[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124289-en":3,"doc-seo-124289-105":30,"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124289,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine learning algorithms to predict epidural-related maternal fever - a retrospective study","Epidural-related maternal fever (ERMF) after patient-controlled epidural analgesia (PCEA) is difficult to anticipate, creating uncertainty for obstetric management. This retrospective study uses real-world data from women receiving PCEA in a tertiary hospital in Jiangsu to build six machine learning prediction models. The primary endpoint is maternal fever associated with epidural use, evaluated via AUC, calibration, and decision-curve analyses. Logistic regression provided better calibration, leading to a nomogram that highlights eight clinical predictors.","TYPE Original Research PUBLISHED 11 June 2025  \nDOI 10.3389/fphar.2025.1614770  \nOPEN ACCESS  \nEDITED BY  \nKarel Allegaert,  \nKU Leuven, Belgium  \nREVIEWED BY  \nFatemeh Darsareh,  \nHormozgan University of Medical Sciences, Iran Sulaiman Salim Said Al Mashrafi,  \nRMIT University, Australia  \n*CORRESPONDENCE  \nHaixia Zhang,  \n [zhx_510@hotmail.com](zhx_510@hotmail.com)[ ](zhx_510@hotmail.com)Hongliang Mei,  \n [liulidemao@126.com](liulidemao@126.com)  \nRECEIVED 19 April 2025  \nACCEPTED 19 May 2025  \nPUBLISHED 11 June 2025  \nCITATION  \nGuo X, Zhang H and Mei H (2025) Machine learning algorithms to predict epidural-related maternal fever: a retrospective study.  \nFront. Pharmacol. 16:1614770 .  \ndoi: 10.3389/fphar.2025.1614770  \nCOPYRIGHT  \n© 2025 Guo, Zhang and Mei. This is an openaccess 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 algorithms to predict epidural-related maternal fever: a retrospective study  \nXiaohui Guo 1,2,3, Haixia Zhang 1,3,4* and Hongliang Mei 1,3,4*  \n1Department of Pharmacy, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China, 2Department of Pharmacy, Hainan Women and Children ’s Medical Center, Haikou, Hainan, China, 3China Hospital Reform and Development Research Institute of Nanjing University, Nanjing Drum Tower Hospital, Nanjing, China, 4Nanjing Medical Center for Clinical Pharmacy, Nanjing, Jiangsu, China  \nIntroduction: The epidural-related maternal fever (ERMF) induced by patientcontrolled epidural analgesia (PCEA) remains unpredictable. Our objective is to develop ERMF prediction models using real-world data, aiming to identify pertinent contributing factors and support obstetricians in making personalized clinical decisions.  \nMethods: Women who used patient-controlled epidural analgesia between October 2021 and March 2023 at a tertiary hospital in Jiangsu Province were retrospectively documented. The primary outcome was the occurrence of maternal fever associated with epidural use. We developed six machine learning (ML) models and assessed the area under curve (AUC) for characteristics of subjects’ performance, calibration curves, and decision curve analyses.  \nResults: A total of 1,492 women were enrolled, with 24 .3% experiencing ERMF (362 cases) . The AUC ratios between the logistic regression (LR) model and the stochastic gradient descent (SGD) models showed statistical significance (p \u003C 0.05), while the differences between the other models were not statistically significant. In comparison to the SVM model, the LR model exhibited better calibration (Brier score: 0.193; calibration slope: 0.715; calibration intercept: 0. 062) . Consequently, the LR model was selected as the prediction model. Furthermore, the LR-based nomogram identified eight significant predictors of ERMF, including neutrophil percentage, first stage of labor, amniotic fluid contamination during membrane rupture, artificial rupture of membranes, chorioamnionitis, post-analgesic antimicrobials, pre-analgesic oxytocin, postanalgesic oxytocin, and dinoprostone suppositories.  \nConclusion: Optimally applying logistic regression models can enable rapid and straightforward identification of ERMF risk and the implementation of rational therapeutic measures, in contrast to machine learning models.  \nKEYWORDS  \nepidural-related maternal fever, machine learning, predictive model, Nomograms, risk assessment  \nFrontiers in Pharmacology 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nEpidural analgesia (EA) has gained widespread acceptance, chosen by a significant proportion of women in l","cbCaiecMl3hXg5vu","https://ap.wps.com/l/cbCaiecMl3hXg5vu","pdf",1315165,1,10,"English","en",105,"# Introduction\n# Methods\n# Results\n# Conclusion","[{\"question\":\"What is the main goal of this retrospective study?\",\"answer\":\"To develop prediction models for epidural-related maternal fever (ERMF) using real-world data and to support personalized obstetric decisions.\"},{\"question\":\"How is the ERMF outcome defined and analyzed?\",\"answer\":\"The primary outcome is the occurrence of maternal fever associated with epidural use among women who received PCEA between October 2021 and March 2023. Model performance is assessed using AUC, calibration curves, and decision curve analyses.\"},{\"question\":\"Which model performed best, and what does it produce?\",\"answer\":\"Logistic regression showed better calibration than other evaluated machine learning models and was selected as the prediction model. A nomogram based on it identifies eight significant predictors of ERMF.\"}]","Machine learning algorithms to predict epidural-related maternal fever - a retrospective study | PDF",1785821391,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-algorithms-to-predict-epidural-related-maternal-fever-a-retrospective-study","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-algorithms-to-predict-epidural-related-maternal-fever-a-retrospective-study/124289/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main goal of this retrospective study?","Question",{"text":76,"@type":77},"To develop prediction models for epidural-related maternal fever (ERMF) using real-world data and to support personalized obstetric decisions.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the ERMF outcome defined and analyzed?",{"text":81,"@type":77},"The primary outcome is the occurrence of maternal fever associated with epidural use among women who received PCEA between October 2021 and March 2023. Model performance is assessed using AUC, calibration curves, and decision curve analyses.",{"name":83,"@type":74,"acceptedAnswer":84},"Which model performed best, and what does it produce?",{"text":85,"@type":77},"Logistic regression showed better calibration than other evaluated machine learning models and was selected as the prediction model. 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