Vaginal birth after caesarean

Artifact characterization of Nitinol needles in magnetic resonance imaging-guided musculoskeletal interventions at 3.0 tesla: a phantom study

PURPOSE

To characterize the artifacts of an 18-gauge coaxial nickel-titanium needle using a balanced steady-state free precession sequence in magnetic resonance imaging-guided interventions at 3.0 tesla.

METHODS

The influence of flip angle (FA), bandwidth, matrix, slice thickness (ST), and read-out direction on needle artifact behavior was investigated for different intervention angles (IA). Artifact diameters were rated at predefined positions. Subgroup differences were assessed using Bonferroni-corrected non-parametric tests and correlations between continuous variables were expressed using the Bravais-Pearson coefficient. Interrater reliability was quantified using intraclass correlation coefficients (ICCs), and a contrast-enhanced target lesion to non-enhanced muscle tissue contrast ratio was quantified.

RESULTS

The artifact diameters decreased with an increase in FA for all IAs (P < 0.001) and with an increase in ST for IAs of 45°-90° (all P < 0.05). Tip artifacts occurred at low IAs (0°-45°) and gradually increased in size with a decrease in IA (P = 0.022). The interrater reliability was high (ICC: 0.994-0.999). The contrast-enhanced target lesion to non-enhanced muscle tissue contrast ratio presented positive correlations with increasing FAs and matrices (P < 0.001; P = 0.003) and a negative correlation with increasing STs (P = 0.007).

CONCLUSION

To minimize needle artifacts, it is recommended to use FAs of 40°-60°, a ST of >7 mm, and, if possible, an IA of 45°-60°. The visibility of the target lesion and the needle's artifact behavior must be weighed up against each other when choosing the ST, while higher FAs (40°-60°) and matrices (224 × 224/256 × 256) are associated with low artifacts and sufficient lesion visibility.

Taking the reins: A grounded theory study of women's experiences of negotiating water immersion for labour and birth after a previous caesarean section

BACKGROUND

Women seeking a vaginal birth after a caesarean section (VBAC) frequently want to keep their subsequent labour and birth free from intervention. Water immersion (WI) during labour is potentially an effective tool for women having a VBAC for its natural pain-relieving properties. However, negotiating access to WI can be difficult, especially in the context of VBAC.

AIM

To explore women's experiences of negotiating WI for labour and birth in the context of VBAC.

METHODOLOGY

This Grounded Theory study followed Strauss and Corbin's framework and analytic process. Twenty-five women planning or using WI for their VBAC labour or birth were recruited from two midwifery practices and a social media group across Australia. Participants were interviewed during pregnancy and/or postnatally.

FINDINGS

'Taking the reins', the core category explaining the women's experiences of assuming authority over their birth, comprised five categories: 'Robbed of my previous birth experience'; 'My eyes were opened'; 'Water is my tool for a successful VBAC'; 'Actioning my choices and rights for WI', and 'Empowered to take back control'. 'Wanting natural and normal' was the driving force behind women's desire to birth vaginally. Two mediating factors: Having someone in your corner and Rules for birth facilitated or hindered their birth choices, respectively.

CONCLUSION

The women became active participants in their healthcare by seeking information and options to keep their birth experience natural and normal. Support from other women and advocacy in the form of continuity of midwifery care was crucial in successfully negotiating WI for their VBAC when navigating the complex health system.

Intrauterine versus external tocodynamometry in monitoring labour: a randomised controlled clinical trial

OBJECTIVE

To investigate whether the use of intrauterine tocodynamometry versus external tocodynamometry (IT versus ET) during labour reduces operative deliveries and improves newborn outcome. As IT provides more accurate information on labour contractions, the hypothesis was that it may more appropriately guide oxytocin use than ET.

DESIGN

Randomised controlled trial.

SETTING

Two labour wards, in a university tertiary hospital and a central hospital.

