Intervention thresholds and cesarean section rates: A time-trends analysis

Predelivery sonographic predictors of obstetric anal sphincter injury among nulliparous women

OBJECTIVE

Data available at admission to delivery of nulliparous has rarely been investigated for prediction of obstetric anal sphincter injury (OASI). The aim of the present study was to study risk factors for OASI in nulliparous based on information available at admission.

METHODS

A retrospective study of all nulliparous women undergoing labor, during March 2011 to January 2021 was performed. We compared women with OASI following delivery to those without by univariate and multivariable regression.

RESULTS

A total of 30 262 deliveries were included and 4181 (13.4%) of those were delivered by an emergent cesarean delivery. OASI followed 453 (1.5%) deliveries. Women in the OASI group were younger 29 ± 4.4 versus 30 ± 4.8, P = 0.001. In a multivariable regression analysis, higher sonographic estimated fetal weight was positively associated with OASI occurrence (aOR, 95% CI: 1.13 [1.00-1.29]). Maternal age was inversely associated with OASI occurrence (adjusted odds ratio [aOR], 95% confidence interval [CI] 0.95 [0.92-0.97]).

CONCLUSION

Sonographic fetal weight estimation is an independent risk factor for OASI occurrence that may be available at admission for delivery among nulliparous women.

Birthweight trends and their explanatory factors in Hungary between 1999 and 2018: an analysis of the Hungarian Tauffer registry

BACKGROUND

The increasing birthweight trend stopped and even reversed in several high income countries in the last 20 years, however the reason for these changes is not well characterized. We aimed to describe birthweight trends of term deliveries in Hungary between 1999 and 2018 and to investigate potential maternal and foetal variables that could drive these changes.

METHODS

We analysed data from the Hungarian Tauffer registry, a compulsory anonymized data collection of each delivery. We included all singleton term deliveries in 1999-2018 (n = 1,591,932). We modelled birthweight trends separately in 1999-2008 and 2008-2018 in hierarchical multiple linear regression models adjusted for calendar year, newborn sex, maternal age, gestational age at delivery, and other important determinants.

RESULTS

Median birthweights increased from 3250/3400 g (girl/boy) to 3300/3440 g from 1999 to 2008 and decreased to 3260/3400 g in 2018. When we adjusted for gestational age at delivery the increase in the first period became more pronounced (5.4 g/year). During the second period, similar adjustment substantially decreased the rate of decline from 2.5 to 1.4 g/year. Further adjustment for maternal age halved the rate of increase to 2.4 g/year in the first period. During the second period, adjustment for maternal age had little effect on the estimate.

CONCLUSIONS

Our findings of an increasing birthweight trend (mostly related to the aging of the mothers) in 1999-2008 may forecast an increased risk of cardiometabolic diseases in offsprings born in this period. In contrast, the decreasing birthweight trends after 2008 may reflect some beneficial effects on perinatal morbidity. However, the long-term effect cannot be predicted, as the trend is mostly explained by the shorter pregnancies.

An Analysis of the Mode of Delivery, Risk Factors, and Subgroups with High Caesarean Birth Rates Using Robson Classification System

OBJECTIVE

We aimed to understand the utilization of the mode of delivery and related risk factors. Further aimed to apply the Robson classification system to evaluate the data quality and analyze the CS rates in subgroups.

METHODS

We conducted a retrospective descriptive study by reviewing the medical records of all women who delivered at the State Hospital in 2019. A proforma was developed for extracting data from patient records. All women with six obstetric parameters were categorized into Robson groups to determine the absolute and relative contributions of each group to the overall CS rate.

RESULTS

Of 797 deliveries, 401 (50.2%) were CSs. Being older, being Turkish Cypriot, having preterm births, previous CS, multiple fetuses, and having breech or transverse fetal presentations were related to having higher risks of CS. The most common medical indication for CSs (52.3%) was a history of previous CSs. Robson Group 5 contributed the most (50.7%) to the overall CS rate, with the highest absolute contribution of 21.8%. Group 10 and Group 8 were the second and third highest contributors to the overall CS rate, with relative contributions of 25.3% and 9.0%, respectively.

CONCLUSIONS

Findings revealed the substandard quality of obstetric data and a noticeably high overall CS rate. The top priority should be given to improving the quality of medical records. It underscored the necessity of implementing the Robson classification system as a standard clinical practice to enhance data quality, which helps to effectively evaluate and monitor the CS rates in obstetric populations.

