Neuraxial Labor Analgesia for Vaginal Delivery and Severe Maternal Morbidity

The Centers for Disease Control and Prevention (CDC) defines severe maternal morbidity (SMM) as an array of 16 diseases and 5 procedures denoting an acutely life-threatening pregnancy-related condition,¹ which also serve as excellent surrogates for adverse maternal and perinatal outcomes, including maternal and perinatal death.² SMM also has higher risk of an adverse effect on maternal functioning and well-being after the birth. Neuraxial labor analgesia for vaginal delivery is a procedure of neuraxial anesthesia and the most effective method to manage labor pain. In North America, more than 70% of women receive neuraxial labor analgesia for vaginal delivery. Guglielminotti et al³ conducted a US population-based observational study including 575 524 women who experienced vaginal delivery and found that neuraxial labor analgesia for vaginal delivery was associated with a 14% reduction in SMM compared with no neuraxial labor analgesia, which was mediated by more than 21% through reduction of postpartum hemorrhage (PPH).³

The study took the due diligence to delineate the outcomes associated with neuraxial labor analgesia in vaginal delivery for the propensity weighted cohort using the inverse probability treatment weighting (IPTW) method by further stratifying the baseline risk factors associated with SMM, such as race and ethnicity (ie, non-Hispanic White vs other, including Asian or Pacific Islander, Black, Hispanic, Native American, and other) and maternal comorbidities.³ Moreover, Guglielminotti et al³ conducted robust sensitivity analyses using the stabilized weights (SWs) and the propensity score matching as part of the cohort creation and multiple imputations for missing information.

Observational studies are commonly used by medical researchers seeking to make statistical inference on the associations of treatments with outcomes in challenging research areas to conduct randomized clinical trials. In contrast with randomized clinical trials, study participants’ characteristics in an observational study might not be balanced between treatment and control groups. Consequently, the treatment effect estimates may be biased when the treatment assignment is affected by imbalance in participants characteristics that are associated with outcomes. This type of bias is known as confounding bias. To minimize confounding bias, a statistical technique known as propensity score matching is frequently applied. Propensity score matching calculates the participants’ probability of receiving treatment given their observed covariates, called propensity scores, and match participants in the treatment and control groups with similar propensity scores.⁴ Statistical tests and regression models are conducted on the matched sample. Propensity score matching disentangles the distributions of participant characteristics from treatment assignment and in turn decreases or eliminates confounding bias in the treatment effects estimates. However, propensity score matching can result in a substantial reduction in sample size.

Weights to control confounding bias can also be generated from propensity scores. Unlike matching, propensity score weighting does not reduce the original sample size. A common approach is the IPTW method. In the IPTW method, weights are assigned to participants based on the inverse of their propensity score. The IPTW assigns a smaller weight to participants with a high propensity score and a larger weight to participants with a low propensity score, creating more balance in participant covariates. While the IPTW enhances the effectiveness of confounding control, it underestimates the variability of the treatment effect estimates and inflates the type I error rate. An improvement of precision of estimated treatment effects from the IPTW in observational studies is the use of SWs.⁴ SWs are calculated by multiplying the IPTW weights with the probability of receiving treatment without considering covariates, which reduces the weights of either treated participants with low propensity scores or untreated participants with high propensity scores. As a result, SWs more appropriately estimate the variability of the treatment effect estimates and maintain a less inflated type I error rate. The use of SWs not only improves precision in treatment effect estimates but also overcomes the drawbacks of the IPTW. SWs is a useful technique to balance covariates between groups in observational studies, as Guglielminotti et al³ demonstrated in their sensitivity analysis.

It has long been demonstrated that neuraxial anesthesia in surgical procedures, with or without general anesthesia, has beneficial effects compared with no neuraxial anesthesia, through a reduction in risks of deep vein thrombosis, pulmonary embolus, transfusion requirements, and kidney failure⁵; the contributing mechanisms are likely multifactorial, but may include improved regional blood flow, mitigating hypercoagulation, or reduced surgical stress response. It is feasible that the study by Guglielminotti et al³ likewise demonstrates a beneficial association of neuraxial labor analgesia for vaginal delivery with SMM outcomes in the obstetrical population. Indeed, reducing SMM by reducing the risk of PPH is akin to historical studies in which patients who received neuraxial anesthesia had decreased surgical blood loss compared with those who received general anesthesia.⁶ The postulated mechanism involves reduced central and peripheral venous pressures. While these historical studies used general anesthesia as the comparator, the association of neuraxial labor anesthesia for vaginal delivery with reduced surgical blood loss and PPH may be analogous. Hence, Guglielminotti et al³ used a mediation analysis to explore a potential mediating association of PPH in the association between neuraxial labor analgesia and SMM. Mediation analysis examines whether the association between 2 variables can be explained by a third intermediate variable, often referred as a mediator variable. In the context of health care research, mediator variables interact with the association between the treatment and the outcome, explaining all or part of the association of the treatment with the outcome. Guglielminotti et al³ found that that greater than one fifth of the association of neuraxial labor analgesia with SMM was mediated by the reduced risk of PPH.³

The incidence of SMM has been increasing with time in the US.¹ However, a real-world problem is the disparity in maternal morbidity and mortality rates observed across the world⁷ and also access to neuraxial labor analgesia for vaginal delivery, as Guglielminotti and colleagues alluded to, which may preclude our ability to generalize their findings to populations outside of the US. Hence the beneficial association of neuraxial labor analgesia for vaginal delivery with SMM may be exaggerated in the population studied.

Neuraxial labor analgesia for vaginal delivery provides a relatively safe intervention that can improve the maternal and overall parental birthing experience by alleviating discomfort and distress. When weighing the risks and benefits of neuraxial labor analgesia for vaginal delivery in clinical provision of care, practitioners need to be aware of the rare but significant risks associated with neuraxial techniques, such as neuraxial hemorrhage, infection, and neurologic injury. Neuraxial anesthesia also has the potential to produce hemodynamic alterations that necessitate clinical monitoring and supervision.

The last factor at health system level, which may have substantial impact on the benefit of neuraxial labor analgesia for vaginal delivery, is its accessibility. For instance, socioeconomic factors and insurance coverage may be barriers to patient access to neuraxial anesthesia despite its associations with improved SMM outcomes. To echo Guglielminotti and colleagues,³ increasing access to neuraxial labor analgesia for vaginal delivery may improve maternal health outcomes and may be a strategy well worth pursuing in public health policy.

Accepted for Publication: December 21, 2021.

Published: February 22, 2022. doi:10.1001/jamanetworkopen.2022.0142

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2022 Pankiv E et al. JAMA Network Open.

Corresponding Author: Kazuyoshi Aoyama, MD, PhD, Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, 555 University Ave, Ste 2211, Toronto, ON M5G 1X8, Canada (kazu.aoyama@utoronto.ca).

Conflict of Interest Disclosures: Dr Aoyama reported receiving grants from the Perioperative Services Facilitator Grant Program and Hospital for Sick Children. No other disclosures were reported.