Background and objective

Frozen-thawed embryo transfer (FET) is now widely used for the treatment of infertility. For many couples and clinicians, concerns over the probability and how to increase the chance of a successful birth are very common. Currently, there is not a single model to predict the live birth outcomes for FET. To estimate the probability of live birth (PLB) in FET and to provide advice on potential treatment options by a data-driven predictive (DDP) model.

Methods

2,189 FET cycles from Jan 2012 to Dec 2015 were recruited in a single center. 815 cycles of FET outcomes were live births and 1,374 cycles of FET outcomes failed. To verify the consistency of the DDP model, we carried out 10-fold cross-validation, and the mean and standard deviation of the accuracy were measured. Moreover, the performance of this model was evaluated by the mean and standard deviation of receiver operating characteristic curve and area under the curve (AUC).

Results

Nine dominant factors, including age, BMI, HOMA-IR, basal follicle stimulating hormone, basal luteinizing hormone, basal estradiol, endometrial thickness, the number of embryo transfers and the total number of embryos, were automatically extracted from 28 candidate factors. The accuracy of our prediction model is 76.9%±1.6%, and the AUC is 0.83. Then, the PLB is estimated by the random forest algorithm. On this basis, the DDP model can comprehensively traverse and dynamically visualize the impact of several factors on live birth outcomes. Finally, optimal suggestions for the treatment of patients before FET are attempted to be made by the genetic algorithm.

Conclusion

The DDP model can not only provide satisfactory performance for predicting live birth outcomes in FET but also offer a visual estimation and simulation tool for clinicians to make treatment plans.

中文翻译：

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预测和提高冷冻解冻胚胎移植妇女活产的可能性：数据驱动的估计和模拟模型

背景和目标

冻融胚胎移植（FET）现在被广泛用于治疗不育症。对于许多夫妇和临床医生而言，对概率以及如何增加成功分娩机会的关注非常普遍。当前，还没有单一的模型来预测FET的活产结果。通过数据驱动的预测（DDP）模型来估计FET中活产的可能性（PLB），并为潜在的治疗选择提供建议。

方法

从2012年1月至2015年12月，在一个中心招募了2189个FET周期。FET结局有815个周期是活产，而FET结局有1,374个周期失败。为了验证DDP模型的一致性，我们进行了10倍交叉验证，并测量了准确性的均值和标准差。此外，该模型的性能通过接收器工作特性曲线和曲线下面积（AUC）的平均值和标准偏差进行评估。

结果

从28个候选因子中自动提取了9个主要因子，包括年龄，BMI，HOMA-IR，基底卵泡刺激激素，基底黄体生成激素，基底雌二醇，子宫内膜厚度，胚胎移植次数和胚胎总数。我们的预测模型的准确性为76.9％±1.6％，AUC为0.83。然后，通过随机森林算法估计PLB。在此基础上，DDP模型可以全面遍历并动态可视化几个因素对活产结局的影响。最后，尝试通过遗传算法对FET进行治疗的最佳建议。

结论

DDP模型不仅可以为FET预测活产结果提供令人满意的性能，而且还为临床医生制定治疗计划提供了可视化的评估和模拟工具。