Author:
Zhang Weiyi,Liang Hongping
Abstract
BACKGROUND: Preeclampsia (PE) has adverse effects on pregnant women, fetuses, and newborns [1], and accounts for 3%–10% of pregnancy-related diseases globally. OBJECTIVE: This study aimed to screen a series of prenatal markers (pregnancy-associated plasma protein [PAPP-A], β-human chorionic gonadotropin [β-hCG], alpha fetoprotein [AFP], and estriol [uE3]) to establish a risk model and evaluate the diagnostic values of the markers for predicting PE. METHODS: Sixty-five pregnant women were enrolled in this study. They were divided into two groups containing healthy pregnant women (n= 51, the non-PE group) and pregnant women with PE (n= 14, the PE group). According to the stage of pregnancy, the pregnant women in each group were divided into early, middle, and late pregnancy groups for statistical analysis. The levels of PAPPA-A β-hCG, AFP, and uE3 were compared among these groups. Then, a risk model was established, and PE was diagnosed using receiver operating characteristic (ROC) curve results. RESULTS: In the early pregnancy group, the differences in the levels of PAPP-A, AFP, and uE3 between the PE and non-PE groups were statistically significant (P< 0.001, P= 0.029, and P= 0.033, respectively), while the difference in the single remaining marker was not statistically significant. A ROC curve analysis revealed that in early pregnancy, the sensitivity and specificity of PAPP-A were 76.5% and 71.4%, respectively, and the sensitivity and specificity of β-hCG were 82.4% and 57.1%, respectively. The sensitivity and specificity of the combination of the two markers for diagnosing PE were 86.3% and 57.1%, respectively. CONCLUSION: This study demonstrated that the combination of PAPP-A and β-hCG has diagnostic value for PE in pregnant women. Accordingly, we should formulate innovative PE screening strategies to target the prevention of PE and create important conditions for predictive and preventive personalized medical treatments.
Subject
Health Informatics,Biomedical Engineering,Information Systems,Biomaterials,Bioengineering,Biophysics
Cited by
1 articles.
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