Guangxi Reproductive Medical Center, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Reproductive Center, Nanning Maternity and Child Health Hospital, Nanning, Guangxi, China.
Reprod Biol Endocrinol. 2024 Oct 7;22(1):120. 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.
不孕症影响全球六分之一的夫妇，由于卵巢储备减少和卵母细胞质量下降，高龄产妇（AMA）带来了独特的挑战。单囊胚玻璃化冷冻-解冻移植（SVBT）在辅助生殖技术（ART）中显示出了前景，但在 AMA 患者中的成功率仍然不理想。本研究旨在确定和细化 AMA 患者接受 SVBT 后活产的预测因素，旨在增强临床决策能力并实现个性化治疗策略。
本回顾性队列研究分析了 2016 年 6 月至 2022 年 12 月在广西医科大学第一附属医院和南宁市妇幼保健院进行的 1168 例 SVBT 周期。应用 19 种机器学习模型来确定活产的关键预测因素。采用特征选择和 10 倍交叉验证对模型进行验证。
活产的最重要预测因素包括内细胞团质量、滋养层质量、获卵数、子宫内膜厚度和第 3 天的 8 细胞胚胎。堆叠模型表现出最佳的预测性能（AUC：0.791），其次是 Extra Trees（AUC：0.784）和随机森林（AUC：0.768）。这些模型优于传统方法，实现了更高的准确性、敏感性和特异性。
利用先进的机器学习模型和确定关键预测因素可以提高 AMA 患者接受 SVBT 后活产结果预测的准确性。这些发现为增强临床决策和管理患者期望提供了有价值的见解。需要进一步的研究来在更大的、多中心队列中验证这些结果，并探索额外的因素，包括新鲜胚胎移植，以拓宽这些模型在临床实践中的适用性。
