Recognition of out-of-hospital cardiac arrest by medical dispatchers in emergency medical dispatch centres in two countries

2024 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations: Summary From the Basic Life Support; Advanced Life Support; Pediatric Life Support; Neonatal Life Support; Education, Implementation, and Teams; and First Aid Task Forces

This is the eighth annual summary of the International Liaison Committee on Resuscitation International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations; a more comprehensive review was done in 2020. This latest summary addresses the most recent published resuscitation evidence reviewed by the International Liaison Committee on Resuscitation task force science experts. Members from 6 International Liaison Committee on Resuscitation task forces have assessed, discussed, and debated the quality of the evidence, using Grading of Recommendations Assessment, Development, and Evaluation criteria, and their statements include consensus treatment recommendations. Insights into the deliberations of the task forces are provided in the Justification and Evidence-to-Decision Framework Highlights sections. In addition, the task forces list priority knowledge gaps for further research.

Optimising telecommunicator recognition of out-of-hospital cardiac arrest: A scoping review

Aim

To summarize existing literature and identify knowledge gaps regarding barriers and enablers of telecommunicators' recognition of out-of-hospital cardiac arrest (OHCA).

Methods

This scoping review was undertaken by an International Liaison Committee on Resuscitation (ILCOR) Basic Life Support scoping review team and guided by the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR). Studies were eligible for inclusion if they were peer-reviewed and explored barriers and enablers of telecommunicator recognition of OHCA. We searched Ovid MEDLINE® and Embase and included articles from database inception till June 18th, 2024.

Results

We screened 9,244 studies and included 62 eligible studies on telecommunicator recognition of OHCA. The studies ranged in methodology. The majority were observational studies of emergency calls. The barriers most frequently described to OHCA recognition were breathing status and agonal breathing. The most frequently tested enabler for recognition was a variety of dispatch protocols focusing on breathing assessment. Only one randomized controlled trial (RCT) was identified, which found no difference in OHCA recognition with the addition of machine learning alerting telecommunicators in suspected OHCA cases.

Conclusion

Most studies were observational, assessed barriers to recognition of OHCA and compared different dispatch protocols. Only one RCT was identified. Randomized trials should be conducted to inform how to improve telecommunicator recognition of OHCA, including recognition of pediatric OHCAs and assessment of dispatch protocols.

2024 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations: Summary From the Basic Life Support; Advanced Life Support; Pediatric Life Support; Neonatal Life Support; Education, Implementation, and Teams; and First Aid Task Forces

This is the eighth annual summary of the International Liaison Committee on Resuscitation International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations; a more comprehensive review was done in 2020. This latest summary addresses the most recent published resuscitation evidence reviewed by the International Liaison Committee on Resuscitation task force science experts. Members from 6 International Liaison Committee on Resuscitation task forces have assessed, discussed, and debated the quality of the evidence, using Grading of Recommendations Assessment, Development, and Evaluation criteria, and their statements include consensus treatment recommendations. Insights into the deliberations of the task forces are provided in the Justification and Evidence-to-Decision Framework Highlights sections. In addition, the task forces list priority knowledge gaps for further research.

When the machine is wrong. Characteristics of true and false predictions of Out-of-Hospital Cardiac arrests in emergency calls using a machine-learning model

BACKGROUND

A machine-learning model trained to recognize emergency calls regarding Out-of-Hospital Cardiac Arrest (OHCA) was tested in clinical practice at Copenhagen Emergency Medical Services (EMS) from September 2018 to December 2019. We aimed to investigate emergency call characteristics where the machine-learning model failed to recognize OHCA or misinterpreted a call as being OHCA.

METHODS

All emergency calls were linked to the dispatch database and verified OHCAs were identified by linkage to the Danish Cardiac Arrest Registry. Calls with either false negative or false positive predictions of OHCA were evaluated by trained auditors. Descriptive analyses were performed with absolute numbers and percentages reported.

RESULTS

The machine-learning model processed 169,236 calls to Copenhagen EMS and suspected 5,811 (3.4%) of the calls as OHCA, resulting in 84.5% sensitivity and 97.1% specificity. Among OHCAs not recognised by machine-learning model, a condition completely different from OHCA was presented by caller in 31% of the cases. In 28% of unrecognised calls, patient was reported breathing normally, and language barriers were identified in 23% of the cases. Among falsely suspected OHCA, the patient was reported unconscious in 28% of the cases, and in 13% of the false positive cases the machine-learning model interpreted calls regarding dead patients with irreversible signs of death as OHCA.