Risk of spontaneous preterm birth elevated after first cesarean delivery at full dilatation: a retrospective cohort study of over 30,000 women

BACKGROUND

Having a cesarean delivery at full dilatation has been associated with increased subsequent risk of spontaneous preterm birth. The Aberdeen Maternity and Neonatal Databank provides a rare opportunity to study subsequent pregnancy outcomes after a previous cesarean delivery at full dilatation over 40 years, with an ability to include a detailed evaluation of potential confounding factors.

OBJECTIVE

This study aimed to investigate if having an initial cesarean delivery at full dilatation is associated with spontaneous preterm birth or other adverse pregnancy outcomes in the subsequent pregnancy.

STUDY DESIGN

A retrospective cohort study was conducted including women with a first and second pregnancy recorded within the Aberdeen Maternity and Neonatal Databank between 1976 and 2017, where previous cesarean delivery at full dilatation at term in the first birth was the exposure. The primary outcome was spontaneous preterm birth (defined as spontaneous birth <37 weeks). Multivariate logistic regression was used to investigate any association between cesarean delivery at full dilatation and the odds of spontaneous preterm birth. Cesarean delivery at full dilatation in previous pregnancy was compared with: (1) any other mode of birth, and (2) individual modes of birth, including planned cesarean delivery, cesarean delivery in first stage of labor (<10-cm dilatation), and vaginal birth (including spontaneous vaginal birth, nonrotational forceps, Kielland forceps, vacuum-assisted birth, breech vaginal birth). Other outcomes such as antepartum hemorrhage and mode of second birth were also compared.

RESULTS

Of the 30,253 women included, 900 had a previous cesarean delivery at full dilatation in the first pregnancy. Women with previous cesarean delivery at full dilatation had a 3-fold increased risk of spontaneous preterm birth in a second pregnancy (unadjusted odds ratio, 2.63; 95% confidence interval, 1.82-3.81; adjusted odds ratio, 3.31; 95% confidence interval, 2.17-5.05) compared with those with all other modes of first birth, adjusted for maternal age, diabetes mellitus, body mass index, smoking, preeclampsia, antepartum hemorrhage, socioeconomic deprivation (Scottish Index of Multiple Deprivation 2016), year of birth, and interpregnancy interval (in second pregnancy). When compared with women with vaginal births only, women with cesarean delivery at full dilatation had 5-fold increased odds of spontaneous preterm birth (adjusted odds ratio, 5.37; 95% confidence interval, 3.40-8.48). Compared with first spontaneous vaginal birth, first instrumental births (nonrotational forceps, Kielland forceps, and vacuum births) were not associated with increased risk of spontaneous preterm birth in the second birth. After an initial cesarean delivery at full dilatation, 3.7% of women had a repeated cesarean delivery at full dilatation and 48% had a planned cesarean delivery in the second birth.

CONCLUSION

This study is a substantial addition to the body of evidence on the risk of subsequent spontaneous preterm birth after cesarean delivery at full dilatation, and demonstrates a strong association between cesarean delivery at full dilatation in the first birth and spontaneous preterm birth in subsequent pregnancy, although the absolute risk remains small. This is a large retrospective cohort and includes a comprehensive assessment of potential confounding factors, including preeclampsia, antepartum hemorrhage, and lengths of first and second stage of labor. Future research should focus on understanding possible causality and developing primary and secondary preventative measures.

Utilizing machine learning to predict unplanned cesarean delivery

OBJECTIVE

To develop a comprehensive machine learning (ML) model predicting unplanned cesarean delivery (uCD) among singleton pregnancies based on features available at admission to labor.

METHODS

A retrospective cohort study from a tertiary medical center. Women with singleton vertex pregnancy of 34 weeks or more admitted for vaginal delivery between March 2011 and May 2019 were included. The cohort was divided into training (80%) and validation (20%) data sets. A separate cohort between June 2019 and April 2021 served as a test data set. Features selection was performed using a Random Forest ML algorithm.

RESULTS

The study population included 73 667 women, of which 4125 (6.33%) underwent uCD. The final model consisted of 13 features, based on prediction importance. The XGBoost model performed best with areas under the curve for the training, validation, and test data sets of 0.874, 0.839, and 0.840, respectively. The model showed a 65% positive predictive value for uCD among women in the 100th centile group, and a 99% or more negative predictive value in the less than 50th centile group. Positive and negative predictive values remained high among subgroups with high pretest probability of uCD.

CONCLUSION

An ML model for the prediction of uCD provides clinically useful risk stratification that remains accurate across gestational weeks 34-42 and among clinical risk groups. The model may be clinically useful for physicians and women admitted for labor.

SYNOPSIS

A machine learning model predicts unplanned cesarean delivery and can inform women's individualized decision making.