ArticleFrontiers in medicine2025
Building a predictive model for pregnancy outcomes in ART patients based on AMH, FORT, HCG day EMT, and clinical characteristics.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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 patients did not achieve pregnancy, accounting for 54.50%, and were included in the non-pregnancy group. 91 patients achieved clinical pregnancy, accounting for 45.50%, and were included in the clinical pregnancy group. There were significant statistical differences between the non-pregnant group and the clinically pregnant group in terms of age, body mass index, number of oocytes retrieved, HCG day EMT, AMH, and FORT ( Conclusion: There are numerous factors influencing pregnancy outcomes in ART patients, primarily including age, body mass index, number of oocytes retrieved, HCG day EMT, AMH, and FORT. The risk prediction nomogram model constructed based on these factors has certain predictive value for pregnancy outcomes in ART patients. Clinicians should identify high-risk populations early and optimize treatment plans accordingly.
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