CONCLUSION

Continuous optimization of the language model is needed to improve the prediction of OHCA and thereby improve sensitivity and specificity of the machine-learning model on recognising OHCA in emergency telephone calls.

Accuracy of dispatch and prehospital triage performance in poisonings - A retrospective study from northern Finland

BACKGROUND

Increasing numbers of dispatches place a burden on EMS; this study sought to assess the prehospital evaluation of poisoned patients transported to hospital. The primary aim of this study was to measure dispatch centre and EMS provider performance as well as factors contributing to the recognition of poisoning among prehospital patients. The secondary aim was to compare triage performance between dispatch centres and EMS providers.

METHODS

A retrospective single-centre study in Northern Finland was conducted. Patients suspected as poisonings by dispatch centres as well as other EMS-transported patients who received a diagnosis of poisoning in hospital between June 1, 2015 and June 1, 2017, were included.

RESULTS

There were a total of 1668 poisoning-related EMS missions. Dispatch centres suspected poisonings with sensitivity of 79.9% (95% CI 76.7-82.9) and specificity of 98.9% (95% CI 98.9-99.0) when all EMS missions were taken into account. In a logistic regression model, decreased state of consciousness as dispatch code (OR 7.18, 95% CI 1.90-27.05) and intravenous fluid resuscitation (OR 6.58, 95% CI 1.34-32.37) were associated with EMS transport providers not recognizing poisoning. Overtriage rate appeared significantly higher (33.6%, 95% CI 28.6-39.2) for dispatch when compared with transport (17.8%, 95% CI 13.9-22.6).

CONCLUSION

Dispatch centres seem to suspect poisonings fairly accurately. Poisonings unrecognized by EMS providers may be linked with intravenous fluid resuscitation and decreased patient consciousness. Overtriage appears to resolve somewhat from dispatch to transport. There were no fatal poisonings in this study population.

Swedish dispatchers’ compliance with the American Heart Association performance goals for dispatch-assisted cardiopulmonary resuscitation and its association with survival in out-of-hospital cardiac arrest: A retrospective study

Aim

We aimed 1) to investigate how Swedish dispatchers perform during emergency calls in accordance with the American Heart Association (AHA) goals for dispatcher-assisted cardiopulmonary resuscitation (DA-CPR), 2) calculate the potential impact on 30-day survival.

Methods

This observational study includes a random sample of 1000 out-of-hospital cardiac arrest (OHCA) emergency ambulance calls during 2018 in Sweden. Voice logs were audited to evaluate dispatchers’ handling of emergency calls according to the AHA performance goals. Number of possible additional survivors was estimated assuming the timeframes of the AHA performance goals was achieved.

Results

A total of 936 cases were included. An OHCA was recognized by a dispatcher in 79% (AHA goal 75%). In recognizable OHCA, dispatchers recognized 85% (AHA goal 95%). Dispatch-directed compressions were given in 61% (AHA goal 75%). Median time to OHCA recognition was 113 s [interquartile range (IQR), 62, 204 s] (AHA goal < 60 s). The first dispatch-directed compression was performed at a median time of 240 s [IQR, 176, 332 s] (AHA goal < 90 s). If eligible patients receive dispatch-directed compressions within the AHA 90 s goal, 73 additional lives may be saved; if all cases are recognized within the AHA 60 s goal, 25 additional lives may be saved.

Conclusions

The AHA policy statement serves as a benchmark for all emergency medical dispatch centres (EMDC). Additional effort is needed at Swedish EMDC to achieve AHA goals for DA-CPR. Our study suggests that if EMDC further optimize handling of OHCA calls in accordance with AHA goals, many more lives may be saved.

A registry-based observational study comparing emergency calls assessed by emergency medical dispatchers with and without support by registered nurses

Background

The requirement concerning formal education for emergency medical dispatcher (EMD) is debated and varies, both nationally and internationally. There are few studies on the outcomes of emergency medical dispatching in relation to professional background. This study aimed to compare calls handled by an EMD with and without support by a registered nurse (RN), with respect to priority level, accuracy, and medical condition.

Methods

A retrospective observational study, performed on registry data from specific regions during 2015. The ambulance personnel’s first assessment of the priority level and medical condition was used as the reference standard. Outcomes were: the proportion of calls dispatched with a priority in concordance with the ambulance personnel’s assessment; over- and undertriage; the proportion of most adverse over- and undertriage; sensitivity, specificity and predictive values for each of the ambulance priorities; proportion of calls dispatched with a medical condition in concordance with the ambulance personnel’s assessment. Proportions were reported with 95% confidence intervals. χ²-test was used for comparisons. P-levels < 0.05 were regarded as significant.

