed with these independent factors. The area under the ROC curve (AUC) for the modeling and validation groups were 0.884 (95% CI: 0.865-0.903) and 0.867 (95% CI: 0.835-0.898), respectively. The calibration curve showed good consistency between the nomogram model’s predicted live birth rate and the actual incidence rate. The decision curve indicated that the nomogram model has good application value for clinical decision-making. Limitations, reasons for caution The Nomotu prediction model has only undergone internal validation. Wider implications of the findings The nomogram prediction model constructed based on these factors can predict the live birth rate of fresh ET in PCOS patients and can serve as an effective auxiliary tool for guiding clinical decision-making. Trial registration number No