Reproductive Medical Center, Renmin Hospital of Wuhan University and Hubei Clinic Research Center for Assisted Reproductive Technology and Embryonic Development, Wuhan 430060, China.
Department of Orthopedics, Renmin Hospital of Wuhan University, Wuhan 430060, China.
Aging (Albany NY). 2021 May 17;13(13):17137-17154. doi: 10.18632/aging.203032.
The prediction of poor ovarian response (POR) for stratified interference is a critical clinical issue that has received an increasing amount of recent concern. Anthropogenic diagnostic modes remain too simple for the handling of actual clinical complexity. Therefore, this study conducted extensive selection using models that were derived from a variety of machine learning algorithms, including random forest (RF), decision trees, eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), and artificial neural networks (ANN) for the development of two models called the COS pre-launch model (CPLM) and the hCG pre-trigger model (HPTM) to assess POR based on different requirements. The results demonstrated that CPLM constructed using ANN achieved the highest AUC result of all the algorithms in COS pre-launch (AUC=0.859, C-index=0.87, good calibration), and HPTL constructed using random forest was found to be the most effective in hCG pre-trigger (AUC=0.903, C-index=0.90, good calibration). It is notable that CPLM and HPTM exhibited better performance than common clinical characteristics (0.895 [CPLM], and 0.903 [HPTM] in comparison to 0.824 [anti-Müllerian hormone (AMH)], and 0.799 [antral follicle count (AFC)]). Furthermore, variable importance figure elucidated the values of AMH, AFC, and E level and follicle number on hCG day, which provides important theoretical guidance and experimental data for further application. Generally, the CPLM and HPTM can offer effective POR prediction for patients who are receiving assisted reproduction technology (ART), and has great potential for guiding the clinical treatment of infertility.
预测卵巢低反应（POR）对于分层干预是一个关键的临床问题，最近受到了越来越多的关注。人为的诊断模式对于处理实际的临床复杂性来说仍然过于简单。因此，本研究使用了各种机器学习算法（包括随机森林（RF）、决策树、极端梯度提升（XGBoost）、支持向量机（SVM）和人工神经网络（ANN））进行了广泛的选择，为两种模型开发了两种模型，称为 COS 预启动模型（CPLM）和 hCG 预触发模型（HPTM），根据不同的需求评估 POR。结果表明，基于 ANN 构建的 CPLM 在 COS 预启动时实现了所有算法中最高的 AUC 结果（AUC=0.859，C 指数=0.87，良好校准），而基于随机森林构建的 HPTL 在 hCG 预触发时是最有效的（AUC=0.903，C 指数=0.90，良好校准）。值得注意的是，CPLM 和 HPTM 的表现优于常见的临床特征（CPLM 为 0.895，HPTM 为 0.903，而抗苗勒管激素（AMH）为 0.824，窦卵泡计数（AFC）为 0.799）。此外，变量重要性图阐明了 AMH、AFC 和 E 水平以及 hCG 日的卵泡数的重要性，为进一步应用提供了重要的理论指导和实验数据。一般来说，CPLM 和 HPTM 可以为接受辅助生殖技术（ART）的患者提供有效的 POR 预测，具有指导不孕临床治疗的巨大潜力。