Results

A total of 25,025 calls were included (EMD n = 23,723, EMD + RN n = 1302). Analyses relating to priority and medical condition were performed on 23,503 and 21,881 calls, respectively. A dispatched priority in concordance with the ambulance personnel’s assessment were: EMD n = 11,319 (50.7%) and EMD + RN n = 481 (41.5%) (p < 0.01). The proportion of overtriage was equal for both groups: EMD n = 5904, EMD + RN n = 306, (26.4%) p = 0.25). The proportion of undertriage for each group was: EMD n = 5122 (22.9%) and EMD + RN n = 371 (32.0%) (p < 0.01). Sensitivity for the most urgent priority was 54.6% for EMD, compared to 29.6% for EMD + RN (p < 0.01), and specificity was 67.3% and 84.8% (p < 0.01) respectively. A dispatched medical condition in concordance with the ambulance personnel’s assessment were: EMD n = 13,785 (66.4%) and EMD + RN n = 697 (62.2%) (p = 0.01).

Conclusions

A higher precision of emergency medical dispatching was not observed when the EMD was supported by an RN. How patient safety is affected by the observed divergence in dispatched priorities is an area for future research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13049-021-00987-y.

Outcomes and interventions in patients transported to hospital with ongoing CPR after out-of-hospital cardiac arrest - An observational study

Introduction

The main objective was to present characteristics and outcome of patients without sustained field return of spontaneous circulation (ROSC) transported to hospital with ongoing cardiopulmonary resuscitation (CPR). Our secondary objectives were to investigate hospital-based interventions and the performance of the universal Termination of Resuscitation-rule (uTOR).

Methods

In this retrospective observational cohort study, out-of-hospital cardiac arrest (OHCA) patients arriving to the emergency department of a university hospital in Sweden during a six-year period (2010–2015) were identified using a prospectively recorded hospital-based registry. Additional data were retrieved from medical records and from the Swedish cardiopulmonary resuscitation registry.

Results

Among 409 patients transported with ongoing CPR, 7 survived to hospital discharge (1.7%). Hospital-based interventions against a suspected cause of arrest were attempted during ongoing resuscitation in 34 patients (8.3%), of whom 3 survived to hospital discharge. The remaining 4 survivors had spontaneous in-hospital ROSC. Survivors presented with either a shockable rhythm (n = 4) or pulseless electrical activity (n = 3). The uTOR identified non-survivors with a positive predictive value (PPV) of 98.4% and a specificity of 71.4% for termination.

Conclusion

Survival after OHCA where sustained prehospital ROSC is not achieved is rare and available in-hospital interventions are rarely utilised. No patient with asystole as the first recorded rhythm survived. The uTOR identified non-survivors with a PPV of 98.4% but showed poor specificity.

Features of Emergency Medical System calls that facilitate or inhibit Emergency Medical Dispatcher recognition that a patient is in, or at imminent risk of, cardiac arrest: A systematic mixed studies review

Aim

To identify and appraise evidence relating to the features of an Emergency Medicine System call interaction that enable, or inhibit, an Emergency Medical Dispatcher’s recognition that a patient is in out-of-hospital cardiac arrest, or at imminent risk of out-of-hospital cardiac arrest.

Methods

All study designs were eligible for inclusion. Data sources included Medline, BNI, CINAHL, EMBASE, PubMed, Cochrane Database of Systematic Reviews, AMED and OpenGrey. Stakeholder resources were screened and experts in resuscitation were asked to review the studies identified. Studies were appraised using the Mixed Methods Appraisal Tool. Synthesis was completed using a segregated mixed research synthesis approach.

Results

Thirty-two studies were included in the review. Three main themes were identified: Key features of the Emergency Medical Service call interaction; Managing the Emergency Medical Service call; Emotional distress.

Conclusion

A dominant finding is the difficulty in recognising abnormal/agonal breathing during the Emergency Medical Service call. The interaction between the caller and the Emergency Medical Dispatcher is critical in the recognition of patients who suffer an out-of-hospital cardiac arrest. Emergency Medical Dispatchers adapt their approach to the Emergency Medical Service call, and regular training for Emergency Medical Dispatchers is recommended to optimise out-of-hospital cardiac arrest recognition. Further research is required with a focus on the Emergency Medical Service call interaction of patients who are alive at the time of the Emergency Medical Service call and who later deteriorate into OHCA.

PROSPERO registration: CRD42019155458.

