The incidence and risk factors of meconium amniotic fluid in singleton pregnancies: an experience of a tertiary hospital in Iran

Shekari, Mitra; Jahromi, Malihe Shirzadfard; Ranjbar, Amene; Mehrnoush, Vahid; Darsareh, Fatemeh; Roozbeh, Nasibeh

doi:10.1186/s12884-022-05285-8

Cited by 5 publications

(1 citation statement)

References 16 publications

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“…The late-term group had a higher prevalence of primiparous mothers. Primiparity is one of the most common identifiable risk factors for the prolongation of pregnancy [ 7 ]. The late-term group had a lower rate of diabetes in terms of maternal comorbidities.…”

Section: Discussionmentioning

confidence: 99%

The Incidence and Outcomes of Late-Term Pregnancy

Ranjbar¹,

Mehrnoush²,

Darsareh³

et al. 2023

Cureus

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Background: Little is known about the outcomes of late-term pregnancy. In this study, we aim to assess the incidence and adverse prenatal outcomes associated with late-term pregnancy.Methods: We retrospectively assessed all singleton pregnant mothers who gave birth at Khalij-e-Fars Hospital in Bandar Abbas, Iran, between January 2020 and 2022. All preterm and post-term deliveries were excluded. Mothers were divided into two groups: late-term mothers (41 0/7-41 6/7 weeks of gestation) and term mothers (37 0/7-40 6/7 weeks of gestation). Demographic factors, obstetric factors, maternal comorbidities, and prenatal outcomes were extracted from the electronic data of each mother. The incidence of late-term births was calculated. The chi-squared test was used to compare differences between the groups. Logistic regression models were used to assess the association of prenatal outcome with late-term pregnancy.Results: There were 8,888 singleton deliveries during the study period, and 1,269 preterm and post-term pregnancies were ruled out. Of the 7,619 deliveries, 309 (4.1%) were late-term, while 7,310 (95.9%) were term. There were no sociodemographic differences between term and late-term mothers. The late-term group had a higher prevalence of primiparous mothers, and the term group had a higher prevalence of diabetes. Late-term mothers had a higher risk of macrosomia (adjusted odds ratio (aOR): 2.24 (95% confidence interval (CI): 1.34-3.01)), meconium amniotic fluid (aOR: 2.32 (95% CI: 1.59-3.14)), and fetal distress (aOR: 2.38 (95% CI: 1.54-2.79)). When compared to term pregnancy, the risk of low birth weight (LBW) was lower in late-term pregnancy (aOR: 0.69 (95% CI: 0.36-0.81)).Conclusions: Late-term pregnancy was found to be more likely to be associated with macrosomia, meconium amniotic fluid, and fetal distress, but serious maternal and neonatal adverse events were comparable to term pregnancy.

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Section: Discussionmentioning

confidence: 99%

The Incidence and Outcomes of Late-Term Pregnancy

Ranjbar¹,

Mehrnoush²,

Darsareh³

et al. 2023

Cureus

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Does cesarean section prevent adverse neonatal outcomes associated with meconium amniotic fluid?

Ranjbar,

Ghamsari,

Taeidi

et al. 2023

gocm

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BackgroundMaking decisions regarding the mode of delivery in the cases of meconium amniotic fluid (MAF) presents a challenge for healthcare providers. We aimed to compare the neonatal outcome of MAF cases delivered via cesarean section (CS) versus those delivered vaginally to determine if CS is a protective factor against the adverse neonatal outcomes.MethodsIn this retrospective study, we assessed singleton pregnant mothers diagnosed with MAF who gave birth at a tertiary hospital in Bandar Abbas, Iran, between January 2020-2022. Mothers with certain adverse pregnancy conditions were excluded from the study. These conditions included: abnormal fetal heart rate and pattern, bloody amniotic fluid, malpresentation, abnormal placentation, chorioamnionitis, intrauterine growth restriction, intrauterine fetal death, obstructed labor, and maternal comorbidities. The MAF mothers were divided into two groups based on the method of delivery: those who had CS and those who had a normal vaginal delivery (NVD). Demographic factors, obstetrical factors, and neonatal outcomes were compared between the two groups.ResultsOut of 746 MAF mothers, 213 (28.5%) underwent CS, while 533 (71.4%) had NVD. There were no significant differences between the groups in terms of demographic characteristics. Among MAF mothers who had CS, 66.2% were primiparous, and 33.8% were multiparous. For those who had NVD, 35.1% were primiparous, and 64.9% were multiparous. The first and 5-min Apgar values, rates of asphyxia, neonatal intensive care unit (NICU) admission, and neonatal death were not statistically different between the two delivery modes. The rate of newborns who breastfed within the first hour did not differ depending on the mode of delivery. Although initial resuscitation steps were required more frequently in MAF mothers with NVD than in those with CS (11.1% vs. 2.3%), no correlation was found between the mode of delivery and the need for resuscitation using logistic regression.ConclusionsOur research findings suggest that there were no superior neonatal outcomes in terms of CS compared to NVD in MAF mothers. Further studies are needed to provide more substantial evidence to support this conclusion.

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Application of machine learning to identify risk factors of birth asphyxia

Darsareh

Ranjbar

Etebarian

et al. 2023

BMC Pregnancy Childbirth

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Background Developing a prediction model that incorporates several risk factors and accurately calculates the overall risk of birth asphyxia is necessary. The present study used a machine learning model to predict birth asphyxia. Methods Women who gave birth at a tertiary Hospital in Bandar Abbas, Iran, were retrospectively evaluated from January 2020 to January 2022. Data were extracted from the Iranian Maternal and Neonatal Network, a valid national system, by trained recorders using electronic medical records. Demographic factors, obstetric factors, and prenatal factors were obtained from patient records. Machine learning was used to identify the risk factors of birth asphyxia. Eight machine learning models were used in the study. To evaluate the diagnostic performance of each model, six metrics, including area under the receiver operating characteristic curve, accuracy, precision, sensitivity, specificity, and F1 score were measured in the test set. Results Of 8888 deliveries, we identified 380 women with a recorded birth asphyxia, giving a frequency of 4.3%. Random Forest Classification was found to be the best model to predict birth asphyxia with an accuracy of 0.99. The analysis of the importance of the variables showed that maternal chronic hypertension, maternal anemia, diabetes, drug addiction, gestational age, newborn weight, newborn sex, preeclampsia, placenta abruption, parity, intrauterine growth retardation, meconium amniotic fluid, mal-presentation, and delivery method were considered to be the weighted factors. Conclusion Birth asphyxia can be predicted using a machine learning model. Random Forest Classification was found to be an accurate algorithm to predict birth asphyxia. More research should be done to analyze appropriate variables and prepare big data to determine the best model.

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The incidence and risk factors of meconium amniotic fluid in singleton pregnancies: an experience of a tertiary hospital in Iran

Cited by 5 publications

References 16 publications

The Incidence and Outcomes of Late-Term Pregnancy

The Incidence and Outcomes of Late-Term Pregnancy

Does cesarean section prevent adverse neonatal outcomes associated with meconium amniotic fluid?

Application of machine learning to identify risk factors of birth asphyxia

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