Clinical characteristics and risk factors of premature rupture of membranes infection in pregnant and lying-in women
Premature rupture of membranes is one of the more common symptoms of pregnant women before labor, which can lead to an increased rate of preterm birth and a higher mortality rate of the fetus born from it. The current research on premature rupture of membranes (PROM) is mainly based on multivariate...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
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| Series: | SLAS Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2472630325000780 |
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| Summary: | Premature rupture of membranes is one of the more common symptoms of pregnant women before labor, which can lead to an increased rate of preterm birth and a higher mortality rate of the fetus born from it. The current research on premature rupture of membranes (PROM) is mainly based on multivariate regression analysis, and variables are selected for multivariate regression analysis after univariate analysis. This method may omit some independent variables, resulting in one-sided analysis results. In this context, this study uses Bayesian method and Logistic regression analysis to construct a new variable analysis model to analyze the clinical characteristics and risk factors of PROM infection. First, through Bayesian Logistic regression, the clinical features of PROM infection mainly include fever, increased white blood cells and C-reactive protein, and increased fetal heart rate. The analysis of risk factors showed that pathogen infection, maternal pregnancy number, and scarred uterus were all risk factors for PROM infection. Finally, in order to explain the effect of the analysis model used in this paper, a nonparametric test, AUC value and ROC curve were used to compare the effect of Bayesian Logistic regression and Logistic regression. The results showed that the statistic value of Bayesian logistic regression was 0.177 higher than that of logistic regression, and the AUC value was 0.014 higher. That is, the performance of the Bayesian logistic regression model is better. The method used in the experiment is feasible, and the experimental results are in line with expectations. |
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| ISSN: | 2472-6303 |