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Population Time‐to‐Event Modeling of Pregnancy Loss Risk Following Frozen Embryo Transfer: A Real‐World Study in Chinese Women

Year: 2025

Type: article

Abstract: Pregnancy loss after frozen embryo transfer (FET) remains an under-recognized complication in assisted reproductive technology (ART), particularly in non-Western settings. Existing prediction models rarely capture evolving hazards over gestation or externally validated. To address this, we developed and externally validated a parametric time-to-event model to predict the dynamic risks of pregnancy loss following FET using a large real-world Chinese cohort. The model incorporated routinely available clinical variables, including maternal demographics, progesterone concentration, endometrium thickness, and endometrium preparation protocols. A Gompertz distribution model captured the time-dependent hazard trajectory. Model performance was externally validated in two temporal cohorts, as well as in older women (≥40 years) and ethnic minority subgroups. Clinical utility was assessed by decision-curve analysis (DCA). Among 21,242 conceptions, 17.6% resulted in miscarriage or stillbirth, with risks peaking in the first trimester. Maternal age showed a nonlinear effect: compared with reference age 32, hazard increased by 67% at age 40 and more than doubled by age 45. Higher weight accelerated risk over time. Protective factors included greater endometrial thickness, higher progesterone levels, and use of mild- (hazard ratio, HR 0.80, 95% CI: 0.753-0862), late- (HR 0.90, 95% CI: 0.798-0.951), or modified-late stimulation (HR 0.80, 95% CI: 0.732-0.863) protocols. DCA indicated that model-guided risk-stratification strategies offered clear net benefit in older women across a broader range of risk threshold. This validated, time-resolved model supports individualized risk-stratification strategies and informed treatment decisions in FET-based ART across diverse clinical settings in China.

Source: Clinical Pharmacology & Therapeutics

DOI: 10.1002/cpt.70174

Authors: Wen Yao Mak, Muyesaier Alifu, Aole Zheng, Ning Jia, Qingfeng He, Xiao Zhu, Xiaoyan Mao, Xiaoqiang Xiang

Institutions: Fudan University, Shanghai Jiao Tong University, Shanghai Ninth People's Hospital

Language: English

Topic: Assisted Reproductive Technology and Twin Pregnancy

Subfield: Pediatrics, Perinatology and Child Health

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: bronze

Funder: China Scholarship Council