Trial of labor after 2 previous cesareans: a multicenter study.

BACKGROUND: Trial of labor after cesarean after 2 cesarean deliveries is linked to a lower success rate of vaginal delivery and higher rates of adverse obstetrical outcomes than trial of labor after cesarean after 1 previous cesarean delivery. OBJECTIVE: This study aimed to investigate the factors associated with failed trial of labor after cesarean among women with 2 previous cesarean deliveries. STUDY DESIGN: This was a multicenter retrospective cohort study, which included all women with singleton pregnancies attempting trial of labor after cesarean after 2 previous cesarean deliveries between 2003 and 2021. This study compared labor, maternal, and neonatal characteristics between women with failed trial of labor after cesarean and those with successful trial of labor after cesarean. Univariate analysis was initially performed, followed by multivariable analysis (adjusted odds ratios with 95% confidence intervals). RESULTS: The study included a total of 1181 women attempting trial of labor after cesarean after 2 previous cesarean deliveries. Among these cases, vaginal birth after cesarean was achieved in 973 women (82.4%). Women with failed trial of labor after cesarean had higher rates of maternal and neonatal morbidities. Several factors were found to be associated with failed trial of labor after cesarean, including longer interpregnancy and interdelivery intervals, lower gravidity and parity, lower rates of previous successful vaginal delivery, smoking, earlier gestational age at delivery (38.3±2.1 vs 39.5±1.3 weeks), late preterm delivery (34-37 weeks of gestation), lower cervical dilation on admission, no use of epidural, and smaller neonatal birthweight. Our multivariable model revealed that late preterm delivery (adjusted odds ratio, 3.79; 95% confidence interval, 1.37-10.47) and cervical dilation on admission for labor <3 cm (adjusted odds ratio, 2.58; 95% confidence interval, 1.47-4.54) were associated with higher odds of failed trial of labor after cesarean. CONCLUSION: In the investigated population of women with 2 previous cesarean deliveries undergoing trial of labor after cesarean, admission at the late preterm period with a cervical dilation of <3 cm, which reflects the latent phase, may elevate the risk of failed trial of labor after cesarean and a repeated intrapartum cesarean delivery.

Induction of Labor in Twins-Double Trouble?

OBJECTIVE: To determine and compare the safety and efficacy of different methods of induction of labor in twin gestations and their effect on maternal and neonatal outcomes. METHODS: A retrospective observational cohort study was conducted at a single university-affiliated medical center. Patients with a twin gestation undergoing induction of labor at >32 + 0 weeks comprised the study group. Outcomes were compared to patients with a twin gestation at >32 + 0 weeks who went into labor spontaneously. The primary outcome was cesarean delivery. Secondary outcomes included operative vaginal delivery, postpartum hemorrhage, uterine rupture, 5 min APGAR < 7, and umbilical artery pH < 7.1. A subgroup analysis comparing outcomes for the induction of labor with oral prostaglandin E1 (PGE1), IV Oxytocin ± artificial rupture of membranes (AROM), and extra-amniotic balloon (EAB)+ IV Oxytocin was performed. Data were analyzed using Fisher's exact test, ANOVA, and chi-square tests. RESULTS: 268 patients who underwent induction of labor with a twin gestation comprised the study group. 450 patients with a twin gestation who went into labor spontaneously comprised the control group. There were no clinically significant differences between the groups for maternal age, gestational age, neonatal birthweight, birthweight discordancy, and non-vertex second twin. There were significantly more nulliparas in the study group compared to the control group (23.9% vs. 13.8% p < 0.001). The study group was significantly more likely to undergo a cesarean delivery of at least one twin (12.3% vs. 7.5% OR, 1.7 95% CI 1.04-2.85 p = 0.03). However, there was no significant difference in the rate of operative vaginal delivery (15.3% vs. 19.6% OR, 0.74, 95% CI 0.5-1.1 p = 0.16), PPH (5.2% vs. 6.9% OR, 0.75 95% CI 0.39-1.42 p = 0.37), 5-min APGAR scores < 7 (0% vs. 0.2% OR, 0.99 95%CI 0.99-1.00 p = 0.27), umbilical artery pH < 7.1 (1.5% vs. 1.3% OR, 1.12 95% CI 0.3-4.0), or combined adverse outcome (7.8% vs. 8.7% OR, 0.93 95% CI 0.6-1.4 p = 0.85). Furthermore, there were no significant differences in the rates of cesarean delivery or combined adverse outcomes in patients undergoing induction with oral PGE1 compared to IV Oxytocin ± AROM (13.3% vs. 12.5% OR, 1.1 95% CI 0.4-2.0 p = 1.0) (7% vs. 9.3% OR, 0.77 95% CI 0.5-3.5 p = 0.63 ) or EAB+ IV Oxytocin (13.3% vs. 6.9% OR, 2.1 95% CI 0.1-2.1 p = 0.53) (7% vs. 6.9% OR, 1.4 95% CI 0.15-3.5 p = 0.5) or between patients undergoing induction of labor with IV Oxytocin ± AROM and EAB+ IV Oxytocin (12.5% vs. 6.9% OR, 2.1 95% CI 0.1-2.4 p = 0.52) (9.3% vs. 6.9% OR, 0.98 95% CI 0.2-4.7 p = 0.54). There were no cases of uterine rupture in our study. CONCLUSIONS: Induction of labor in twin gestations is associated with a two-fold increased risk of cesarean delivery, although this is not associated with adverse maternal or neonatal outcomes. Furthermore, the method of induction of labor used does not affect the chances of success nor the rate of adverse maternal or neonatal outcomes.

