Reproductive Medicine Center, Huizhou Municipal Central Hospital, Huizhou, 516001, P.R. China.
Obstetrics and Gynecology, Huizhou Municipal Central Hospital, Xiapu Branch, No. 8 Hengjiang 4Th Road, Huizhou, 516001, P.R. China.
BMC Pregnancy Childbirth. 2023 Jun 27;23(1):476. doi: 10.1186/s12884-023-05775-3.
This study was to conduct prediction models based on parameters before and after the first cycle, respectively, to predict live births in women who received fresh or frozen in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) for the first time.
This retrospective cohort study population consisted of 1,857 women undergoing the IVF cycle from 2019 to 2021 at Huizhou Municipal Central Hospital. The data between 2019 and 2020 were completely randomly divided into a training set and a validation set (8:2). The data from 2021 was used as the testing set, and the bootstrap validation was carried out by extracting 30% of the data for 200 times on the total data set. In the training set, variables are divided into those before the first cycle and after the first cycle. Then, predictive factors before the first cycle and after the first cycle were screened. Based on the predictive factors, four supervised machine learning algorithms were respectively considered to build the predictive models: logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LGBM). The performances of the prediction models were evaluated by the area under the receiver operator characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy.
Totally, 851 women (45.83%) had a live birth. The LGBM model showed a robust performance in predicting live birth before the first cycle, with AUC being 0.678 [95% confidence interval (CI): 0.651 to 0.706] in the training set, 0.612 (95% CI: 0.553 to 0.670) in the validation set, 0.634 (95% CI: 0.511 to 0.758) in the testing set, and 0.670 (95% CI: 0.626 to 0.715) in the bootstrap validation. The AUC value in the training set, validation set, testing set, and bootstrap of LGBM to predict live birth after the first cycle was 0.841 (95% CI: 0.821 to 0.861), 0.816 (95% CI: 0.773 to 0.859), 0.835 (95% CI: 0.743 to 0.926), and 0.839 (95% CI: 0.806 to 0.871), respectively.
The LGBM model based on the predictive factors before and after the first cycle for live birth in women showed a good predictive performance. Therefore, it may assist fertility specialists and patients to adjust the appropriate treatment strategy.
本研究旨在分别基于新鲜或冷冻体外受精（IVF）或胞浆内单精子注射（ICSI）的第一次周期前后的参数建立预测模型，以预测首次接受 IVF 或 ICSI 的女性的活产。
本回顾性队列研究人群包括 2019 年至 2021 年在惠州市中心医院接受 IVF 周期的 1857 名女性。2019 年至 2020 年的数据完全随机分为训练集和验证集（8:2）。2021 年的数据被用作测试集，并通过在总数据集上提取 30%的数据进行 200 次 bootstrap 验证。在训练集中，变量分为第一周期前和第一周期后。然后，筛选第一周期前和第一周期后的预测因素。基于预测因素，分别考虑四种监督机器学习算法：逻辑回归（LR）、随机森林（RF）、极端梯度提升（XGBoost）和轻梯度提升机（LGBM）来构建预测模型。通过受试者工作特征曲线下面积（AUC）、灵敏度、特异性、阳性预测值（PPV）、阴性预测值（NPV）和准确性来评估预测模型的性能。
共有 851 名女性（45.83%）活产。LGBM 模型在预测第一周期前的活产方面表现出稳健的性能，在训练集的 AUC 为 0.678[95%置信区间（CI）：0.651-0.706]，验证集的 AUC 为 0.612（95%CI：0.553-0.670），测试集的 AUC 为 0.634（95%CI：0.511-0.758），bootstrap 验证集的 AUC 为 0.670（95%CI：0.626-0.715）。LGBM 预测第一周期后活产的训练集、验证集、测试集和 bootstrap 的 AUC 值分别为 0.841（95%CI：0.821-0.861）、0.816（95%CI：0.773-0.859）、0.835（95%CI：0.743-0.926）和 0.839（95%CI：0.806-0.871）。
基于第一周期前后预测因素的 LGBM 模型对女性活产的预测表现出良好的预测性能。因此，它可能有助于生育专家和患者调整适当的治疗策略。
