子宫内膜异位症患者新鲜胚胎移植临床妊娠率预测模型的建立与验证OA北大核心CSTPCDMEDLINE
Establishment of a predictive nomogram for clinical pregnancy rate in patients with endometriosis undergoing fresh embryo transfer
目的 构建并验证子宫内膜异位症(EMs)患者辅助生殖治疗(ART)中新鲜胚胎移植的临床妊娠率预测模型.方法 选取2017年5月～2023年11月在本院生殖医学中心进行ART治疗的464例子宫内膜异位症不孕患者,并将其分为建模人群(60%)和验证人群(40%).采用单因素分析、多因素Logistic回归分析、LASSO回归分析EMs患者新鲜胚胎移植妊娠率的相关因素,并建立预测EMs患者新鲜胚胎移植临床妊娠率的列线图模型.采用ROC的曲线下面积(AUC)、校准曲线和决策曲线分别在建模人群和验证人群对预测模型进行验证.为提高模型性能,本研究采用Stacking集成学习方法集成GBM、XGBOOST、MLP 3种机器学习方法,利用它们各自优势相互补充以提高模型预测性能.结果 女性年龄、Gn启动用量、ART次数、移植胚胎数是影响新鲜胚胎移植临床妊娠率的独立因素(P<0.05).通过LASSO模型筛选纳入变量:女性年龄、FSH、Gn时间、Gn启动用量、ART次数、获卵数、移植胚胎数、HCG日内膜厚度、HCG日P.训练集中,模型准确性为0.642(95%CI:0.605-0.679),测试集中,模型准确性为0.652(95%CI:0.600-0.704).集成学习方法可以提高模型的性能:训练集中,模型准确性为0.725(95%CI:0.680-0.770),测试集中,模型准确性为0.718(95%CI:0.675-0.761).结论 本研究所建立预测模型有助于预测子宫内膜异位症患者新鲜胚胎移植的临床妊娠率,为子宫内膜异位症ART治疗提供指导意见.
Objective To establish a nomogram model for predicting clinical pregnancy rate in patients with endometriosis undergoing fresh embryo transfer.Methods We retrospectively collected the data of 464 endometriosis patients undergoing fresh embryo transfer,who were randomly divided into a training dataset(60%)and a testing dataset(40%).Using univariate analysis,multiple logistic regression analysis,and LASSO regression analysis,we identified the factors associated with the fresh transplantation pregnancy rate in these patients and developed a nomogram model for predicting the clinical pregnancy rate following fresh embryo transfer.We employed an integrated learning approach that combined GBM,XGBOOST,and MLP algorithms for optimization of the model performance through parameter adjustments.Results The clinical pregnancy rate following fresh embryo transfer was significantly influenced by female age,Gn initiation dose,number of assisted reproduction cycles,and number of embryos transferred.The variables included in the LASSO model selection included female age,FSH levels,duration and initial dose of Gn usage,number of assisted reproduction cycles,retrieved oocytes,embryos transferred,endometrial thickness on HCG day,and progesterone level on HCG day.The nomogram demonstrated an accuracy of 0.642(95%CI:0.605-0.679)in the training dataset and 0.652(95%CI:0.600-0.704)in the validation dataset.The predictive ability of the model was further improved using ensemble learning methods and achieved predicative accuracies of 0.725(95%CI:0.680-0.770)in the training dataset and 0.718(95%CI:0.675-0.761)in the validation dataset.Conclusions The established prediction model in this study can help in prediction of clinical pregnancy rates following fresh embryo transfer in patients with endometriosis.
潘甚豪;李炎坤;伍哲维;毛玉玲;王春艳
广州医科大学附属第三医院妇产科//生殖医学中心//广东省产科重大疾病重点实验室//广东省妇产疾病临床医学研究中心//粤港澳母胎医学高校联合实验室,广东 广州 510150||广州医科大学临床医学系,广东 广州 511436广州医科大学附属第三医院妇产科//生殖医学中心//广东省产科重大疾病重点实验室//广东省妇产疾病临床医学研究中心//粤港澳母胎医学高校联合实验室,广东 广州 510150
子宫内膜异位症不孕临床妊娠率预测模型列线图
endometriosisinfertilityclinical pregnancy ratepredictive modelnomogram
《南方医科大学学报》 2024 (007)
1407-1415 / 9
国家自然科学基金(82101672,82371650);广州市科技局基础研究计划(2023A04J0578,2023A04J0579,2024A03J0180);广州医科大学科研提升计划(2024SRP114) Supported by National Natural Science Foundation of China(82101672,82371650).
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