Clinical pharmacology and therapeutics · 2025
Population Time-to-Event Modeling of Pregnancy Loss Risk Following Frozen Embryo Transfer: A Real-World Study in Chinese Women.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Mak Wen Yao, Alifu Muyesaier, Zheng Aole, Jia Ning, He Qingfeng, Zhu Xiao, Mao Xiaoyan, Xiang Xiaoqiang
Аннотация
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.