Obstetric anaesthesia: Overview + SIG resources

Abstract
Pain management in pregnant and postpartum people with an opioid-use disorder (OUD) requires a balance between risks associated with opioid tolerance, including withdrawal or return to opioid use, considerations around social needs of the maternal-infant dyad, and the provision of adequate pain relief for the birth episode that is often characterized as the worst pain a person will experience in their lifetime. This multidisciplinary consensus statement between the Society for Obstetric Anesthesia and Perinatology (SOAP), Society for Maternal-Fetal Medicine (SMFM), and American Society of Regional Anesthesia and Pain Medicine (ASRA) provides a framework for pain management in obstetric patients with OUD. The purpose of this consensus statement is to provide practical and evidence-based recommendations and is targeted to health care providers in obstetrics and anesthesiology. The statement is focused on prenatal optimization of pain management, labor analgesia, and postvaginal delivery pain management, and postcesarean delivery pain management. Topics include a discussion of nonpharmacologic and pharmacologic options for pain management, medication management for OUD (eg, buprenorphine, methadone), considerations regarding urine drug testing, and other social aspects of care for maternal-infant dyads, as well as a review of current practices. The authors provide evidence-based recommendations to optimize pain management while reducing risks and complications associated with OUD in the peripartum period. Ultimately, this multidisciplinary consensus statement provides practical and concise clinical guidance to optimize pain management for people with OUD in the context of pregnancy to improve maternal and perinatal outcomes.

Abstract
Background: Hypertensive disorders of pregnancy (HDP) are a major contributor to maternal morbidity, mortality, and accelerated cardiovascular (CV) disease. Comorbid conditions are likely important predictors of CV risk in pregnant people. Currently, there is no way to predict which people with HDP are at risk of acute CV complications. We developed and validated a predictive model for all CV events and for heart failure, renal failure, and cerebrovascular events specifically after HDP.

Methods: Models were created using the Premier Healthcare Database. The inclusion criteria for the model dataset were delivery with an HDP with discharge from October 1, 2015 to December 31, 2020. Machine learning methods were used to derive predictive models of CV events occurring during delivery hospitalization (Index Model) or during readmission (Readmission Model) using a training set (60%) to estimate model parameters, a validation set (20%) to tune model hyperparameters and select a final model, and a test set (20%) to evaluate final model performance.

Results: The total model cohort consisted of 553,658 deliveries with an HDP. A CV event occurred in 6501 (1.2%) of the delivery hospitalizations. Multilabel neural networks were selected for the Index Model and Readmission Model due to favorable performance compared to alternatives. This approach is designed for prediction of multiple events that share risk factors and may cooccur. The Index Model predicted all CV events with area under the receiver operating curve (AUROC) 0.878 and average precision (AP) 0.239 (cerebrovascular events: AUROC 0.941, heart failure: AUROC 0.898, and renal failure: AUROC 0.885). With a positivity threshold set to achieve ≥90% sensitivity, model specificity was 65.0%, 83.5%, 68.6%, and 65.6% for predicting all CV events, cerebrovascular events, heart failure, and renal failure, respectively. CV events within 1 year of delivery occurred in 3018 (0.6%) individuals. The Readmission Model predicted all CV events with AUROC 0.717 and AP 0.022 (renal failure: AUROC 0.748, heart failure: AUROC 0.734, and cerebrovascular events AUROC 0.698). Feature importance analysis indicated that the presence of chronic renal disease, cardiac disease, pulmonary hypertension, and preeclampsia with severe features had the greatest effect on the prediction of CV events.

Conclusions: Among individuals with HDP, our multilabel neural network model predicted CV events at delivery admission with good classification and events within 1 year of delivery with fair classification.

Abstract in English, French
Purpose: To determine the optimal programmed intermittent epidural bolus (PIEB) volume for providing effective analgesia in 90% of patients (EV90) during the first stage of labour using the dural puncture epidural (DPE) technique to initiate labour analgesia.

Methods: We conducted a biased-coin up-and-down sequential allocation study. We included 40 nulliparous women requiring epidural labour analgesia. We used a 25G Whitacre spinal needle to puncture the dural mater and then administered a loading dose of 12 mL of ropivacaine 0.1% and sufentanil 0.3 μg·mL-1. Subsequently, the PIEB pump delivered boluses with an identical solution at a fixed 40-min interval, starting 1 hr after epidural initiation. The bolus volume for the first patient was 7 mL and was adjusted for subsequent patients according to the study protocol (bolus volume, 7-12 mL). The primary endpoint was effective analgesia, indicated by no need for patient-controlled or manual boluses within 6 hr after analgesia initiation or until complete cervical dilation, whichever came first. Outcome evaluators assessed the patients' pain ratings, Bromage scores, sensory blockade level, and maternal blood pressure hourly.

Results: Using the truncated Dixon and Mood method, the estimated EV90 was 9.2 mL (95% confidence interval [CI], 8.5 to 9.9) whereas the isotonic regression method yielded a value of 8.8 mL (95% CI, 8.6 to 9.8). None of the patients experienced a motor block. Two patients experienced hypotension without the need for vasopressors.

Conclusions: The estimated PIEB EV90 for ropivacaine 0.1% and sufentanil 0.3 μg·mL-1 approached 9 mL when DPE was combined with a fixed 40-min interval.

Study registration: ChiCTR.org.cn ( ChiCTR2300067281 ); first submitted 3 January 2023.

Keywords: dural puncture epidural; effective bolus volume; labour analgesia; programmed intermittent epidural bolus.