Ortiz José A, Lledó B, Morales R, Máñez-Grau A, Cascales A, Rodríguez-Arnedo A, Castillo Juan C, Bernabeu A, Bernabeu R
Instituto Bernabeu, Molecular Biology Department, Alicante, Spain.
Instituto Bernabeu, Reproductive Biology, Alicante, Spain.
Reprod Biol Endocrinol. 2024 Aug 8;22(1):101. doi: 10.1186/s12958-024-01271-1.
To determine the factors influencing the likelihood of biochemical pregnancy loss (BPL) after transfer of a euploid embryo from preimplantation genetic testing for aneuploidy (PGT-A) cycles.
The study employed an observational, retrospective cohort design, encompassing 6020 embryos from 2879 PGT-A cycles conducted between February 2013 and September 2021. Trophectoderm biopsies in day 5 (D5) or day 6 (D6) blastocysts were analyzed by next generation sequencing (NGS). Only single embryo transfers (SET) were considered, totaling 1161 transfers. Of these, 49.9% resulted in positive pregnancy tests, with 18.3% experiencing BPL. To establish a predictive model for BPL, both classical statistical methods and five different supervised classification machine learning algorithms were used. A total of forty-seven factors were incorporated as predictor variables in the machine learning models.
Throughout the optimization process for each model, various performance metrics were computed. Random Forest model emerged as the best model, boasting the highest area under the ROC curve (AUC) value of 0.913, alongside an accuracy of 0.830, positive predictive value of 0.857, and negative predictive value of 0.807. For the selected model, SHAP (SHapley Additive exPlanations) values were determined for each of the variables to establish which had the best predictive ability. Notably, variables pertaining to embryo biopsy demonstrated the greatest predictive capacity, followed by factors associated with ovarian stimulation (COS), maternal age, and paternal age.
The Random Forest model had a higher predictive power for identifying BPL occurrences in PGT-A cycles. Specifically, variables associated with the embryo biopsy procedure (biopsy day, number of biopsied embryos, and number of biopsied cells) and ovarian stimulation (number of oocytes retrieved and duration of stimulation), exhibited the strongest predictive power.
确定影响非整倍体植入前基因检测（PGT-A）周期中整倍体胚胎移植后生化妊娠丢失（BPL）可能性的因素。
本研究采用观察性回顾性队列设计，纳入了2013年2月至2021年9月期间进行的2879个PGT-A周期中的6020个胚胎。对第5天（D5）或第6天（D6）囊胚的滋养外胚层活检样本进行二代测序（NGS）分析。仅考虑单胚胎移植（SET），共计1161次移植。其中，49.9%的妊娠试验呈阳性，18.3%发生了BPL。为建立BPL的预测模型，使用了经典统计方法和五种不同的监督分类机器学习算法。共有47个因素作为预测变量纳入机器学习模型。
在每个模型的优化过程中，计算了各种性能指标。随机森林模型成为最佳模型，其ROC曲线下面积（AUC）值最高，为0.913，准确率为0.830，阳性预测值为0.857，阴性预测值为0.807。对于所选模型，确定了每个变量的SHAP（SHapley加性解释）值，以确定哪些变量具有最佳预测能力。值得注意的是，与胚胎活检相关的变量显示出最大的预测能力，其次是与卵巢刺激（COS）、母亲年龄和父亲年龄相关的因素。
随机森林模型在识别PGT-A周期中BPL发生情况方面具有更高的预测能力。具体而言，与胚胎活检程序（活检日、活检胚胎数量和活检细胞数量）和卵巢刺激（获卵数和刺激持续时间）相关的变量表现出最强的预测能力。
