Implementing enhanced extracorporeal membrane oxygenation for CPR (ECPR) in the emergency department.

Refractory out-of-hospital cardiac arrest (OHCA) has a very poor prognosis, with survival rates at around 10%. Extracorporeal membrane oxygenation (ECMO) for patients in refractory arrest, known as ECPR, aims to provide perfusion to the patient whilst the underlying cause of arrest can be addressed. ECPR use has increased substantially, with varying survival rates to hospital discharge. The best outcomes for ECPR occur when the time from cardiac arrest to implementation of ECPR is minimised. To reduce this time, systems must be in place to identify the correct patient, expedite transfer to hospital, facilitate rapid cannulation and ECMO circuit flows. We describe the process of activation of ECPR, patient selection, and the steps that emergency department clinicians can utilise to facilitate timely cannulation to ensure the best outcomes for patients in refractory cardiac arrest. With these processes in place our survival to hospital discharge for OHCA patients is 35%, with most patients having a good neurological function.

The Emergency nurse Protocols Initiating Care-Sydney Triage to Admission Risk Tool (EPIC-START) trial: protocol for a stepped wedge implementation trial.

INTRODUCTION: Emergency department (ED) overcrowding is a global problem and a threat to the quality and safety of emergency care. Providing timely and safe emergency care therein is challenging. To address this in New South Wales (NSW), Australia, the Emergency nurse Protocol Initiating Care-Sydney Triage to Admission Risk Tool (EPIC-START) was developed. EPIC-START is a model of care incorporating EPIC protocols, the START patient admission prediction tool, and a clinical deterioration tool to support ED flow, timely care, and patient safety. The aim of this study is to evaluate the impact of EPIC-START implementation across 30 EDs on patient, implementation, and health service outcomes. METHODS AND ANALYSIS: This study protocol adopts an effectiveness-implementation hybrid design (Med Care 50: 217-226, 2012) and uses a stepped-wedge cluster randomised control trial of EPIC-START, including uptake and sustainability, within 30 EDs across four NSW local health districts spanning rural, regional, and metropolitan settings. Each cluster will be randomised independently of the research team to 1 of 4 dates until all EDs have been exposed to the intervention. Quantitative and qualitative evaluations will be conducted on data from medical records and routinely collected data, and patient, nursing, and medical staff pre- and post-surveys. ETHICS AND DISSEMINATION: Ethical approval for the research was received from the Sydney Local Health District Research Ethics Committee (Reference Number 2022/ETH01940) on 14 December 2022. TRIAL REGISTRATION: Australian and New Zealand Clinical trial, ACTRN12622001480774p. Registered on 27 October 2022.

Age-related trends in injury and injury severity presenting to emergency departments in New South Wales Australia: Implications for major injury surveillance and trauma systems.

OBJECTIVES: To describe population based trends and clinical characteristics of injury related presentations to Emergency Departments (EDs). DESIGN AND SETTING: A retrospective, descriptive analysis of de-identified linked ED data across New South Wales, Australia over five calendar years, from 2010 to 2014. PARTICIPANTS: Patients were included in this analysis if they presented to an Emergency Department and had an injury related diagnosis. Injury severity was categorised into critical (triage category 1-2 and admitted to ICU or operating theatre, or died in ED), serious (admitted as an in-patient, excluding above critical injuries) and minor injuries (discharged from ED). MAIN OUTCOME MEASURES: The outcomes of interest were rates of injury related presentations to EDs by age groups and injury severity. RESULTS: A total of 2.09 million injury related ED presentations were analysed. Minor injuries comprised 85.0%, and 14.1% and 1.0% were serious and critical injuries respectively. There was a 15.8% per annum increase in the rate of critical injuries per 1000 population in those 80 years and over, with the most common diagnosis being head injuries. Around 40% of those with critical injuries presented directly to a major trauma centre. CONCLUSION: Critical injuries in the elderly have risen dramatically in recent years. A minority of critical injuries present directly to major trauma centres. Trauma service provision models need revision to ensure appropriate patient care. Injury surveillance is needed to understand the external causes of injury presenting to hospital.

The Sydney Triage to Admission Risk Tool (START) to predict Emergency Department Disposition: A derivation and internal validation study using retrospective state-wide data from New South Wales, Australia.

BACKGROUND: Disposition decisions are critical to the functioning of Emergency Departments. The objectives of the present study were to derive and internally validate a prediction model for inpatient admission from the Emergency Department to assist with triage, patient flow and clinical decision making. METHODS: This was a retrospective analysis of State-wide Emergency Department data in New South Wales, Australia. Adult patients (age ≥ 16 years) were included if they presented to a Level five or six (tertiary level) Emergency Department in New South Wales, Australia between 2013 and 2014. The outcome of interest was in-patient admission from the Emergency Department. This included all admissions to short stay and medical assessment units and being transferred out to another hospital. Analyses were performed using logistic regression. Discrimination was assessed using area under curve and derived risk scores were plotted to assess calibration. RESULTS: 1,721,294 presentations from twenty three Level five or six hospitals were analysed. Of these 49.38% were male and the mean (sd) age was 49.85 years (22.13). Level 6 hospitals accounted for 47.70% of cases and 40.74% of cases were classified as an in-patient admission based on their mode of separation. The final multivariable model including age, arrival by ambulance, triage category, previous admission and presenting problem had an AUC of 0.82 (95% CI 0.81, 0.82). CONCLUSION: By deriving and internally validating a risk score model to predict the need for in-patient admission based on basic demographic and triage characteristics, patient flow in ED, clinical decision making and overall quality of care may be improved. Further studies are now required to establish clinical effectiveness of this risk score model.