The recognition of stemi by paramedics and the effect of computer interpretation (respect): a cluster-randomised crossover pilot study.

BACKGROUND: Timely diagnosis and appropriate management of patients with ST-segment elevation myocardial infarction (STEMI) depends on accurate interpretation of the 12-lead ECG by paramedics. Computer interpretation messages on ECGs are often provided, but the effect they exert on paramedics' decision making is not known. The objective of this pilot study was to assess the feasibility of a trial using an online assessment tool, to determine the effect of computer interpretation messages on paramedics' diagnosis of STEMI. METHODS: The RESPECT pilot study is a cluster-randomised crossover trial using a bespoke, web-based assessment tool. Participants were randomly allocated 12 of 48 ECGs, with an equal mix of correct and incorrect computer interpretation messages, and STEMI and STEMI-mimics. These were viewed in two phases, with message visibility reversed between phases. RESULTS: 254 paramedics consented into the study, 205 completing the first phase and 150 completing phase two. The data were analysed in two subsets: computer message correct and computer message incorrect. In the subset of correct computer interpretations, accuracy was 84% (message hidden) and 87% (message visible). The subset of incorrect computer interpretations resulted in an accuracy of 77% (message hidden) and 71% (message visible). For the subset of correct computer interpretation, the unadjusted OR was 1.31 (95% CI 1.01-1.71), adjusted OR, 1.80 (95% CI 0.84-4.80), the ICC for participants was 0.36 and for ECGs, 0.18 Incorrect computer interpretations had an unadjusted OR of 0.76 (95% CI 0.61-0.93), adjusted OR, 0.58 (95% CI 0.41-0.80), the ICC for participants was 0.06 and for ECGs, 0.01. CONCLUSION: A randomised crossover trial to determine the effect of computer interpretation messages is feasible. Pilot data have provided an indication about expected rates of discordance and suggest that incorrect computer messages have a stronger influence across participants and ECGs

What influences ambulance clinician decisions to pre-alert emergency departments: a qualitative exploration of pre-alert practice in UK ambulance services and emergency departments

Background Ambulance clinicians use pre-alerts to inform receiving hospitals of the imminent arrival of a time-critical patient considered to require immediate attention, enabling the receiving emergency department (ED) or other clinical area to prepare. Pre-alerts are key to ensuring immediate access to appropriate care, but unnecessary pre-alerts can divert resources from other patients and fuel ‘pre-alert fatigue’ among ED staff. This research aims to provide a better understanding of pre-alert decision-making practice. Methods Semi-structured interviews were conducted with 34 ambulance clinicians from three ambulance services and 40 ED staff from six receiving EDs. Observation (162 hours) of responses to pre-alerts (n=143, call-to-handover) was also conducted in the six EDs. Interview transcripts and observation notes were imported into NVIVO and analysed using thematic analysis. Findings Pre-alert decisions involve rapid assessment of clinical risk based on physiological observations, clinical judgement and perceived risk of deterioration, with reference to pre-alert guidance. Clinical experience (pattern recognition and intuition) and confidence helped ambulance clinicians to understand which patients required immediate ED care on arrival or were at highest risk of deterioration. Ambulance clinicians primarily learnt to pre-alert ‘on the job’ and via informal feedback mechanisms, including the ED response to previous pre-alerts. Availability and access to clinical decision support was variable, and clinicians balanced the use of guidance and protocols with concerns about retention of clinical judgement and autonomy. Differences in pre-alert criteria between ambulance services and EDs created difficulties in deciding whether to pre-alert and was particularly challenging for less experienced clinicians. Conclusion We identified potentially avoidable variation in decision-making, which has implications for patient care and emergency care resources, and can create tension between the services. Consistency in practice may be improved by greater standardisation of guidance and protocols, training and access to performance feedback and cross-service collaboration to minimise potential sources of tension

An analysis of NHS 111 demand for primary care services: a retrospective cohort study

The NHS 111 service triages over 16,650,745 calls per year and approximately 48% of callers are triaged to a primary care disposition, such as a telephone appointment with a general practitioner (GP). However, there has been little assessment of the ability of primary care services to meet this demand. If a timely service cannot be provided to patients, it could result in patients calling 999 or attending emergency departments (ED) instead. This study aimed to explore the patient journey for callers who were triaged to a primary care disposition, and the ability of primary care services to meet this demand. We obtained routine, retrospective data from the Connected Yorkshire research database, and identified all 111 calls between the 1st January 2021 and 31st December 2021 for callers registered with a GP in the Bradford or Airedale region of West Yorkshire, who were triaged to a primary care disposition. Subsequent healthcare system access (111, 999, primary and secondary care) in the 72 hours following the index 111 call was identified, and a descriptive analysis of the healthcare trajectory of patients was undertaken. There were 56,102 index 111 calls, and a primary care service was the first interaction in 26,690/56,102 (47.6%) of cases, with 15,470/26,690 (58%) commenced within the specified triage time frame. Calls to 999 were higher in the cohort who had no prior contact with primary care (58% vs 42%) as were ED attendances (58.2% vs 41.8), although the proportion of avoidable ED attendances was similar (10.5% vs 11.8%). Less than half of 111 callers triaged to a primary care disposition make contact with a primary care service, and even when they do, call triage time frames are frequently not met, suggesting that current primary care provision cannot meet the demand from 111

