System Maintenance Notice

Please note that maintenance work is scheduled between 4 AM and 5 AM - CET. During this time, you may experience reduced performance. Thanks for your patience.

Publications

English Abstract Journal Article Ppublish

PubMed: 39051087 DOI: 10.12122/j.issn.1673-4254.2024.07.21

[Establishment of a predictive nomogram for clinical pregnancy rate in patients with endometriosis undergoing fresh embryo transfer].

Nan Fang Yi Ke Da Xue Xue Bao 44(7):1407-1415

Authors: S Pan, Y Li, Z Wu, Y Mao, C Wang

Affiliations

  • Department of Obstetrics and Gynecology, Center for Reproductive Medicine, Guangdong Provincial Key Laboratory of Major Obstetric Diseases; Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology; Guangdong-Hong Kong-Macao Greater Bay Area Higher Education Joint Laboratory of Maternal-Fetal Medicine; Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China. Clinical Medical College, Guangzhou Medical University, Guangzhou 511436, China.
  • Department of Obstetrics and Gynecology, Center for Reproductive Medicine, Guangdong Provincial Key Laboratory of Major Obstetric Diseases; Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology; Guangdong-Hong Kong-Macao Greater Bay Area Higher Education Joint Laboratory of Maternal-Fetal Medicine; Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China.

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% : 0.605-0.679) in the training dataset and 0.652 (95% : 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% : 0.680-0.770) in the training dataset and 0.718 (95% : 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.

MeSH

Regression Analysis, Endometriosis, Pregnancy, Reproduction, Nomograms, Infertility, Embryo Transfer, Learning, Pregnancy Rate