Embryologist agreement when assessing blastocyst implantation probability: is data-driven prediction the solution to embryo assessment subjectivity?

STUDY QUESTION: What is the accuracy and agreement of embryologists when assessing the implantation probability of blastocysts using time-lapse imaging (TLI), and can it be improved with a data-driven algorithm? SUMMARY ANSWER: The overall interobserver agreement of a large panel of embryologists was moderate and prediction accuracy was modest, while the purpose-built artificial intelligence model generally resulted in higher performance metrics. WHAT IS KNOWN ALREADY: Previous studies have demonstrated significant interobserver variability amongst embryologists when assessing embryo quality. However, data concerning embryologists' ability to predict implantation probability using TLI is still lacking. Emerging technologies based on data-driven tools have shown great promise for improving embryo selection and predicting clinical outcomes. STUDY DESIGN, SIZE, DURATION: TLI video files of 136 embryos with known implantation data were retrospectively collected from two clinical sites between 2018 and 2019 for the performance assessment of 36 embryologists and comparison with a deep neural network (DNN). PARTICIPANTS/MATERIALS, SETTING, METHODS: We recruited 39 embryologists from 13 different countries. All participants were blinded to clinical outcomes. A total of 136 TLI videos of embryos that reached the blastocyst stage were used for this experiment. Each embryo's likelihood of successfully implanting was assessed by 36 embryologists, providing implantation probability grades (IPGs) from 1 to 5, where 1 indicates a very low likelihood of implantation and 5 indicates a very high likelihood. Subsequently, three embryologists with over 5 years of experience provided Gardner scores. All 136 blastocysts were categorized into three quality groups based on their Gardner scores. Embryologist predictions were then converted into predictions of implantation (IPG ≥ 3) and no implantation (IPG ≤ 2). Embryologists' performance and agreement were assessed using Fleiss kappa coefficient. A 10-fold cross-validation DNN was developed to provide IPGs for TLI video files. The model's performance was compared to that of the embryologists. MAIN RESULTS AND THE ROLE OF CHANCE: Logistic regression was employed for the following confounding variables: country of residence, academic level, embryo scoring system, log years of experience and experience using TLI. None were found to have a statistically significant impact on embryologist performance at α = 0.05. The average implantation prediction accuracy for the embryologists was 51.9% for all embryos (N = 136). The average accuracy of the embryologists when assessing top quality and poor quality embryos (according to the Gardner score categorizations) was 57.5% and 57.4%, respectively, and 44.6% for fair quality embryos. Overall interobserver agreement was moderate (κ = 0.56, N = 136). The best agreement was achieved in the poor + top quality group (κ = 0.65, N = 77), while the agreement in the fair quality group was lower (κ = 0.25, N = 59). The DNN showed an overall accuracy rate of 62.5%, with accuracies of 62.2%, 61% and 65.6% for the poor, fair and top quality groups, respectively. The AUC for the DNN was higher than that of the embryologists overall (0.70 DNN vs 0.61 embryologists) as well as in all of the Gardner groups (DNN vs embryologists-Poor: 0.69 vs 0.62; Fair: 0.67 vs 0.53; Top: 0.77 vs 0.54). LIMITATIONS, REASONS FOR CAUTION: Blastocyst assessment was performed using video files acquired from time-lapse incubators, where each video contained data from a single focal plane. Clinical data regarding the underlying cause of infertility and endometrial thickness before the transfer was not available, yet may explain implantation failure and lower accuracy of IPGs. Implantation was defined as the presence of a gestational sac, whereas the detection of fetal heartbeat is a more robust marker of embryo viability. The raw data were anonymized to the extent that it was not possible to quantify the number of unique patients and cycles included in the study, potentially masking the effect of bias from a limited patient pool. Furthermore, the lack of demographic data makes it difficult to draw conclusions on how representative the dataset was of the wider population. Finally, embryologists were required to assess the implantation potential, not embryo quality. Although this is not the traditional approach to embryo evaluation, morphology/morphokinetics as a means of assessing embryo quality is believed to be strongly correlated with viability and, for some methods, implantation potential. WIDER IMPLICATIONS OF THE FINDINGS: Embryo selection is a key element in IVF success and continues to be a challenge. Improving the predictive ability could assist in optimizing implantation success rates and other clinical outcomes and could minimize the financial and emotional burden on the patient. This study demonstrates moderate agreement rates between embryologists, likely due to the subjective nature of embryo assessment. In particular, we found that average embryologist accuracy and agreement were significantly lower for fair quality embryos when compared with that for top and poor quality embryos. Using data-driven algorithms as an assistive tool may help IVF professionals increase success rates and promote much needed standardization in the IVF clinic. Our results indicate a need for further research regarding technological advancement in this field. STUDY FUNDING/COMPETING INTEREST(S): Embryonics Ltd is an Israel-based company. Funding for the study was partially provided by the Israeli Innovation Authority, grant #74556. TRIAL REGISTRATION NUMBER: N/A.