Building a predictive model for pregnancy outcomes in ART patients based on AMH, FORT, HCG day EMT, and clinical characteristics

作者：Lei Zhang, Xiaofang Liu, Jiaojiao Bai, Yushan Wang · 发表于：Frontiers in Medicine · 年份：2025 · DOI：10.3389/fmed.2025.1699477 · 被引用次数：2 · 研究领域：Pregnancy and Medication Impact、Ovarian function and disorders、Assisted Reproductive Technology and Twin Pregnancy

Objective To establish a predictive model for pregnancy outcomes in patients undergoing assisted reproductive technology (ART) treatment using human chorionic gonadotropin (HCG) day endometrial thickness (EMT), follicle output rate (FORT), serum anti-Müllerian hormone (AMH), and clinical characteristics. Methods A retrospective study was conducted on 200 patients who underwent ART treatment at our hospital from August 2019 to August 2024. All patient data were obtained from the electronic medical record system, and patients were divided into a clinical pregnancy group and a non-pregnancy group based on whether they achieved pregnancy. Clinical-related data were collected and compared. Two machine learning methods, Lasso regression and Extreme Gradient Boosting (XGBoost), were used for “overlap coverage” screening of risk factors. The XGBoost model was implemented using the “xgboost” package in R, with the following hyperparameters: max_depth = 6, learning_rate = 0.3, n_estimators = 100, objective = “binary:logistic.” These parameters were selected based on common practices for binary classification tasks and were not extensively tuned due to the sample size limitations. Multivariate logistic analysis was performed to identify risk factors for pregnancy outcomes in ART patients. A regression model was established using R software for prediction and validation. Results A total of 200 ART patients were included in this study. According to medical record system records, 109 patie...