Department of Thoracic Surgery, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, China.
Key Laboratory of Tumor Molecular Diagnosis and Individualized Medicine of Zhejiang Province, Hangzhou, China.
Front Endocrinol (Lausanne). 2022 May 27;13:881983. doi: 10.3389/fendo.2022.881983. eCollection 2022.
Predicting the number of oocytes retrieved (NOR) following controlled ovarian stimulation (COS) is the only way to ensure effective and safe treatment in assisted reproductive technology (ART). To date, there have been limited studies about predicting specific NOR, which hinders the development of individualized treatment in ART.
To establish an online tool for predicting NOR.
In total, 621 prospective routine gonadotropin releasing hormone (GnRH) antagonist COS cycles were studied. Independent variables included age, body mass index, antral follicle counts, basal FSH, basal and increment of anti-mullerian hormone, Luteinizing hormon, estradiol, testosterone, androstenedione, and inhibin B. The outcome variable was NOR. The independent variables underwent appropriate transformation to achieve a better fit for a linear relationship with NOR. Pruned forward selection with holdback validation was then used to establish predictive models. Corrected Akaike's information criterion, Schwarz-Bayesian information criterion, scaled -[likelihood], and the generalized coefficient of determination () were used for model evaluation.
A multiple negative binomial regression model was used for predicting NOR because it fitted a negative binomial distribution. We established Model 1, using basal ovarian reserve markers, and Model 2, using both basal and early dynamic markers for predicting NOR following COS. The generalized values were 0.54 and 0.51 for Model 1 and 0.64 and 0.62 for Model 2 in the training and validation sets, respectively.
Models 1 and 2 could be applied to different scenarios. For directing the starting dose of recombinant follicle stimulation hormone (rFSH), Model 1 using basic predictors could be used prior to COS. Model 2 could be used for directing the adjustment of rFSH dosages during COS. An online tool (http://121.43.113.123:8002/) based on these two models is also developed. We anticipate that the clinical application of this tool could help the ART clinics to reduce iatrogenic ovarian under- or over-responses, and could reduce costs during COS for ART.
预测控制性卵巢刺激（COS）后获得的卵子数量（NOR）是确保辅助生殖技术（ART）中有效和安全治疗的唯一方法。迄今为止，关于预测特定 NOR 的研究有限，这阻碍了 ART 中个体化治疗的发展。
建立预测 NOR 的在线工具。
共研究了 621 例前瞻性常规促性腺激素释放激素（GnRH）拮抗剂 COS 周期。自变量包括年龄、体重指数、窦卵泡计数、基础 FSH、基础和递增的抗苗勒管激素、黄体生成素、雌二醇、睾酮、雄烯二酮和抑制素 B。因变量是 NOR。对自变量进行适当转换，以更好地拟合与 NOR 的线性关系。然后使用修剪前向选择和回溯验证建立预测模型。校正的赤池信息量准则、施瓦茨-贝叶斯信息量准则、标准化似然比和广义确定系数（）用于模型评估。
由于 NOR 符合负二项分布，因此使用多元负二项回归模型进行预测。我们建立了用于预测 COS 后 NOR 的模型 1，使用基础卵巢储备标志物，以及模型 2，使用基础和早期动态标志物。在训练集和验证集中，模型 1 和模型 2 的广义值分别为 0.54 和 0.51，以及 0.64 和 0.62。
模型 1 和模型 2 可应用于不同场景。对于指导重组卵泡刺激素（rFSH）的起始剂量，可在 COS 前使用基于基本预测因子的模型 1。在 COS 期间，可以使用模型 2 来指导 rFSH 剂量的调整。还开发了基于这两个模型的在线工具（http://121.43.113.123:8002/）。我们预计该工具的临床应用可以帮助 ART 诊所减少医源性卵巢过度或反应不足，并降低 ART 期间的 COS 成本。
