[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128285-en":3,"doc-seo-128285-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128285,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136",7,"Healthcare","Machine learning-based prediction of adverse pregnancy outcomes in antiphospholipid syndrome using pregnancy antibody levels","Antiphospholipid syndrome (APS) is an immune-related disorder strongly linked to adverse pregnancy outcomes, including recurrent miscarriage, placental abruption, preterm birth, and fetal growth restriction. Antiphospholipid antibodies (aPLs)—especially anticardiolipin (aCL), anti-β2-glycoprotein I (aβ2GP1), and lupus anticoagulant (LA)—function as key biomarkers, but their predictive value remains inconsistently validated. This prospective observational cohort study builds machine-learning models to predict adverse pregnancy outcomes using early-pregnancy aPL levels and clinical features, assessing discrimination, accuracy, and clinical utility.","TYPE Original Research PUBLISHED 25 August 2025  \nDOI 10.3389/fphys.2025.1617796  \nOPEN ACCESS  \nEDITED BY  \nAhsan H. Khandoker,  \nKhalifa University, United Arab Emirates  \nREVIEWED BY  \nDheiver Francisco Santos, Centro Avancado de Tecnologias Inteligentes, Brazil  \nJunjun Chen,  \nJohns Hopkins University, United States  \n*CORRESPONDENCE  \nShanling Yan,  \n [SLYan_DYHospital@163.com](SLYan_DYHospital@163.com)  \nRECEIVED 23 May 2025  \nACCEPTED 28 July 2025  \nPUBLISHED 25 August 2025  \nCITATION  \nLiu W, Huang J, Xiao J and Yan S (2025) Machine learning-based prediction of adverse pregnancy outcomes in antiphospholipid syndrome using pregnancy antibody levels. Front. Physiol. 16:1617796 .  \ndoi: 10.3389/fphys.2025.1617796  \nCOPYRIGHT  \n© 2025 Liu, Huang, Xiao and Yan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning-based prediction of adverse pregnancy outcomes in antiphospholipid syndrome using pregnancy antibody levels  \nWanqing Liu 1, Ju Huang 1, Jun Xiao 1 and Shanling Yan 􀁋 2*  \n1 Department of Obstetrics and Gynecology, Deyang People’s Hospital, Deyang, Sichuan, China, 2 Department of Ultrasound, Deyang People’s Hospital, Deyang, Sichuan, China  \nBackground: Antiphospholipid syndrome (APS) is a major immune-related disorder that leads to adverse pregnancy outcomes (APO), including recurrent miscarriage, placental abruption, preterm birth, and fetal growth restriction. Antiphospholipid antibodies (aPLs), particularly anticardiolipin antibodies (aCL), anti-β2-glycoprotein I antibodies (aβ2GP1), and lupus anticoagulant (LA), are considered key biomarkers for APS and are closely associated with adverse pregnancy outcomes. This is a prospective observational cohort study to use machine learning model to predict adverse pregnancy outcomes in APS patients using early pregnancy aPL levels and clinical features.  \nMethods: This prospective study began data collection and follow-up for APS patients undergoing pregnancy monitoring in January 2023, and all data collection and follow-up were completed by January 2025 . The samples were divided into theAPO group and non-APO group. Multivariable logistic regression and ridge regression were used to identify independent predictive factors for adverse pregnancy outcomes. Six machine learning models were developed: Light Gradient Boosting Machine (LGBM), CatBoost, Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP) . The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, and F1 score. The best-performing model was further explained using Shapley Additive Explanations (SHAP) analysis. Additionally, decision curve analysis (DCA) was performed to assess the clinical utility of the models.  \nResults: A total of 708 patients were included. Ridge regression analysis identified aβ2GP1, LA1/LA2, aCL, gestational week at termination, age at first miscarriage, age, BMI during pregnancy, use of medication, >3 adverse pregnancies, 1–2 adverse pregnancies, preeclampsia, and natural miscarriage as significant predictors. Among the six models, the XGBoost model performed the best for predicting adverse pregnancy outcomes (AUROC = 0 . 864) . Decision curve analysis (DCA) further confirmed the superiority of the XGBoost model, and feature importance analysis revealed that aβ2GP1 levels were the most important variable among the 12 factors.  \nFrontiers in Physiology 01 [frontiersin.org](frontiersin.org)  \nConclusion: This study demons","cbCairYHi4ueaLab","https://ap.wps.com/l/cbCairYHi4ueaLab","pdf",22846767,2,1,12,"English","en",105,"# Background\n## Study aim\n# Methods\n## Study design and data split\n## Predictive modeling and evaluation\n## Explainability and clinical utility\n# Results\n## Patient cohort and predictors\n## Model performance and feature importance\n# Conclusion\n## Clinical implications","[{\"question\":\"What does the study aim to predict in APS patients?\",\"answer\":\"The study predicts adverse pregnancy outcomes in patients with antiphospholipid syndrome using early-pregnancy antiphospholipid antibody levels and clinical features.\"},{\"question\":\"Which data and models are used in the analysis?\",\"answer\":\"A prospective cohort collected APS pregnancy-monitoring data from January 2023 to January 2025, and six machine-learning models were trained: LGBM, CatBoost, XGBoost, Logistic Regression, Random Forest, and MLP.\"},{\"question\":\"Which model performed best and what was identified as most important?\",\"answer\":\"XGBoost showed the best predictive performance, and feature importance indicated that aβ2GP1 levels were the most important variable among the factors considered.\"}]","Machine learning-based prediction of adverse pregnancy outcomes in antiphospholipid syndrome using pregnancy antibody levels | 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does the study aim to predict in APS patients?","Question",{"text":76,"@type":77},"The study predicts adverse pregnancy outcomes in patients with antiphospholipid syndrome using early-pregnancy antiphospholipid antibody levels and clinical features.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which data and models are used in the analysis?",{"text":81,"@type":77},"A prospective cohort collected APS pregnancy-monitoring data from January 2023 to January 2025, and six machine-learning models were trained: LGBM, CatBoost, XGBoost, Logistic Regression, Random Forest, and MLP.",{"name":83,"@type":74,"acceptedAnswer":84},"Which model performed best and what was identified as most important?",{"text":85,"@type":77},"XGBoost showed the best predictive performance, and feature importance indicated that aβ2GP1 levels were the most important variable among the factors 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