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, Zhejiang, China.
Front Endocrinol (Lausanne). 2022 Jul 22;13:946123. doi: 10.3389/fendo.2022.946123. eCollection 2022.
To establish a more convenient ovarian reserve model with anti-Müllerian hormone (AMH) level and age (the AA model), with blood samples taken at any time in the menstrual cycle.
We have established this AA model for predicting ovarian reserve using the AMH level and age. The outcome variable was defined as poor ovarian response (POR) with <5 oocytes retrieved during assisted reproductive technology treatment cycles. Least Absolute Shrinkage and Selection Operator logistic regression with 5-fold cross validation methods was applied to construct the model, and that with the lowest scaled log-likelihood was selected as the final one.
The areas under the receiver operating characteristic curve for the training, inner, and external validation sets were 0.862, 0.843, and 0.854 respectively. The main effects of AMH level and age contributing to the prediction of POR were 95.3% and 1.8%, respectively. The incidences of POR increased with its predicted probability in both the model building and in external validation datasets, indicating its stability. An online website-based tool for assessing the score of ovarian reserve (http://121.43.113.123:9999) has been developed.
Based on external validation data, the AA model performed well in predicting POR, and was more cost-effective and convenient than our previous published models.
建立一个更方便的卵巢储备模型，结合抗苗勒管激素（AMH）水平和年龄（AA 模型），可以在月经周期的任何时间采集血样。
我们使用 AMH 水平和年龄建立了这个预测卵巢储备的 AA 模型。结局变量定义为辅助生殖技术治疗周期中获得的卵母细胞<5 个的卵巢反应不良（POR）。应用 5 倍交叉验证的最小绝对收缩和选择算子逻辑回归方法构建模型，并选择具有最低比例对数似然的模型作为最终模型。
训练集、内部验证集和外部验证集的受试者工作特征曲线下面积分别为 0.862、0.843 和 0.854。对 POR 预测有贡献的 AMH 水平和年龄的主要作用分别为 95.3%和 1.8%。在模型构建和外部验证数据集中，POR 的发生率随着其预测概率的增加而增加，表明其稳定性。已经开发了一个基于在线网站的卵巢储备评分评估工具（http://121.43.113.123:9999）。
基于外部验证数据，AA 模型在预测 POR 方面表现良好，比我们之前发表的模型更具成本效益和便利性。
