Establishment of a predictive nomogram for clinical pregnancy rate in patients with endometriosis undergoing fresh embryo transfer
10.12122/j.issn.1673-4254.2024.07.21
- VernacularTitle: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
- Author:
Shenhao PAN
1
,
2
;
Yankun LI
;
Zhewei WU
;
Yuling MAO
;
Chunyan WANG
Author Information
1. 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
2. 广州医科大学临床医学系,广东 广州 511436
- Keywords:
endometriosis;
infertility;
clinical pregnancy rate;
predictive model;
nomogram
- From:
Journal of Southern Medical University
2024;44(7):1407-1415
- CountryChina
- Language:Chinese
-
Abstract:
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.
