More Options for Prelabor Rupture of Membranes, A Bayesian Analysis

TL;DR

The study tackles the unclear optimal induction agent for prelabor rupture of membranes (PROM) by explicitly accounting for Bishop score as a confounder. It deploys a Bayesian hierarchical, expert-informed approach with missing-value imputation to compare Pitocin and buccal misoprostol, focusing on outcomes such as time to delivery and cesarean risk. After adjusting for confounding, the analysis finds no significant difference between the two drugs, challenging the assumption of Pitocin superiority. The work demonstrates that causal Bayesian methods can yield actionable insights from small retrospective datasets and informs future trials and guidelines, particularly for resource-limited settings.

Abstract

An obstetric goal for a laboring mother is to achieve a vaginal delivery as it reduces the risks inherent in major abdominal surgery (i.e., a Cesarean section). Various medical interventions may be used by a physician to increase the likelihood of this occurring while minimizing maternal and fetal morbidity. However, patients with prelabor rupture of membranes (PROM) have only two commonly used options for cervical ripening, Pitocin and misoprostol. Little research exists on the benefits/risks for these two key drugs for PROM patients. A major limitation with most induction-of-labor related research is the inability to account for differences in \textit{Bishop scores} that are commonly used in obstetrical practice to determine the next induction agent offered to the patient. This creates a confounding factor, which biases the results, but has not been realized in the literature. In this work, we use a Bayesian model of the relationships between the relevant factors, informed by expert physicians, to separate the confounding variable from its actual impact. In doing so, we provide strong evidence that pitocin and buccal misoprostol are equally effective and safe; thus, physicians have more choice in clinical care than previously realized. This is particularly important for developing countries where neither medication may be readily available, and prior guidelines may create an artificial barrier to needed medication.

Figures (9)

• Figure 1: Diagram of the clinical process being evaluated. Patients have a cervical exam (top) that is used to calculate several measurements to form a clinically relevant Bishop score. This calculation is imprecise due to measurement error. This score is then used to determine if cervical ripening is needed (i.e., a closed cervix), at which point either buccal misoprostol or Pitocin is prescribed. Prior literature determined that Pitocin is more effective, but ignored that Pitocin was only chosen when the patient had a more favorable cervix (higher Bishop score). In this study, we include this confounding variable.
• Figure 2: Plate diagram of the relationships and connections in the model. The white notes indicate latent variables and grey nodes are observed variables used for calculating loglikelihood in the model. The "Position + Consistency" is mixed because it is partially observed, but in other cases, its value is inferred from other variables. Covariates with $\aleph$ are used to infer the missing value, and covariates with $\beta$ are used to make the predictions. The model is hierarchical, where each $\beta$ is replicated for each outcome it is used against, with a shared hyperprior. Nodes with an ampersand ("&") represent multiple nodes that have the same connections but are grouped together for legibility.
• Figure 3: The original distribution of the dependent variables for Augmentations and ROMs on the top and bottom rows, respectively. The x-axis in all figures is in hours.
• Figure 4: A posterior predictive check is performed to see that the model is appropriate for the observed data. In each case, the dashed red line is the model's mean, the blue lines show one of 200 draws from the distribution to show the variance, and the black line shows the observed data distribution. Our posterior shows high correspondence to the underlying data distribution and thus supports drawing inferences about the population from the results.
• Figure 5: Augmentation to Delivery is the time between when PIT or MISO was started, and when the baby was delivered. The non-difference of PIT-over-MISO is important as it shows the marginal impact of the medication is not as large as previously thought.