On Assessing Trustworthy AI in Healthcare. Machine Learning as a Supportive Tool to Recognize Cardiac Arrest in Emergency Calls

Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission (EC), recently published ethics guidelines for what it terms “trustworthy” AI. These guidelines are aimed at a variety of stakeholders, especially guiding practitioners toward more ethical and more robust applications of AI. In line with efforts of the EC, AI ethics scholarship focuses increasingly on converting abstract principles into actionable recommendations. However, the interpretation, relevance, and implementation of trustworthy AI depend on the domain and the context in which the AI system is used. The main contribution of this paper is to demonstrate how to use the general AI HLEG trustworthy AI guidelines in practice in the healthcare domain. To this end, we present a best practice of assessing the use of machine learning as a supportive tool to recognize cardiac arrest in emergency calls. The AI system under assessment is currently in use in the city of Copenhagen in Denmark. The assessment is accomplished by an independent team composed of philosophers, policy makers, social scientists, technical, legal, and medical experts. By leveraging an interdisciplinary team, we aim to expose the complex trade-offs and the necessity for such thorough human review when tackling socio-technical applications of AI in healthcare. For the assessment, we use a process to assess trustworthy AI, called 1Z-Inspection® to identify specific challenges and potential ethical trade-offs when we consider AI in practice.

Rapid dispatch for out-of-hospital cardiac arrest is associated with improved survival

AIM

As proxy for initiation of the first link in the Chain of Survival by the dispatcher, we aimed to investigate the effect of time to first dispatch on 30-day survival among patients with OHCA ultimately receiving the highest-level emergency medical response.

METHODS

We linked data on all OHCA unwitnessed by emergency medical services (EMS) treated by Copenhagen EMS from 2016 through 2018 to corresponding emergency call records. Among patients receiving highest priority emergency response, we calculated time to dispatch as time from start of call to time of first dispatch.

RESULTS

We included 3548 patients with OHCA. Of these, 94.1% received the highest priority response (median time to dispatch 0.84 min, 25th-75th percentile 0.58-1.24 min). Patients with time to dispatch within one minute compared to three or more minutes were more likely to receive bystander cardiopulmonary resuscitation (77.3 vs 54.2%), bystander defibrillation (11.5 vs 6.5%) and defibrillation by emergency medical services (24.1 vs 7.5%) and were 2.6-fold more likely to survive 30 days after the OHCA (P = 0.004). Results from multivariate logistic regression were similar: odds ratio (OR) of survival 0.83 per minute increase (95% confidence interval 0.70-1.00, P = 0.04). However, survival was similar between those who received highest priority response and those who did not: OR of survival 0.88 (95% confidence interval 0.53-1.46, P = 0.61).

CONCLUSION

Rapid time to dispatch among patients with highest priority response was significantly associated with a higher probability of 30-day survival following OHCA.

European Resuscitation Council Guidelines 2021: Systems saving lives

The European Resuscitation Council (ERC) has produced these Systems Saving Lives guidelines, which are based on the 2020 International Consensus on Cardiopulmonary Resuscitation Science with Treatment Recommendations. The topics covered include chain of survival, measuring performance of resuscitation, social media and smartphones apps for engaging community, European Restart a Heart Day, World Restart a Heart, KIDS SAVE LIVES campaign, lower-resource setting, European Resuscitation Academy and Global Resuscitation Alliance, early warning scores, rapid response systems, and medical emergency team, cardiac arrest centres and role of dispatcher.

Machine learning can support dispatchers to better and faster recognize out-of-hospital cardiac arrest during emergency calls: A retrospective study

AIM

Fast recognition of out-of-hospital cardiac arrest (OHCA) by dispatchers might increase survival. The aim of this observational study of emergency calls was to (1) examine whether a machine learning framework (ML) can increase the proportion of calls recognizing OHCA within the first minute compared with dispatchers, (2) present the performance of ML with different false positive rate (FPR) settings, (3) examine call characteristics influencing OHCA recognition.

METHODS

ML can be configured with different FPR settings, i.e., more or less inclined to suspect an OHCA depending on the predefined setting. ML OHCA recognition within the first minute is evaluated with a 1.5 FPR as the primary endpoint, and other FPR settings as secondary endpoints. ML was exposed to a random sample of emergency calls from 2018. Voice logs were manually audited to evaluate dispatchers time to recognition.

RESULTS

Of 851 OHCA calls, the ML recognized 36% (n = 305) within 1 min compared with 25% (n = 213) by dispatchers. The recognition rate at any time during the call was 86% for ML and 84% for dispatchers, with a median time to recognition of 72 versus 94 s. OHCA recognized by both ML and dispatcher showed a 28 s mean difference in favour of ML (P < 0.001). ML with higher FPR settings reduced recognition times.

