[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117029-en":3,"doc-seo-117029-105":29,"detail-sidebar-cat-0-en-105":90},{"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":4,"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":13,"seo_description":14,"update_tm":27,"read_time":28},117029,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Machine learning-enabled maternal risk assessment for women with pre-eclampsia - the PIERS-ML model - a modelling study","Pre-eclampsia affects 2–4% of pregnancies and remains a major global driver of maternal death and severe morbidity. Using routinely available health system, demographic, and clinical data captured on the first assessment, the PIERS-ML study develops and validates a machine learning, clinical setting-responsive time-of-disease model. The goal is to rule out and rule in a Delphi-derived composite outcome of maternal mortality or severe morbidity within 2 days, with internal and external validation.","Articles  \nMachine learning-enabled maternal risk assessment for women with pre-eclampsia (the PIERS-ML model):  \na modelling study  \nTünde Montgomery-Csobán, Kimberley Kavanagh, Paul Murray, Chris Robertson, Sarah J E Barry, U Vivian Ukah, Beth A Payne,  \nKypros H Nicolaides, Argyro Syngelaki, Olivia Ionescu, Ranjit Akolekar, Jennifer A Hutcheon, Laura A Magee, Peter von Dadelszen, on behalf of the PIERS Consortium*  \nSummary  \nBackground Affecting 2–4% of pregnancies, pre-eclampsia is a leading cause of maternal death and morbidity worldwide. Using routinely available data, we aimed to develop and validate a novel machine learning-based and clinical setting-responsive time-of-disease model to rule out and rule in adverse maternal outcomes in women presenting with pre-eclampsia.  \nMethods We used health system, demographic, and clinical data from the day of first assessment with pre-eclampsia to predict a Delphi-derived composite outcome of maternal mortality or severe morbidity within 2 days. Machine learning methods, multiple imputation, and ten-fold cross-validation were used to fit models on a development dataset (75% of combined published data of 8843 patients from 11 low-income, middle-income, and high-income countries) . Validation was undertaken on the unseen 25%, and an additional external validation was performed in 2901 inpatient women admitted with pre-eclampsia to two hospitals in south-east England. Predictive risk accuracy was determined by area-under-the-receiver-operator characteristic (AUROC), and risk categories were data-driven and defined by negative (–LR) and positive (+LR) likelihood ratios.  \nFindings Of 8843 participants, 590 (6·7%) developed the composite adverse maternal outcome within 2 days, 813 (9·2%) within 7 days, and 1083 (12·2%) at any time. An 18-variable random forest-based prediction model, PIERSML, was accurate (AUROC 0·80 [95% CI 0·76–0·84] vs the currently used logistic regression model, fullPIERS: AUROC 0·68 [0·63–0·74]) and categorised women into very low risk (–LR \u003C0·1; eight [0·7%] of 1103 women), low risk (–LR 0·1 to 0·2; 321 [29·1%] women), moderate risk (–LR >0·2 and +LR \u003C5·0; 676 [61·3%] women), high risk (+LR 5·0 to 10·0, 87 [7·9%] women), and very high risk (+LR >10·0; 11 [1·0%] women) . Adverse maternal event rates were 0% for very low risk, 2% for low risk, 5% for moderate risk, 26% for high risk, and 91% for very high risk within 48 h. The 2901 women in the external validation dataset were accurately classified as being at very low risk (0% withoutcomes), low risk (1%), moderate risk (4%), high risk (33%), or very high risk (67%) .  \nInterpretation The PIERS-ML model improves identification of women with pre-eclampsia who are at lowest and greatest risk of severe adverse maternal outcomes within 2 days of assessment, and can support provision of accurate guidance to women, their families, and their maternity care providers.  \nFunding University of Strathclyde Diversity in Data Linkage Centre for Doctoral Training, the Fetal Medicine Foundation, The Canadian Institutes of Health Research, and the Bill & Melinda Gates Foundation.  \nCopyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.  \nIntroduction  \nComplicating 2–4% of pregnancies, pre-eclampsia (defined as new-onset hypertension at or after 20 weeks’gestation, accompanied by either new-onset proteinuria, other maternal target organ damage, or evidence of uteroplacental dysfunction)1,2 remains a leading global cause of maternal mortality and life-threatening morbidity.1–5 More than 99% of the annual 46 000 preeclampsia-related maternal deaths occur in low-income and middle-income countries (LMICs) .6  \nIn pregnancies complicated by pre-eclampsia, it is clear that perinatal survival without major morbidity is largely related to gestational age at birth.7 However, the burden  \nof adverse maternal outcomes is spread across gestation. Although maternal risks are ","cbCailbXGcm0oB5m","https://ap.wps.com/l/cbCailbXGcm0oB5m","pdf",983086,1,13,"English","en",105,"# Summary\n## Background\n## Methods\n## Findings\n## Interpretation\n# Introduction","[{\"question\":\"What is the purpose of the PIERS-ML model in pre-eclampsia?\",\"answer\":\"The model improves identification of women with pre-eclampsia who are at the lowest and greatest risk of severe adverse maternal outcomes within 2 days of assessment, supporting accurate guidance for patients and maternity care providers.\"},{\"question\":\"What data and time point are used to build the model?\",\"answer\":\"Health system, demographic, and clinical data from the day of first assessment with pre-eclampsia are used to predict a Delphi-derived composite adverse maternal outcome within 2 days.\"},{\"question\":\"How was the model validated and how accurate is it?\",\"answer\":\"Internal validation used the unseen 25% of the development dataset with ten-fold cross-validation, and external validation used 2901 inpatient women from two hospitals in south-east England. The random forest-based PIERS-ML achieved AUROC 0.80, outperforming the currently used fullPIERS logistic regression model (AUROC 0.68).\"}]",1785673163,33,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"machine-learning-enabled-maternal-risk-assessment-for-women-with-pre-eclampsia-the-piers-ml-model-a-modelling-study","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/machine-learning-enabled-maternal-risk-assessment-for-women-with-pre-eclampsia-the-piers-ml-model-a-modelling-study/117029/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What is the purpose of the PIERS-ML model in pre-eclampsia?","Question",{"text":74,"@type":75},"The model improves identification of women with pre-eclampsia who are at the lowest and greatest risk of severe adverse maternal outcomes within 2 days of assessment, supporting accurate guidance for patients and maternity care providers.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What data and time point are used to build the model?",{"text":79,"@type":75},"Health system, demographic, and clinical data from the day of first assessment with pre-eclampsia are used to predict a Delphi-derived composite adverse maternal outcome within 2 days.",{"name":81,"@type":72,"acceptedAnswer":82},"How was the model validated and how accurate is it?",{"text":83,"@type":75},"Internal validation used the unseen 25% of the development dataset with ten-fold cross-validation, and external validation used 2901 inpatient women from two hospitals in south-east England. The random forest-based PIERS-ML achieved AUROC 0.80, outperforming the currently used fullPIERS logistic regression model (AUROC 0.68).","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]