Anaesthesia
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Acute postoperative pain is common, distressing and associated with increased morbidity. Targeted interventions can prevent its development. We aimed to develop and internally validate a predictive tool to pre-emptively identify patients at risk of severe pain following major surgery. ⋯ Discrimination was improved by the addition of intra-operative variables (likelihood ratio χ2 496.5, p < 0.001) but not by the addition of baseline opioid data. On internal validation, our pre-operative prediction model was well calibrated but discrimination was moderate. Performance was improved with the inclusion of peri-operative covariates suggesting pre-operative variables alone are not sufficient to adequately predict postoperative pain.
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Liver injury or failure is observed in up to 20% of patients admitted to the intensive care unit and is associated with poor prognosis. Timely recognition and initiation of appropriate management are the most important steps in minimising adverse outcome for patients. Distinguishing between primary or secondary liver failure, and between acute or chronic liver disease aids appropriate management. ⋯ We focus on interpretation of patterns of deranged liver biochemistry and the necessary investigations required to identify the related aetiologies. We also propose an evidence-based approach to the management of liver failure and its extrahepatic manifestations. This review, in addition, clarifies when to seek expert advice or refer patients to a tertiary centre.
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Myocardial injury due to ischaemia within 30 days of non-cardiac surgery is prognostically relevant. We aimed to determine the discrimination, calibration, accuracy, sensitivity and specificity of single-layer and multiple-layer neural networks for myocardial injury and death within 30 postoperative days. We analysed data from 24,589 participants in the Vascular Events in Non-cardiac Surgery Patients Cohort Evaluation study. ⋯ Discrimination for myocardial injury by single-layer vs. multiple-layer models generated areas (95%CI) under the receiver operating characteristic curve of: 0.70 (0.69-0.72) vs. 0.71 (0.70-0.73) with variables available before surgical referral, p < 0.001; 0.73 (0.72-0.75) vs. 0.75 (0.74-0.76) with additional variables available on admission, but before surgery, p < 0.001; and 0.76 (0.75-0.77) vs. 0.77 (0.76-0.78) with the addition of subsequent variables, p < 0.001. Discrimination for death by single-layer vs. multiple-layer models generated areas (95%CI) under the receiver operating characteristic curve of: 0.71 (0.66-0.76) vs. 0.74 (0.71-0.77) with variables available before surgical referral, p = 0.04; 0.78 (0.73-0.82) vs. 0.83 (0.79-0.86) with additional variables available on admission but before surgery, p = 0.01; and 0.87 (0.83-0.89) vs. 0.87 (0.85-0.90) with the addition of subsequent variables, p = 0.52. The accuracy of the multiple-layer model for myocardial injury and death with all variables was 70% and 89%, respectively.
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Aotearoa New Zealand uses a single early warning score (EWS) across all public and private hospitals to detect adult inpatient physiological deterioration. This combines the aggregate weighted scoring of the UK National Early Warning Score with single parameter activation from Australian medical emergency team systems. We conducted a retrospective analysis of a large vital sign dataset to validate the predictive performance of the New Zealand EWS in discriminating between patients at risk of serious adverse events and compared this with the UK EWS. ⋯ Area under the receiver operating characteristic curve for both EWSs for any adverse outcome was 0.874 (95%CI 0.871-0.878) and 0.874 (95%CI 0.870-0.877), respectively. Both EWSs showed superior predictive value for cardiac arrest and/or death in patients admitted under surgical rather than medical specialties. Our study is the first validation of the New Zealand EWS in predicting serious adverse events in a broad dataset and supports previous work showing the UK EWS has superior predictive performance in surgical rather than medical patients.
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Observational Study
A national cohort study to investigate the association between ethnicity and the provision of care in obstetric anaesthesia in England between 2011 and 2021.
There is evidence that ethnic inequalities exist in maternity care in the UK, but those specifically in relation to UK obstetric anaesthetic care have not been investigated before. Using routine national maternity data for England (Hospital Episode Statistics Admitted Patient Care) collected between March 2011 and February 2021, we investigated ethnic differences in obstetric anaesthetic care. Anaesthetic care was identified using OPCS classification of interventions and procedures codes. ⋯ For women giving birth vaginally (excluding assisted vaginal births), Bangladeshi (Asian or Asian British), Pakistani (Asian or Asian British) and Caribbean (black or black British) women were, respectively, 24% (0.76 [0.74-0.78]), 15% (0.85 [0.84-0.87]) and 8% (0.92 [0.89-0.94]) less likely than British (white) women to receive neuraxial anaesthesia. This observational study cannot determine the causes for these disparities, which may include unaccounted confounders. Our findings merit further research to investigate potentially remediable factors such as inequality of access to appropriate obstetric anaesthetic care.