CONCLUSION

ML recognized a higher proportion of OHCA within the first minute compared with dispatchers and has the potential to be a supportive tool during emergency calls. The optimal FPR settings need to be evaluated in a prospective study.

Variation in pre-hospital outcomes after out-of-hospital cardiac arrest in Michigan

AIM

Care by emergency medical service (EMS) agencies is critical for optimizing prehospital outcomes following out-of-hospital cardiac arrest (OHCA). We explored whether substantial differences exist in prehospital outcomes across EMS agencies in Michigan-specifically focusing on rates of sustained return of spontaneous circulation (ROSC) upon emergency department (ED) arrival.

METHODS

Using data from Michigan Cardiac Arrest Registry to Enhance Survival (MI-CARES) for years 2014-2017, we calculated rates of sustained ROSC upon ED arrival across EMS agencies in Michigan. We used hierarchical logistic regression models that accounted for patient, arrest-, community-, and response-level characteristics to determine adjusted rates of sustained ROSC among EMS agencies.

RESULTS

A total of 103 EMS agencies and 20,897 OHCA cases were included. Average age of the cohort was 62.5 years (SD = 19.6), 39.7% were female, and 17.9% had an initial shockable rhythm due to ventricular fibrillation or pulseless ventricular tachycardia. The adjusted rate of sustained ROSC upon ED arrival across all EMS agencies was 23.8% with notable variation across EMS agencies (interquartile range [IQR], 20.5-29.2%). The top five EMS agencies had mean adjusted rates of sustained ROSC upon ED arrival of 42.7% (95% CI: 34.6-51.1%) while the bottom five had mean adjusted rates of 9.8% (95% CI: 7.6-12.7%).

CONCLUSIONS

Substantial variation in sustained ROSC upon ED arrival exists across EMS agencies in Michigan after adjusting for patient-, arrest, community-, and response-level features. Such differences suggest opportunities to identify and improve best practices in EMS agencies to advance OHCA care.

Diagnosis of out-of-hospital cardiac arrest by emergency medical dispatch: A diagnostic systematic review

INTRODUCTION

Cardiac arrest is a time-sensitive condition requiring urgent intervention. Prompt and accurate recognition of cardiac arrest by emergency medical dispatchers at the time of the emergency call is a critical early step in cardiac arrest management allowing for initiation of dispatcher-assisted bystander CPR and appropriate and timely emergency response. The overall accuracy of dispatchers in recognizing cardiac arrest is not known. It is also not known if there are specific call characteristics that impact the ability to recognize cardiac arrest.

METHODS

We performed a systematic review to examine dispatcher recognition of cardiac arrest as well as to identify call characteristics that may affect their ability to recognize cardiac arrest at the time of emergency call. We searched electronic databases for terms related to "emergency medical dispatcher", "cardiac arrest", and "diagnosis", among others, with a focus on studies that allowed for calculating diagnostic test characteristics (e.g. sensitivity and specificity). The review was consistent with Grading of Recommendations, Assessment, Development and Evaluation (GRADE) method for evidence evaluation.

RESULTS

We screened 2520 article titles, resulting in 47 studies included in this review. There was significant heterogeneity between studies with a high risk of bias in 18 of the 47 which precluded performing meta-analyses. The reported sensitivities for cardiac arrest recognition ranged from 0.46 to 0.98 whereas specificities ranged from 0.32 to 1.00. There were no obvious differences in diagnostic accuracy between different dispatching criteria/algorithms or with the level of education of dispatchers.

CONCLUSION

The sensitivity and specificity of cardiac arrest recognition at the time of emergency call varied across dispatch centres and did not appear to differ by dispatch algorithm/criteria used or education of the dispatcher, although comparisons were hampered by heterogeneity across studies. Future efforts should focus on ways to improve sensitivity of cardiac arrest recognition to optimize patient care and ensure appropriate and timely resource utilization.

Adult Basic Life Support: International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations

This 2020 International Consensus on Cardiopulmonary Resuscitation (CPR) and Emergency Cardiovascular Care Science With Treatment Recommendations on basic life support summarizes evidence evaluations performed for 20 topics that were prioritized by the Basic Life Support Task Force of the International Liaison Committee on Resuscitation. The evidence reviews include 16 systematic reviews, 3 scoping reviews, and 1 evidence update. Per agreement within the International Liaison Committee on Resuscitation, new or revised treatment recommendations were only made after a systematic review.

