Ethnic disparities in obstetric anaesthetic care

Authors: Mike Charlesworth and Andrew Klein

#TheAnesthesiaBlog July 2023

The association between ethnicity and outcomes in obstetrics is well known, but there has been little published about its relationship with obstetric anaesthetic care. This new national cohort study of women who gave birth between 2011–2021 from Bamber et al. finds differences between ethnic groups in rates of general anaesthesia received by women who had caesarean births and in rates of neuraxial anaesthesia received by women who had vaginal births (Fig. 1). In the associated editorial, Lee and Palanisamy examine parallels and paradoxes with care in the USA. Although this is the first study of this kind outside the USA, the overall themes seem to be pervasive. Some may suggest the two healthcare systems are different for many reasons, but it is argued instead that there are similar factors at play requiring similar solutions.

The New Zealand Early Warning Score (NZEWS) is a blended EWS system developed in Wellington Regional Hospital, Wellington, New Zealand and subsequently implemented in every public and private hospital as part of the national patient deterioration programme. The primary objective of this new study from Mohan et al. was to validate the ability of NZEWS to predict serious adverse advents. They found that NZEWS is similar to NEWS in discriminating between patients at risk of serious adverse events, and that blended NZEWS system is safe, accurate and fit for purpose. In the associated editorial, Murali and Inada-Kim highlight the global picture as well as the use of single parameters vs. aggregate scoring. They argue that aggregate rather than blended early warning scores should remain the method of choice for the detection of the deteriorating patient in hospitals.

Is it possible to predict pain after major surgery? This new secondary analysis from Armstrong et al. and the PQIP delivery team shows the development and validation of a prediction model for severe pain on postoperative day 1 after major, non-cardiac surgery which utilises only pre-operative patient data. This is the first attempt to systematically develop a peri-operative pain prediction model using such a large, high-quality dataset in a mixed surgical population (Fig. 3). In the associated editorial, Abdallah et al. ask whether it is time to move beyond the ‘kitchen-sink’ approach for postoperative pain management. They argue that truly personalised pain management requires perfect prediction: the ability to predict who, among a group of patients undergoing the same surgical intervention, will proceed to develop moderate-to-severe pain, who will specifically benefit from a more inclusive approach to selecting analgesic modalities and who will benefit from longer vs. shorter duration of initial postoperative pain treatment.

There is growing interest in how artificial intelligence will affect our lives in the future, but can machine learning predict myocardial injury and death after cardiac surgery? This new secondary analysis of the VISION study from Nolde et al. finds that it can, and that most discriminatory information was provided by pre-operative patient characteristics, with discrimination increased by pre-operative investigations and peri-operative variables. Two editorials provide important context to work published earlier in the year. First, Shanthanna discussed risk factors and prediction modelling for chronic post-surgical pain after breast cancer surgery. Importantly, chronic post-surgical pain is an important health concern for nearly 64% of women having breast cancer surgery. It is crucial therefore to precisely define the outcome being measured and understand the associated complexity. Second, Wiles comments on a consensus statement that was endorsed by multiple specialty bodies on the use of CT as an ancillary investigation to support a diagnosis of death using neurological criteria. He argues it is an investigation that will be used more frequently and that the guideline will help maintain public and clinician confidence in diagnosing neurological death. Elsewhere we have reviews of renal medicine, liver care and haematological malignancies in ICU, and two brand new Reviewer Recommendations looking at survey-based research and engaging in social media.