多年来,不孕症仍然是一个重大的全球负担。生殖手术是治疗不孕妇女的有效策略。对于寻求不孕症治疗的患者来说,早期预测生殖手术后的自然妊娠是非常重要的。然而,没有高质量的模型和临床适用的工具来预测生殖手术后自然受孕的概率。
■2016年6月至2021年6月在中国烟台蔚皇顶医院进行手术治疗的1013例不孕症患者的合格数据,被随机分为培训和内部测试队列。来自中国临沂市人民医院的195名受试者被考虑进行外部验证。进行了单变量结合多变量逻辑回归和最小绝对收缩和选择算子(LASSO)算法以识别独立的预测因子。多种常见的机器学习算法,即逻辑回归,决策树,随机森林,支持向量机,k-最近邻,和极端梯度增强,用于构建预测模型。通过在内部和外部验证数据集中评估模型性能来验证最优模型。
■六个临床指标,包括女性年龄,不孕类型,不孕的持续时间,术中诊断,排卵监测,和抗苗勒管激素(AMH)水平,被筛选出来。基于logistic回归模型的优越的临床预测价值,如内部(0.870)和外部(0.880)验证集的接受者工作特征曲线(AUC)下面积所示,我们最终选择它作为最优模型。因此,我们利用它生成了一个基于网络的列线图,用于预测生殖手术后自然妊娠的概率.此外,校正曲线,Hosmer-Lemeshow(H-L)试验,决策曲线分析(DCA)和临床影响曲线分析(CIC)表明,临床净效益和泛化能力,这些都得到了内部和外部验证的证实。
■总的来说,我们开发的第一个在线操作列线图为生殖手术后自然受孕的概率提供了早期和准确的预测,这有助于临床医生和不育夫妇在选择后续受孕模式时做出明智的决定,自然或IVF,进一步提高不孕症治疗的临床实践。
UNASSIGNED: Infertility remains a significant global burden over the years. Reproductive surgery is an effective strategy for infertile women. Early prediction of spontaneous pregnancy after reproductive surgery is of high interest for the patients seeking the infertility treatment. However, there are no high-quality models and clinical applicable tools to predict the probability of natural conception after reproductive surgery.
UNASSIGNED: The eligible data involving 1013 patients who operated for infertility between June 2016 and June 2021 in Yantai Yuhuangding Hospital in China, were randomly divided into training and internal testing cohorts. 195 subjects from the Linyi People\'s Hospital in China were considered for external validation. Both univariate combining with multivariate logistic regression and the least absolute shrinkage and selection operator (LASSO) algorithm were performed to identify independent predictors. Multiple common machine learning algorithms, namely logistic regression, decision tree, random forest, support vector machine, k-nearest neighbor, and extreme gradient boosting, were employed to construct the predictive models. The optimal model was verified by evaluating the model performance in both the internal and external validation datasets.
UNASSIGNED: Six clinical indicators, including female age, infertility type, duration of infertility, intraoperative diagnosis, ovulation monitoring, and anti-Müllerian hormone (AMH) level, were screened out. Based on the logistic regression model\'s superior clinical predictive value, as indicated by the area under the receiver operating characteristic curve (AUC) in both the internal (0.870) and external (0.880) validation sets, we ultimately selected it as the optimal model. Consequently, we utilized it to generate a web-based nomogram for predicting the probability of spontaneous pregnancy after reproductive surgery. Furthermore, the calibration curve, Hosmer-Lemeshow (H-L) test, the decision curve analysis (DCA) and clinical impact curve analysis (CIC) demonstrated that the model has superior calibration degree, clinical net benefit and generalization ability, which were confirmed by both internal and external validations.
UNASSIGNED: Overall, our developed first nomogram with online operation provides an early and accurate prediction for the probability of natural conception after reproductive surgery, which helps clinicians and infertile couples make sensible decision of choosing the mode of subsequent conception, natural or IVF, to further improve the clinical practices of infertility treatment.