Prediction of live birth using machine learning based on β-hCG and embryo quality in patients with unexplained...

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).

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.

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<0.001). Multivariate analysis further identified the day 14 β-hCG level post-transfer and transferred embryo quality as key variables. The constructed random forest machine learning model demonstrated strong predictive performance [Area Under the Curve (AUC)=0.917, 95%CI 0.778-1.000]. The day 14 β-hCG level cut-off for predicting live birth was 457.75 mIU/mL, with an optimal range of 828.69-3571.77 mIU/mL. SHAP analysis showed that high-quality embryos increased the chances of live birth, while low-quality embryos decreased it. Different medications or luteal support regimens did not significantly affect live birth rates.

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.