Systematic reviews were performed for the following topics: dispatch diagnosis of cardiac arrest, use of a firm surface for CPR, sequence for starting CPR (compressions-airway-breaths versus airway-breaths-compressions), CPR before calling for help, duration of CPR cycles, hand position during compressions, rhythm check timing, feedback for CPR quality, alternative techniques, public access automated external defibrillator programs, analysis of rhythm during chest compressions, CPR before defibrillation, removal of foreign-body airway obstruction, resuscitation care for suspected opioid-associated emergencies, drowning, and harm from CPR to victims not in cardiac arrest.

The topics that resulted in the most extensive task force discussions included CPR during transport, CPR before calling for help, resuscitation care for suspected opioid-associated emergencies, feedback for CPR quality, and analysis of rhythm during chest compressions. After discussion of the scoping reviews and the evidence update, the task force prioritized several topics for new systematic reviews.

Adult Basic Life Support: 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations

This2020 International Consensus on Cardiopulmonary Resuscitation(CPR)and Emergency Cardiovascular Care Science With Treatment Recommendationson basic life support summarizes evidence evaluations performed for 22 topics that were prioritized by the Basic Life Support Task Force of the International Liaison Committee on Resuscitation. The evidence reviews include 16 systematic reviews, 5 scoping reviews, and 1 evidence update. Per agreement within the International Liaison Committee on Resuscitation, new or revised treatment recommendations were only made after a systematic review.

Systematic reviews were performed for the following topics: dispatch diagnosis of cardiac arrest, use of a firm surface for CPR, sequence for starting CPR (compressions-airway-breaths versus airway-breaths-compressions), CPR before calling for help, duration of CPR cycles, hand position during compressions, rhythm check timing, feedback for CPR quality, alternative techniques, public access automated external defibrillator programs, analysis of rhythm during chest compressions, CPR before defibrillation, removal of foreign-body airway obstruction, resuscitation care for suspected opioid-associated emergencies, drowning, and harm from CPR to victims not in cardiac arrest.

The topics that resulted in the most extensive task force discussions included CPR during transport, CPR before calling for help, resuscitation care for suspected opioid-associated emergencies, feedback for CPR quality, and analysis of rhythm during chest compressions. After discussion of the scoping reviews and the evidence update, the task force prioritized several topics for new systematic reviews.

Skeletal and soft tissue injuries after manual and mechanical chest compressions

AIMS

To determine the rate of injuries related to cardiopulmonary resuscitation (CPR) in cardiac arrest non-survivors, comparing manual CPR with CPR performed using the Lund University Cardiac Assist System (LUCAS).

METHODS AND RESULTS

We prospectively evaluated 414 deceased adult patients using focused, standardized post-mortem investigation in years 2005 through 2013. Skeletal and soft tissue injuries were noted, and soft tissue injuries were evaluated with respect to degree of severity. We found sternal fracture in 38%, rib fracture in 77%, and severe soft tissue injury in 1.9% of cases treated with CPR with manual chest compressions (n = 52). Treatment with LUCAS CPR (n = 362) was associated with significantly higher rates of sternal fracture (80% of cases), rib fracture (96%), and severe soft tissue injury (10%), including several cases of potentially life-threatening injuries.

CONCLUSION

LUCAS CPR causes significantly more CPR-related injuries than manual CPR, while providing no proven survival benefit on a population basis. We suggest judicious use of the LUCAS device for cardiac arrest.

Identifying the relative importance of predictors of survival in out of hospital cardiac arrest: a machine learning study

Introduction

Studies examining the factors linked to survival after out of hospital cardiac arrest (OHCA) have either aimed to describe the characteristics and outcomes of OHCA in different parts of the world, or focused on certain factors and whether they were associated with survival. Unfortunately, this approach does not measure how strong each factor is in predicting survival after OHCA.

Aim

To investigate the relative importance of 16 well-recognized factors in OHCA at the time point of ambulance arrival, and before any interventions or medications were given, by using a machine learning approach that implies building models directly from the data, and arranging those factors in order of importance in predicting survival.

Methods

Using a data-driven approach with a machine learning algorithm, we studied the relative importance of 16 factors assessed during the pre-hospital phase of OHCA. We examined 45,000 cases of OHCA between 2008 and 2016.

Results

Overall, the top five factors to predict survival in order of importance were: initial rhythm, age, early Cardiopulmonary Resuscitation (CPR, time to CPR and CPR before arrival of EMS), time from EMS dispatch until EMS arrival, and place of cardiac arrest. The largest difference in importance was noted between initial rhythm and the remaining predictors. A number of factors, including time of arrest and sex were of little importance.