POPULATION

A total of 1504 parturients with singleton pregnancies, gestational age ≥37 weeks and fetus in cephalic position: 269 women with uterine scars, 889 nulliparas and 346 parous women with oxytocin augmentation.

METHODS

Participants underwent IT (n = 736) or ET (n = 768) during the active first stage of labour.

MAIN OUTCOME MEASURES

Primary outcome: rate of operative deliveries.

SECONDARY OUTCOMES

duration of labour, amount of oxytocin given, adverse neonatal outcomes.

RESULTS

Operative delivery rates were 26.9% (IT) and 25.9% (ET) (odds ratio 1.05, 95% CI 0.84-1.32, P = 0.663). The ET to IT conversion rate was 31%. We found no differences in secondary outcomes (IT versus ET). IT reduced oxytocin use during labours with signs of fetal distress, and trial of labour after caesarean section.

CONCLUSIONS

IT did not reduce the rate of operative deliveries, use of oxytocin, or adverse neonatal outcomes, and it did not shorten labour duration.

TWEETABLE ABSTRACT

IT (versus ET) reduced oxytocin use in high-risk labours but did not influence operative delivery rate or adverse neonatal outcomes.

Prediction of vaginal birth after cesarean deliveries using machine learning

BACKGROUND

Efforts to reduce cesarean delivery rates to 12-15% have been undertaken worldwide. Special focus has been directed towards parturients who undergo a trial of labor after cesarean delivery to reduce the burden of repeated cesarean deliveries. Complication rates are lowest when a vaginal birth is achieved and highest when an unplanned cesarean delivery is performed, which emphasizes the need to assess, in advance, the likelihood of a successful vaginal birth after cesarean delivery. Vaginal birth after cesarean delivery calculators have been developed in different populations; however, some limitations to their implementation into clinical practice have been described. Machine-learning methods enable investigation of large-scale datasets with input combinations that traditional statistical analysis tools have difficulty processing.

OBJECTIVE

The aim of this study was to evaluate the feasibility of using machine-learning methods to predict a successful vaginal birth after cesarean delivery.

STUDY DESIGN

The electronic medical records of singleton, term labors during a 12-year period in a tertiary referral center were analyzed. With the use of gradient boosting, models that incorporated multiple maternal and fetal features were created to predict successful vaginal birth in parturients who undergo a trial of labor after cesarean delivery. One model was created to provide a personalized risk score for vaginal birth after cesarean delivery with the use of features that are available as early as the first antenatal visit; a second model was created that reassesses this score after features are added that are available only in proximity to delivery.

RESULTS

A cohort of 9888 parturients with 1 previous cesarean delivery was identified, of which 75.6% of parturients (n=7473) attempted a trial of labor, with a success rate of 88%. A machine-learning-based model to predict when vaginal delivery would be successful was developed. When features that are available at the first antenatal visit are used, the model showed a receiver operating characteristic curve with area under the curve of 0.745 (95% confidence interval, 0.728-0.762) that increased to 0.793 (95% confidence interval, 0.778-0.808) when features that are available in proximity to the delivery process were added. Additionally, for the later model, a risk stratification tool was built to allocate parturients into low-, medium-, and high-risk groups for failed trial of labor after cesarean delivery. The low- and medium-risk groups (42.4% and 25.6% of parturients, respectively) showed a success rate of 97.3% and 90.9%, respectively. The high-risk group (32.1%) had a vaginal delivery success rate of 73.3%. Application of the model to a cohort of parturients who elected a repeat cesarean delivery (n=2145) demonstrated that 31% of these parturients would have been allocated to the low- and medium-risk groups had a trial of labor been attempted.

CONCLUSION

Trial of labor after cesarean delivery is safe for most parturients. Success rates are high, even in a population with high rates of trial of labor after cesarean delivery. Application of a machine-learning algorithm to assign a personalized risk score for a successful vaginal birth after cesarean delivery may help in decision-making and contribute to a reduction in cesarean delivery rates. Parturient allocation to risk groups may help delivery process management.