Optimal range of serum progesterone on endometrial transformation day predicts clinical pregnancy in down-regulated HRT-FET cycles: a retrospective cohort study
Abstract
Background The predictive value of serum progesterone on the day of endometrial transformation in hormone replacement therapy-frozen embryo transfer (HRT-FET) cycles remains unclear, and its effect may vary by protocol. This study aimed to evaluate its impact and compare it between standard and down-regulated HRT protocols.Methods This retrospective study of 1,073 cycles defined a progesterone cutoff of 0.56 ng/mL via ROC analysis. Cycles were categorized into low (< 0.56 ng/mL) and high (≥ 0.56 ng/mL) subgroups, with multivariate logistic regression identifying predictors of clinical pregnancy. A secondary three-tier stratification (< 0.56, 0.56–1.0, ≥ 1.0 ng/mL) was performed to define the optimal therapeutic window.Results Clinical pregnancy rates did not differ significantly between progesterone subgroups in the overall cohort or the standard HRT group. However, in down-regulated HRT cycles, the high progesterone level (≥ 0.56 ng/mL) was associated with higher clinical pregnancy rate (62.4% vs. 48.9%, P = 0.012) and was an independent predictor of clinical pregnancy exclusively in down-regulated cycles (OR = 3.682, 95% CI: 1.575–8.612, P = 0.003). Three-tier analysis showed a non-linear relationship, with rates lowest at < 0.56 ng/mL (48.92%), peaking at 0.56–1.0 ng/mL (63.55%), and declining at ≥ 1.0 ng/mL (55.56%, P = 0.035), identifying 0.56–1.0 ng/mL as the optimal range.Conclusion The predictive value of serum progesterone is protocol-specific. In down-regulated HRT-FET cycles, a level of 0.56–1.0 ng/mL on transformation day is optimal, suggesting routine monitoring may guide individualized luteal support.
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Citation Information
@article{jingshufu2026,
title={Optimal range of serum progesterone on endometrial transformation day predicts clinical pregnancy in down-regulated HRT-FET cycles: a retrospective cohort study},
author={Jingshu Fu and Xiaoling Gu and Minyan Yu and Lingna Peng and Jian Song and Xiaoli Sun and Qingxin Wang},
journal={Research Square},
year={2026},
doi={https://doi.org/10.21203/rs.3.rs-9423110/v1}
}
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