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Front Endocrinol (Lausanne). 2026 Aug 12;17:1898690. doi: 10.3389/fendo.2026.1898690. eCollection 2026.
ABSTRACT
PURPOSE: This study aimed to identify key predictors of live birth and to develop a machine learning prediction model for women with unexplained recurrent spontaneous abortion (URSA) undergoing preimplantation genetic testing for aneuploidy (PGT-A).
METHODS: This retrospective study analyzed 127 patients with URSA who underwent PGT-A. Patients underwent frozen-thawed embryo transfer (FET) following either a hormone replacement cycle (HRT) or a down-regulated HRT. Data were collected on clinical baseline characteristics, endometrial thickness, embryo quality, medication use, and pregnancy outcomes. Binary logistic regression was used to assess the correlation between these factors and live birth.
RESULTS: Among the 127 patients with URSA, 79 achieved live births and 48 did not. Through comparative and regression analyses, the study identified key predictive variables, providing a reference for evaluating pregnancy outcomes. There were no significant baseline differences between the live birth and non-live birth groups in terms of age, Anti-Müllerian Hormone (AMH), Body Mass Index (BMI), duration of infertility, thyroid-stimulating hormone (TSH), erythrocyte sedimentation rate (ESR), endometrial thickness (EMT), embryo quality, or post-transfer medication (all P>0.05). Univariate regression analysis showed that β-human chorionic gonadotropin (β-hCG) levels on day 14 post-transfer was significantly positively correlated with live birth (P
CONCLUSIONS: For URSA patients undergoing PGT-A with euploid embryo transfer, day 14 β-hCG levels and embryo quality were key predictors of live birth. Empirical interventions and luteal support type had no significant impact. The random forest model can accurately predict live births, aiding personalized clinical management and minimizing unnecessary treatments.
PMID:42656341 | PMC:PMC13506436 | DOI:10.3389/fendo.2026.1898690