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已发表论文
基于诺模图预测 GnRH 拮抗剂方案新鲜体外受精/卵胞浆内单精子注射周期的活产情况
Authors Zhong Y, Kang Q, Pang X, Wang N
Received 24 March 2025
Accepted for publication 20 September 2025
Published 5 November 2025 Volume 2025:17 Pages 4143—4164
DOI https://doi.org/10.2147/IJWH.S525614
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 4
Editor who approved publication: Dr Vinay Kumar
Yanyu Zhong,* Qian Kang,* Xin Pang,* Nan Wang
Department of Reproductive Medicine Center, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Nan Wang, Department of Reproductive Medicine Center, The First Affiliated Hospital of Soochow University, No. 899, Pinghai Street, Gusu District, Suzhou, People’s Republic of China, Email wang1nan_wn@163.com
Objective: This investigation sought to determine optimal prognostic indicators and develop an implementable predictive framework for estimating live birth probabilities in subfertile individuals receiving gonadotropin-releasing hormone antagonist-based ovarian stimulation during fresh embryo transfer cycles of assisted reproductive technology.
Methods: In this observational cohort analysis, we examined consecutive fresh in vitro fertilization/embryo transfer (IVF/ET) cycles utilizing GnRH antagonist protocols (training = 587, validation = 168 cycles; 2017– 2022). Live birth rate served as the principal outcome measure. Through multivariable regression modeling, we identified key predictive variables and constructed a visual prediction tool. Model robustness was assessed using ROC-AUC metrics and decision curve validation with 500 bootstrap iterations.
Results: The final predictive algorithm incorporated six clinical parameters: serum progesterone on post-ovulation day 9 (serum P (OPU+9) ≥ 51.4 ng/mL), transferred embryo count, progesterone–follicle ratio (PFR), triggering-day progesterone levels, progesterone-to-total follicle ratio, and creatinine concentrations. The training cohort demonstrated moderate discriminative capacity (ROC-AUC 0.72, 95% CI 0.68– 0.76), with enhanced performance in validation samples (AUC 0.81, 95% CI 0.73– 0.89). Decision curve evaluations confirmed the model’s clinical applicability.
Conclusion: Our prognostic scoring chart offers an accessible and practical clinical instrument for estimating reproductive success in GnRH antagonist-based IVF/ICSI cycles. This tool facilitates personalized treatment planning and therapeutic strategy optimization, potentially improving resource allocation in fertility care.
Keywords: reproductive outcomes prediction, IVF prognostic model, ovarian stimulation protocol, progesterone–follicular index, assisted reproduction technology
Department of Reproductive Medicine Center, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Nan Wang, Department of Reproductive Medicine Center, The First Affiliated Hospital of Soochow University, No. 899, Pinghai Street, Gusu District, Suzhou, People’s Republic of China, Email wang1nan_wn@163.com
Objective: This investigation sought to determine optimal prognostic indicators and develop an implementable predictive framework for estimating live birth probabilities in subfertile individuals receiving gonadotropin-releasing hormone antagonist-based ovarian stimulation during fresh embryo transfer cycles of assisted reproductive technology.
Methods: In this observational cohort analysis, we examined consecutive fresh in vitro fertilization/embryo transfer (IVF/ET) cycles utilizing GnRH antagonist protocols (training = 587, validation = 168 cycles; 2017– 2022). Live birth rate served as the principal outcome measure. Through multivariable regression modeling, we identified key predictive variables and constructed a visual prediction tool. Model robustness was assessed using ROC-AUC metrics and decision curve validation with 500 bootstrap iterations.
Results: The final predictive algorithm incorporated six clinical parameters: serum progesterone on post-ovulation day 9 (serum P (OPU+9) ≥ 51.4 ng/mL), transferred embryo count, progesterone–follicle ratio (PFR), triggering-day progesterone levels, progesterone-to-total follicle ratio, and creatinine concentrations. The training cohort demonstrated moderate discriminative capacity (ROC-AUC 0.72, 95% CI 0.68– 0.76), with enhanced performance in validation samples (AUC 0.81, 95% CI 0.73– 0.89). Decision curve evaluations confirmed the model’s clinical applicability.
Conclusion: Our prognostic scoring chart offers an accessible and practical clinical instrument for estimating reproductive success in GnRH antagonist-based IVF/ICSI cycles. This tool facilitates personalized treatment planning and therapeutic strategy optimization, potentially improving resource allocation in fertility care.
Keywords: reproductive outcomes prediction, IVF prognostic model, ovarian stimulation protocol, progesterone–follicular index, assisted reproduction technology