Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, China.
Reproductive Medicine Center, Renmin Hospital of Wuhan University, Wuhan, China.
BMC Pregnancy Childbirth. 2022 Nov 4;22(1):810. doi: 10.1186/s12884-022-05152-6.
Selecting an appropriate and personalized Gn starting dose (GSD) is an essential procedure for determining the quality and quantity of oocytes in the controlled ovarian stimulation (COS) process of the in-vitro fertilization (IVF) treatment cycle. The current approach for determining the GSD is mainly based on the experience of a clinician, lacking unified and scientific standards. This study aims to establish a prediction model of GSD, based on which good COS outcomes can be achieved with the influencing factors comprehensively evaluated quantitatively.
We collected a total of 1555 patients undergoing the first oocytes retrieving cycle and conducted correlation analysis to find the significant factors related to the GSD. Two GSD models are built based on two popular machine learning approaches, and the one with better model performance is selected as the final model. Finally, clinical application and validation were conducted to verify the effectiveness of the proposed model.
(1) Age, duration of infertility, type of infertility, body mass index (BMI), antral follicle count (AFC), basal follicle stimulating hormone (bFSH), estradiol (E), luteinizing hormone (LH), anti-Müllerian hormone (AMH) and COS treatment regimen were closely related to the GSD (P < 0.05). (2) The selected model has good modeling performance in terms of both root mean square error (RMSE) (29.87 ~ 34.21) and regression coefficient R (0.947 ~ 0.953). (3) A comprehensive evaluation of influencing factors for GSD is conducted and shows that the top four most significant factors are age, AMH, AFC, and BMI. (4) The proposed GSD can approximate the actual value well in the clinical application, with the mean absolute error of only 11.26 units, and the recommended results can prompt the number of oocytes retrieved (NOR) close to the optimal number.
Modeling the GSD value with machine learning approaches is feasible and effective, and the proposed model has good clinical application for determining the GSD in the IVF treatment cycle.
选择合适的、个性化的 Gn 起始剂量（GSD）是体外受精（IVF）治疗周期控制性卵巢刺激（COS）过程中确定卵母细胞质量和数量的重要程序。目前，确定 GSD 的方法主要基于临床医生的经验，缺乏统一和科学的标准。本研究旨在建立 GSD 的预测模型，在此基础上，通过全面定量评估影响因素，可以获得良好的 COS 结局。
共收集 1555 例行第一次取卵周期的患者，进行相关性分析，寻找与 GSD 相关的显著因素。基于两种流行的机器学习方法建立两种 GSD 模型，选择性能更好的模型作为最终模型。最后，进行临床应用和验证，以验证所提出模型的有效性。
（1）年龄、不孕持续时间、不孕类型、体重指数（BMI）、窦卵泡计数（AFC）、基础卵泡刺激素（bFSH）、雌二醇（E）、黄体生成素（LH）、抗苗勒管激素（AMH）和 COS 治疗方案与 GSD 密切相关（P<0.05）。（2）所选模型在均方根误差（RMSE）（29.8734.21）和回归系数 R（0.9470.953）方面均具有良好的建模性能。（3）对 GSD 的影响因素进行综合评估，结果显示，前四个最重要的因素是年龄、AMH、AFC 和 BMI。（4）在临床应用中，所提出的 GSD 能够很好地逼近实际值，平均绝对误差仅为 11.26 个单位，推荐结果可以提示接近最佳数量的可获取卵母细胞数（NOR）。
使用机器学习方法对 GSD 值进行建模是可行且有效的，所提出的模型在确定 IVF 治疗周期中的 GSD 方面具有良好的临床应用价值。