The reality of advanced airway management during out of hospital cardiac arrest; why did paramedics deviate from their allocated airway management strategy during the AIRWAYS-2 randomised trial?

Background: AIRWAYS-2 was a large multi-centre cluster randomised controlled trial investigating the effect on functional outcome of a supraglot-tic airway device (i-gel) versus tracheal intubation (TI) as the initial advanced airway during out-of-hospital cardiac arrest. We aimed to understand why paramedics deviated from their allocated airway management algorithm during AIRWAYS-2. Methods: This study employed a pragmatic sequential explanatory design utilising retrospective study data collected during the AIRWAYS-2 trial. Airway algorithm deviation data were analysed to categorise and quantify the reasons why paramedics did not follow their allocated strategy of airway management during AIRWAYS-2. Recorded free text entries provided additional context to the paramedic decision-making related to each category identified. Results: In 680 (11.7%) of 5800 patients the study paramedic did not follow their allocated airway management algorithm. There was a higher percentage of deviations in the TI group (399/2707; 14.7%) compared to the i-gel group (281/3088; 9.1%). The predominant reason for a paramedic not following their allocated airway management strategy was airway obstruction, occurring more commonly in the i-gel group (109/281; 38.7%) versus (50/399; 12.5%) in the TI group. Conclusion: There was a higher proportion of deviations from the allocated airway management algorithm in the TI group (399; 14.7%) compared to the i-gel group (281; 9.1%). The most frequent reason for deviating from the allocated airway management algorithm in AIRWAYS-2 was obstruction of the patient's airway by fluid. This occurred in both groups of the AIRWAYS-2 trial, but was more frequent in the i-gel group

Supporting the ambulance service to safely convey fewer patients to hospital by developing a risk prediction tool: Risk of Adverse Outcomes after a Suspected Seizure (RADOSS)—protocol for the mixed-methods observational RADOSS project

Introduction Ambulances services are asked to further reduce avoidable conveyances to emergency departments (EDs). Risk of Adverse Outcomes after a Suspected Seizure seeks to support this by: (1) clarifying the risks of conveyance and non-conveyance, and (2) developing a risk prediction tool for clinicians to use ‘on scene’ to estimate the benefits an individual would receive if conveyed to ED and risks if not. Methods and analysis Mixed-methods, multi-work package (WP) project. For WP1 and WP2 we shall use an existing linked data set that tracks urgent and emergency care (UEC) use of persons served by one English regional ambulance service. Risk tools are specific to clinical scenarios. We shall use suspected seizures in adults as an exemplar. WP1: Form a cohort of patients cared for a seizure by the service during 2019/2020. It, and nested Knowledge Exchange workshops with clinicians and service users, will allow us to: determine the proportions following conveyance and non-conveyance that die and/or recontact UEC system within 3 (/30) days; quantify the proportion of conveyed incidents resulting in ‘avoidable ED attendances’ (AA); optimise risk tool development; and develop statistical models that, using information available ‘on scene’, predict the risk of death/recontact with the UEC system within 3 (/30) days and the likelihood of an attendance at ED resulting in an AA. WP2: Form a cohort of patients cared for a seizure during 2021/2022 to ‘temporally’ validate the WP1 predictive models. WP3: Complete the ‘next steps’ workshops with stakeholders. Using nominal group techniques, finalise plans to develop the risk tool for clinical use and its evaluation. Ethics and dissemination WP1a and WP2 will be conducted under database ethical approval (IRAS 307353) and Confidentiality Advisory Group (22/CAG/0019) approval. WP1b and WP3 have approval from the University of Liverpool Central Research Ethics Committee (11450). We shall engage in proactive dissemination and knowledge mobilisation to share findings with stakeholders and maximise evidence usage

Research paramedics’ observations regarding the challenges and strategies employed in the implementation of a large-scale out-of-hospital randomised trial

