Feasibility of using a multivariate serum biomarker model in early pregnancy to predict gestational hypertension

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.

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Publication Details

Journal
Technology and Health Care
Published
2024-11-20
DOI
https://doi.org/10.1177/09287329241296399
Citations
2
Primary Topic
Pregnancy and preeclampsia studies
Type
article
Field-Weighted Citation Impact
1.17
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article

Feasibility of using a multivariate serum biomarker model in early pregnancy to predict gestational hypertension

Meixia Fang, Xiaoli Gao
2 citations
Technology and Health Care
Pregnancy and preeclampsia studies
1.17
article

Feasibility of using a multivariate serum biomarker model in early pregnancy to predict gestational hypertension

Meixia Fang, Xiaoli Gao
article en
2 citations

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.

Technology and Health CareVol. 33(2)
Zhaotong University (CN), The First People's Hospital of Zhaoqing (CN)
Openalex Percentile: Top 18%
Pregnancy and preeclampsia studies
1.17
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