ArticleBMC pregnancy and childbirth2026
A nomogram to predict meconium-stained amniotic fluid in patients during labor: a retrospective cohort study.
Article in BMC pregnancy and childbirth, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundMeconium-stained amniotic fluid (MSAF) carries an increased risk of maternal-fetal mortality and morbidity. This study aimed to develop and internally validate a nomogram for predicting the occurrence of MSAF in full-term pregnant women during trial of labor.
methodsWe retrospectively screened cases of MSAF in patients during labor in our hospital. Potential risk factors were screened through univariate analysis, followed by feature selection using least absolute shrinkage and selection operator (LASSO) regression. A multivariate logistic regression model was constructed and visualized as a nomogram. The model underwent internal validation and was evaluated using receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA).
resultsThis cohort study included 2260 women, comprising 354 cases with MSAF and 1906 controls. All enrolled patients were randomly divided into training set (n = 1582) and validation set (n = 678). A clinical nomogram was developed incorporating nine independent predictors, including primiparous women, gestational age ≥ 41 weeks, premature rupture of membranes, C-reactive protein level, intrahepatic cholestasis of pregnancy, catheter balloon insertion, intrapartum fever, pathological fetal heart rate patterns, and duration of latent phase (> 16 h). The area under the curves (AUCs) of the training and validation set were 0.891 and 0.883, respectively. The calibration curve indicated excellent agreement between the predicted probabilities and the actual observed outcomes and the DCA showed significantly good net benefit in the predictive model.
conclusionThe proposed nomogram may help clinicians identify high-risk MSAF patients during labor and optimize perinatal management.
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