Conclusion

Using machine learning, we confirm that the most important predictor of survival in OHCA is initial rhythm, followed by age, time to start of CPR, EMS response time and place of OHCA. Several factors traditionally viewed as important, e.g. sex, were of little importance.

Dispatcher Identification of Out-of-Hospital Cardiac Arrest and Neurologically Intact Survival: A Retrospective Cohort Study

Introduction:

To date, there are no published data on the association of patient-centered outcomes and accurate public-safety answering point (PSAP) dispatch in an American population. The goal of this study is to determine if PSAP dispatcher recognition of out-of-hospital cardiac arrest (OHCA) is associated with neurologically intact survival to hospital discharge.

Methods:

This retrospective cohort study is an analysis of prospectively collected Quality Assurance/Quality Improvement (QA/QI) data from the San Antonio Fire Department (SAFD; San Antonio, Texas USA) OHCA registry from January 2013 through December 2015. Exclusion criteria were: Emergency Medical Services (EMS)-witnessed arrest, traumatic arrest, age <18 years old, no dispatch type recorded, and missing outcome data. The primary exposure was dispatcher recognition of cardiac arrest. The primary outcome was neurologically intact survival (defined as Cerebral Performance Category [CPC] 1 or 2) to hospital discharge. The secondary outcomes were: bystander cardiopulmonary resuscitation (CPR), automated external defibrillator (AED) use, and prehospital return of spontaneous return of circulation (ROSC).

Results:

Of 3,469 consecutive OHCA cases, 2,569 cases were included in this analysis. The PSAP dispatched 1,964/2,569 (76.4%) of confirmed OHCA cases correctly. The PSAP dispatched 605/2,569 (23.6%) of confirmed OHCA cases as another chief complaint. Neurologically intact survival to hospital discharge occurred in 99/1,964 (5.0%) of the recognized cardiac arrest group and 28/605 (4.6%) of the unrecognized cardiac arrest group (OR = 1.09; 95% CI, 0.71–1.70). Bystander CPR occurred in 975/1,964 (49.6%) of the recognized cardiac arrest group versus 138/605 (22.8%) of the unrecognized cardiac arrest group (OR = 3.34; 95% CI, 2.70–4.11).

Conclusion:

This study found no association between PSAP dispatcher identification of OHCA and neurologically intact survival to hospital discharge. Dispatcher identification of OHCA remains an important, but not singularly decisive link in the OHCA chain of survival.

Public access of automated external defibrillators in a metropolitan city of China

BACKGROUND

Public access of automated external defibrillator (AED) is an important public health strategy for improving survival of cardiac arrest. Major metropolitan cities in China are increasingly investing and implementing public access defibrillator programs, but the effectiveness of these programs remains unclear. This study aims to evaluate the public accessibility of AED in Shanghai, a major metropolitan city in China.

METHODS

From July 1 to September 30, 2018, all AED locations indicated by AED Access Map Apps were visited and investigated in three most densely distributing areas of AED (Huangpu District, Xuhui District, and Central Area of the Pudong New District) in Shanghai. Two AED Access Map APPs were used to identify the location of AEDs. Characteristics of and the barriers to access, the AED sites were recorded. Awareness and skills of first aid and AED among on-site staff of the AED installation sites were evaluated.

RESULTS

A total of 283 sites were marked on two AED Apps. One hundred and seventy (60%) locations were accessible, and 142 (50%) were actually with AEDs installed. Among those AED installed sites, 112 (79%) were completely identifiable to the information on the maps, 20 (14%) were inconsistent and 10 (7%) were inaccurate on the maps. Ninety-four (66%) AEDs had visible signs and information around the location, 7 (5%) AEDs had signs outside of the location, and 107 (75%) sites had educational instructions. In addition, 230 individuals who were around the AED site were interviewed. Among them, 79 (34%) had good knowledge of AED. After shown the picture of AED, 112 (49%) knew whether there was AED in the site, and 108 (47%) knew the AED's location. Eighty-seven (38%) staff have received first aid training, and among them 26 (30%) reported that they had skills in operating the AED.

CONCLUSIONS

Public placement and accessibility of AEDs, related public signs and information on AED, and staff's awareness about AED were not optimal in Shanghai. Continuing efforts should be made to improve public accessibility and public awareness, knowledge, and user skills of AED.

