Identifying key predictive features for live birth rate in advanced maternal age patients undergoing single vitrified-warmed blastocyst transfer

• Published in: Reproductive biology and endocrinology 2024-10, Vol.22 (1), p.120-12, Article 120
• Main Authors: Liu, Lidan, Liu, Bo, Liao, Ming, Gan, Qiuying, Huang, Qianyi, Yang, Yihua
• Format: Article
• Language: English
• Subjects: Adult; Advanced maternal age (AMA); Assisted Reproductive Technology (ART); Birth Rate; Cryopreservation - methods; Decision-making; Embryo Transfer - methods; Female; Health aspects; Humans; Infertility; Live Birth - epidemiology; Live birth rate; Machine Learning; Maternal Age; Medical colleges; Medical research; Medical societies; Medicine, Experimental; Methods; Pharmaceutical industry; Predictive modeling; Pregnancy; Pregnancy Rate; Prevention; Reproductive technology; Retrospective Studies; Risk factors; Single vitrified-warmed blastocyst transfer (SVBT); Vitrification
• ISSN: 1477-7827; 1477-7827
• DOI: 10.1186/s12958-024-01295-7

Infertility affects one in six couples worldwide, with advanced maternal age (AMA) posing unique challenges due to diminished ovarian reserve and reduced oocyte quality. Single vitrified-warmed blastocyst transfer (SVBT) has shown promise in assisted reproductive technology (ART), but success rates in AMA patients remain suboptimal. This study aimed to identify and refine predictive factors for live birth following SVBT in AMA patients, with the goal of enhancing clinical decision-making and enabling personalized treatment strategies. This retrospective cohort study analyzed 1,168 SVBT cycles conducted between June 2016 and December 2022 at the First Affiliated Hospital of Guangxi Medical University and Nanning Maternity and Child Health Hospital. Nineteen machine-learning models were applied to identify key predictive factors for live birth. Feature selection and 10-fold cross-validation were employed to validate the models. The most significant predictors of live birth included inner cell mass quality, trophectoderm quality, number of oocytes retrieved, endometrial thickness, and the presence of 8-cell blastomeres on day 3. The stacking model demonstrated the best predictive performance (AUC: 0.791), followed by Extra Trees (AUC: 0.784) and Random Forest (AUC: 0.768). These models outperformed traditional methods, achieving superior accuracy, sensitivity, and specificity. Leveraging advanced machine-learning models and identifying critical predictive factors can improve the accuracy of live birth outcome predictions for AMA patients undergoing SVBT. These findings offer valuable insights for enhancing clinical decision-making and managing patient expectations. Further research is needed to validate these results in larger, multi-center cohorts and to explore additional factors, including fresh embryo transfers, to broaden the applicability of these models in clinical practice.