{"title":"Feasibility of using a multivariate serum biomarker model in early pregnancy to predict gestational hypertension.","authors":"Meixia Fang, Xiaoli Gao","doi":"10.1177/09287329241296399","DOIUrl":null,"url":null,"abstract":"<p><p>BackgroundWith the increasing need for early prediction and intervention of Pregnancy-Induced Hypertension (PIH), researchers have begun to explore the use of multiserum biomarker models to improve the accuracy and reliability of predictions. It is estimated that between 5% and 8% of pregnant women worldwide experience pregnancy-induced hypertension, which is one of the leading causes of maternal death and adverse neonatal outcomes. Given the potential negative impact of pregnancy-induced hypertension on maternal and infant health, early identification of high-risk individuals and appropriate preventive measures are particularly important.ObjectiveTo assess the feasibility of using a multivariate serum biomarker model in early pregnancy to predict gestational hypertension.MethodsRetrospective analysis was conducted on the clinical data of 125 pregnant women admitted to our hospital from January 2021 to December 2022. The occurrence of gestational hypertension was recorded and multiple serum biomarkers were collected and compared between the exposure and non-exposure groups. Logistic regression analysis was performed to identify influencing factors for gestational hypertension. Correlations between each factor and gestational hypertension were analyzed, and a line chart model was constructed. The discriminative ability of the model was evaluated using the C-index, and internal validation was conducted using ten-fold cross-validation and bootstrap validation.ResultsOut of 125 pregnant women, 35 (28.00%) developed gestational hypertension. β-HCG and Hcy were identified as independent risk factors, while PAPP-A, AFP, and uE3 were identified as independent protective factors. There was a positive correlation between Hcy, β-HCG, and gestational hypertension, and a negative correlation between PAPP-A, AFP, uE3, and gestational hypertension. The predictive line chart model had a C-index of 0.885 and an average AUC value of 0.853 after internal validation.Conclusionβ-HCG and Hcy are risk factors, while PAPP-A, AFP, and uE3 are protective factors for gestational hypertension. A line chart model based on these factors can help identify pregnant women at risk of developing gestational hypertension in early pregnancy.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":"33 2","pages":"1046-1055"},"PeriodicalIF":1.4000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Technology and Health Care","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/09287329241296399","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/11/20 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
BackgroundWith the increasing need for early prediction and intervention of Pregnancy-Induced Hypertension (PIH), researchers have begun to explore the use of multiserum biomarker models to improve the accuracy and reliability of predictions. It is estimated that between 5% and 8% of pregnant women worldwide experience pregnancy-induced hypertension, which is one of the leading causes of maternal death and adverse neonatal outcomes. Given the potential negative impact of pregnancy-induced hypertension on maternal and infant health, early identification of high-risk individuals and appropriate preventive measures are particularly important.ObjectiveTo assess the feasibility of using a multivariate serum biomarker model in early pregnancy to predict gestational hypertension.MethodsRetrospective analysis was conducted on the clinical data of 125 pregnant women admitted to our hospital from January 2021 to December 2022. The occurrence of gestational hypertension was recorded and multiple serum biomarkers were collected and compared between the exposure and non-exposure groups. Logistic regression analysis was performed to identify influencing factors for gestational hypertension. Correlations between each factor and gestational hypertension were analyzed, and a line chart model was constructed. The discriminative ability of the model was evaluated using the C-index, and internal validation was conducted using ten-fold cross-validation and bootstrap validation.ResultsOut of 125 pregnant women, 35 (28.00%) developed gestational hypertension. β-HCG and Hcy were identified as independent risk factors, while PAPP-A, AFP, and uE3 were identified as independent protective factors. There was a positive correlation between Hcy, β-HCG, and gestational hypertension, and a negative correlation between PAPP-A, AFP, uE3, and gestational hypertension. The predictive line chart model had a C-index of 0.885 and an average AUC value of 0.853 after internal validation.Conclusionβ-HCG and Hcy are risk factors, while PAPP-A, AFP, and uE3 are protective factors for gestational hypertension. A line chart model based on these factors can help identify pregnant women at risk of developing gestational hypertension in early pregnancy.
期刊介绍:
Technology and Health Care is intended to serve as a forum for the presentation of original articles and technical notes, observing rigorous scientific standards. Furthermore, upon invitation, reviews, tutorials, discussion papers and minisymposia are featured. The main focus of THC is related to the overlapping areas of engineering and medicine. The following types of contributions are considered:
1.Original articles: New concepts, procedures and devices associated with the use of technology in medical research and clinical practice are presented to a readership with a widespread background in engineering and/or medicine. In particular, the clinical benefit deriving from the application of engineering methods and devices in clinical medicine should be demonstrated. Typically, full length original contributions have a length of 4000 words, thereby taking duly into account figures and tables.
2.Technical Notes and Short Communications: Technical Notes relate to novel technical developments with relevance for clinical medicine. In Short Communications, clinical applications are shortly described. 3.Both Technical Notes and Short Communications typically have a length of 1500 words.
Reviews and Tutorials (upon invitation only): Tutorial and educational articles for persons with a primarily medical background on principles of engineering with particular significance for biomedical applications and vice versa are presented. The Editorial Board is responsible for the selection of topics.
4.Minisymposia (upon invitation only): Under the leadership of a Special Editor, controversial or important issues relating to health care are highlighted and discussed by various authors.
5.Letters to the Editors: Discussions or short statements (not indexed).