Summary NHS England recently mandated that the National Early Warning Score of vital signs be used in all acute hospital trusts in the UK despite limited validation in the postoperative setting. We undertook a multicentre UK study of 13,631 patients discharged from intensive care after risk‐stratified cardiac surgery in four centres, all of which used VitalPACTM to electronically collect postoperative National Early Warning Score vital signs. We analysed 540,127 sets of vital signs to generate a logistic score, the discrimination of which we compared with the national additive score for the composite outcome of: in‐hospital death; cardiac arrest; or unplanned intensive care admission. There were 578 patients (4.2%) with an outcome that followed 4300 sets of observations (0.8%) in the preceding 24 h: 499 out of 578 (86%) patients had unplanned re‐admissions to intensive care. Discrimination by the logistic score was significantly better than the additive score. Respective areas (95%CI) under the receiver‐operating characteristic curve with 24‐h and 6‐h vital signs were: 0.779 (0.771–0.786) vs. 0.754 (0.746–0.761), p < 0.001; and 0.841 (0.829–0.853) vs. 0.813 (0.800–0.825), p < 0.001, respectively. Our proposed logistic Early Warning Score was better than the current National Early Warning Score at discriminating patients who had an event after cardiac surgery from those who did not.

Summary Maintaining safe elective surgical activity during the global coronavirus disease 2019 (COVID‐19) pandemic is challenging and it is not clear how COVID‐19 may impact peri‐operative morbidity and mortality in this population. Therefore, adaptations to normal care pathways are required. Here, we establish if implementation of a bespoke peri‐operative care bundle for urgent elective surgery during a pandemic surge period can deliver a low COVID‐19‐associated complication profile. We present a single‐centre retrospective cohort study from a tertiary care hospital of patients planned for urgent elective surgery during the initial COVID‐19 surge in the UK between 29 March and 12 June 2020. Patients asymptomatic for COVID‐19 were screened by oronasal swab and chest imaging (chest X‐ray or computed tomography if aged ≥ 18 years), proceeding to surgery if negative. COVID‐19 positive patients at screening were delayed. Postoperatively, patients transitioning to COVID‐19 positive status by reverse transcriptase polymerase chain reaction testing were identified by an in‐house tracking system and monitored for complications and death within 30 days of surgery. Out of 557 patients referred for surgery (230 (41.3%) women; median (IQR [range]) age 61 (48–72 [1–89])), 535 patients (96%) had COVID‐19 screening, of which 13 were positive (2.4%, 95%CI 1.4–4.1%). Out of 512 patients subsequently undergoing surgery, 7 (1.4%) developed COVID‐19 positive status (1.4%, 95%CI 0.7–2.8%) with one COVID‐19‐related death (0.2%, 95%CI 0.0–1.1%) within 30 days. Out of these seven patients, four developed pneumonia, of which two required invasive ventilation including one patient with acute respiratory distress syndrome. Low rates of COVID‐19 infection and mortality in the elective surgical population can be achieved within a targeted care bundle. This should provide reassurance that elective surgery can continue, where possible, despite high community rates of COVID‐19.

Aims: International early warning scores (EWS) including the additive National Early Warning Score (NEWS) and logistic EWS currently utilise physiological snapshots to predict clinical deterioration. We hypothesised that a dynamic score including vital sign trajectory would improve discriminatory power. Methods: Multicentre retrospective analysis of electronic health record data from postoperative patients admitted to cardiac surgical wards in four UK hospitals. Least absolute shrinkage and selection operator-type regression (LASSO) was used to develop a dynamic model (DyniEWS) to predict a composite adverse event of cardiac arrest, unplanned intensive care re-admission or in-hospital death within 24 h. Results: A total of 13,319 postoperative adult cardiac patients contributed 442,461 observations of which 4234 (0.96%) adverse events in 24 h were recorded. The new dynamic model (AUC = 0.80 [95% CI 0.78À0.83], AUPRC = 0.12 [0.10À0.14]) outperforms both an updated snapshot logistic model (AUC = 0.76 [0.73À0.79], AUPRC = 0.08 [0.60À0.10]) and the additive National Early Warning Score (AUC = 0.73 [0.70À0.76], AUPRC = 0.05 [0.02 À0.08]). Controlling for the false alarm rates to be at current levels using NEWS cutoffs of 5 and 7, DyniEWS delivers a 7% improvement in balanced accuracy and increased sensitivities from 41% to 54% at NEWS 5 and 18%À30% at NEWS 7. Conclusions: Using an advanced statistical approach, we created a model that can detect dynamic changes in risk of unplanned readmission to intensive care, cardiac arrest or in-hospital mortality and can be used in real time to risk-prioritise clinical workload.

Cardiac surgical patients deteriorate for a number of different reasons that often require thorough investigation including transoesophageal echocardiography to direct management. Most cases of cardiac arrest following cardiac surgery are reversible. Initial management of postoperative cardiac arrest should focus on immediate defibrillation and reversible causes before resternotomy within 5 minutes. Avoidance of intravenous adrenaline is recommended. Outcomes of cardiac arrest after cardiac surgery are favourable and result from a unified and protocolised management in such situations.

Airway management, particularly in non-theatre settings, is an area of anaesthesia and critical care associated with significant risk of morbidity & mortality, as highlighted during the 4th National Audit Project of the Royal College of Anaesthetists (NAP4). A survey of junior anaesthetists at our hospital highlighted a lack of confidence and perceived lack of safety in emergency airway management, especially in non-theatre settings.We developed and implemented a multifaceted airway package designed to improve the safety of remote site airway management. A Rapid Sequence Induction (RSI) checklist was developed; this was combined with new advanced airway equipment and drugs bags. Additionally, new carbon dioxide detector filters were procured in order to comply with NAP4 monitoring recommendations.The RSI checklists were placed in key locations throughout the hospital and the drugs and advanced airway equipment bags were centralised in the Intensive Care Unit (ICU). It was agreed with the senior nursing staff that an appropriately trained ICU nurse would attend all emergency situations with new airway resources upon request. Departmental guidelines were updated to include details of the new resources and the on-call anaesthetist's responsibilities regarding checks and maintenance.Following our intervention trainees reported higher confidence levels regarding remote site emergency airway management. Nine trusts within the Northern Region were surveyed and we found large variations in the provision of remote site airway management resources.Complications in remote site airway management due lack of available appropriate drugs, equipment or trained staff are potentially life threatening and completely avoidable. Utilising the intervention package an anaesthetist would be able to safely plan and prepare for airway management in any setting. They would subsequently have the drugs, equipment, and trained assistance required to manage any difficulties or complications. We suggest that this should be the gold standard of airway resource provision and is in line with NAP4 recommendations.