Archiving policy is unclear, 12 months embargo applied temporarily.Introduction: AIRWAYS-2 was a cluster randomised controlled trial (RCT) comparing the clinical and cost effectiveness of the i-gel supraglottic airway device with tracheal intubation in the initial airway management of out-of-hospital cardiac arrest (OHCA). In order to successfully conduct this clinical trial, it was necessary for research paramedics to overcome multiple challenges, many of which will be relevant to future emergency medical service (EMS) research. This article aims to describe a number of the challenges that were encountered during the out-of-hospital phase of the AIRWAYS-2 trial and how these were overcome.Methods: The research paramedics responsible for conducting the pre-hospital phase of the trial were asked to reflect on their experience of facilitating the AIRWAYS-2 trial. Responses were then collated by the lead author. A process of iterative revision and review was undertaken by the research paramedics to produce a consensus of opinion.Results: The main challenges identified by the trial research paramedics related to the recruitment and training of paramedics, screening of eligible patients and investigation of protocol deviations / reporting errors. Even though a feasibility study was conducted prior to the commencement of AIRWAYS-2, the scale of these challenges was underestimated.Conclusion: Large-scale pragmatic cluster randomised trials are being successfully undertaken in out-of-hospital care. However, they require intensive engagement with EMS clinicians and local research paramedics, particularly when the intervention is contentious. Feasibility studies are an important part of research but may fail to identify all potential challenges. Therefore, flexibility is required to manage unforeseen difficulties.</jats:p

Prognostic accuracy of triage tools for adults with suspected COVID-19 in a pre-hospital setting : an observational cohort study

Study Objective: Tools proposed to triage patient acuity in COVID-19 infection have only been validated in hospital populations. We estimated the accuracy of five risk-stratification tools recommended to predict severe illness and compare accuracy to existing clinical decision-making in a pre-hospital setting. Methods: An observational cohort study using linked ambulance service data for patients attended by EMS crews in the Yorkshire and Humber region of England between 18th March 2020 and 29th June 2020 was conducted to assess performance of the PRIEST tool, NEWS2, the WHO algorithm, CRB-65 and PMEWS in patients with suspected COVID-19 infection. The primary outcome was death or need for organ support. Results: Of 7549 patients in our cohort, 17.6% (95% CI:16.8% to 18.5%) experienced the primary outcome. The NEWS2, PMEWS, PRIEST tool and WHO algorithm identified patients at risk of adverse outcomes with a high sensitivity (>0.95) and specificity ranging from 0.3 (NEWS2) to 0.41 (PRIEST tool). The high sensitivity of NEWS2 and PMEWS was achieved by using lower thresholds than previously recommended. On index assessment, 65% of patients were transported to hospital and EMS decision to transfer patients achieved a sensitivity of 0.84 (95% CI 0.83 to 0.85) and specificity of 0.39 (95% CI 0.39 to 0.40). Conclusion: Use of NEWS2, PMEWS, PRIEST tool and WHO algorithm could improve sensitivity of EMS triage of patients with suspected COVID-19 infection. Use of the PRIEST tool would improve sensitivity of triage without increasing the number of patients conveyed to hospital

Pre-hospital prediction of adverse outcomes in patients with suspected COVID-19: Development, application and comparison of machine learning and deep learning methods

Background: COVID-19 infected millions of people and increased mortality worldwide. Patients with suspected COVID-19 utilised emergency medical services (EMS) and attended emergency departments, resulting in increased pressures and waiting times. Rapid and accurate decision-making is required to identify patients at high-risk of clinical deterioration following COVID-19 infection, whilst also avoiding unnecessary hospital admissions. Our study aimed to develop artificial intelligence models to predict adverse outcomes in suspected COVID-19 patients attended by EMS clinicians. Method: Linked ambulance service data were obtained for 7,549 adult patients with suspected COVID-19 infection attended by EMS clinicians in the Yorkshire and Humber region (England) from 18-03-2020 to 29-06-2020. We used support vector machines (SVM), extreme gradient boosting, artificial neural network (ANN) models, ensemble learning methods and logistic regression to predict the primary outcome (death or need for organ support within 30 days). Models were compared with two baselines: the decision made by EMS clinicians to convey patients to hospital, and the PRIEST clinical severity score. Results: Of the 7,549 patients attended by EMS clinicians, 1,330 (17.6%) experienced the primary outcome. Machine Learning methods showed slight improvements in sensitivity over baseline results. Further improvements were obtained using stacking ensemble methods, the best geometric mean (GM) results were obtained using SVM and ANN as base learners when maximising sensitivity and specificity. Conclusions: These methods could potentially reduce the numbers of patients conveyed to hospital without a concomitant increase in adverse outcomes. Further work is required to test the models externally and develop an automated system for use in clinical settings