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Updated: Jun 30, 2025
A Murine Model of Fetal Exposure to Maternal Inflammation to Study the Effects of Acute Chorioamnionitis on Newborn Intestinal Development
Published on: June 24, 2020
Non-invasive prediction model of histologic chorioamnionitis with preterm prelabour rupture of membranes
Yan Hu1, Zheng Ye2, Nathan Obore3
1Department of Obstetrics and Gynecology, Zhongda Hospital Affiliated to Southeast University, Nanjing 210009, China.
Background:
The aim of this study is to identify risk factors associated with histological chorioamnionitis (HCA) and develop a predictive model for antepartum assessment of the risk of PPROM with HCA.
Methods:
This study retrospectively analyzed pregnant women who experienced PPROM between 25 + 0 and 35 + 0 weeks of gestational age. The women were divided into two groups based on the presence or absence of HCA. Univariate and multivariate logistic regression analyses were conducted to identify maternal risk factors and develop a clinical prediction model for HCA. The model's discrimination and consistency were evaluated using receiver operating characteristic (ROC) and calibration curves.
Results:
Seventeen thousand one hundred forty-six (17,146) pregnant women were screened, and 726 (4.23 %) had PPROM. Out of the 286 subjects with PPROM, 160 developed HCA. The maternal age of these subjects ranged from 18 to 43 years (30.0 ± 5.4), while their gestational age (GA) ranged from 25 + 0 to 35 + 0 weeks (31.6 ± 2.0). The average GA at delivery was 32.2 ± 2.0 (weeks).Compared with the non-HCA group, the expectant time > 48 h, GA at delivery > 32 weeks, twin pregnancy, HGB (<110 g/Lg/L), degree of LGB (IIb-III), and WBC (>9.5 × 109 /L) were significantly more than in the PPROM with HCA group. The results show that the best model was obtained by leave-one-out logistic regression (AUC = 0.785, CA = 0.741, F1 = 0.739, Precision = 0.740, Recall = 0.741). In the validation set, logistic regression also achieved good results (AUC = 0.710, CA = 0.671, F1 = 0.654, Precision = 0.683, Recall = 0.671). Combining the previous analysis, we found that the prognostic model constructed using the core six features had the best predictive effect.
Conclusions:
Six features were associated with the occurrence of chorioamnionitis. These features were used to construct a diagnostic model that can accurately predict the probability of chorioamnionitis occurrence and provide a beneficial tool for the prevention and management of PPROM with HCA.