Machine learning as a supportive tool to recognize cardiac arrest in emergency calls

BACKGROUND

Emergency medical dispatchers fail to identify approximately 25% of cases of out of hospital cardiac arrest, thus lose the opportunity to provide the caller instructions in cardiopulmonary resuscitation. We examined whether a machine learning framework could recognize out-of-hospital cardiac arrest from audio files of calls to the emergency medical dispatch center.

METHODS

For all incidents responded to by Emergency Medical Dispatch Center Copenhagen in 2014, the associated call was retrieved. A machine learning framework was trained to recognize cardiac arrest from the recorded calls. Sensitivity, specificity, and positive predictive value for recognizing out-of-hospital cardiac arrest were calculated. The performance of the machine learning framework was compared to the actual recognition and time-to-recognition of cardiac arrest by medical dispatchers.

RESULTS

We examined 108,607 emergency calls, of which 918 (0.8%) were out-of-hospital cardiac arrest calls eligible for analysis. Compared with medical dispatchers, the machine learning framework had a significantly higher sensitivity (72.5% vs. 84.1%, p < 0.001) with lower specificity (98.8% vs. 97.3%, p < 0.001). The machine learning framework had a lower positive predictive value than dispatchers (20.9% vs. 33.0%, p < 0.001). Time-to-recognition was significantly shorter for the machine learning framework compared to the dispatchers (median 44 seconds vs. 54 s, p < 0.001).

CONCLUSIONS

A machine learning framework performed better than emergency medical dispatchers for identifying out-of-hospital cardiac arrest in emergency phone calls. Machine learning may play an important role as a decision support tool for emergency medical dispatchers.

The accuracy of medical dispatch - a systematic review

BACKGROUND

It is a challenge to dispatch Emergency medical Services (EMS) appropriately with limited resources and maintaining patient safety; this requires accurate dispatching systems. The objective of the current systematic review was to examine the evidence, according to GRADE, for medical dispatching systems to accurately dispatch EMS according to level of acuity and in recognition of specific conditions. A systematic search was performed trough PubMed, Web of Science, Embase (free text in all fields), Centre for Reviews and Dissemination (CRD), and Cochrane Central Register of Controlled Trials up to 16th of May, 2017. A combination of keywords and Medical Subject Heading (MeSH) terms relevant to "emergency medical dispatch criteria" were used, to search for articles published between 2012 and 2017. Publications were included according to the inclusion/exclusion criteria using the Systematic Reviews and Meta-Analyses (PRISMA) protocol. Level of evidence was evaluated in accordance with Grading of Recommendations Assessment, Development and Evaluation (GRADE). Articles included were those that provided evidence for at least one of the measures of dispatch system accuracy; i.e. sensitivity, specificity, positive and negative predictive and/or over- and under-triage. The search identified 1445 articles. After the removal of duplicates, 382 titles were reviewed for relevance and an additional 359 articles were excluded based on manuscript title and abstract. An additional five articles were excluded after review of the full text versions of the remaining articles. The current review included 18 publications which all were based on primary research.

CONCLUSIONS

The 18 articles addressed the identification of cardiac arrest, stroke, medical priority and major trauma using different dispatching systems. The results of the current review show that there is a very low to low overall level of evidence for the accuracy of medical dispatching systems. We suggest that it is necessary to create a consensus on common standards for reporting before consensus can be reached for the level of accuracy in medical dispatching systems.

A five-year retrospective study of out-of-hospital cardiac arrest in a north-east Italian urban area

BACKGROUND

The reporting and analysing of data of out-of-hospital cardiac arrests encourages the quality improvement of the emergency medical services. For this reason, the establishment of a sufficiently large patient database is intended to allow analysis of resuscitation treatments for out-of-hospital cardiac arrests and performances of different emergency medical services.

AIMS

The aim of this study was to describe the demographics, characteristics, outcomes and determinant factors of survival for patients who suffered an out-of-hospital cardiac arrest.

METHODS

this was a retrospective study including all out-of-hospital cardiac arrest cases treated by the emergency medical service in the district of Udine (Italy) from 1 January 2010-31 December 2014.

RESULTS

A total of 1105 out-of-hospital cardiac arrest patients were attended by the emergency medical service. Of these, 489 (44.2%) underwent cardiopulmonary resuscitation, and return of spontaneous circulation was achieved in 142 patients (29%). There was a male predominance overall, and the main age was 72.6 years (standard deviation 17.9). Cardiopulmonary resuscitation before emergency medical service arrival was performed on 62 cases (44%) in the return of spontaneous circulation group, and on 115 cases (33%) in the no return of spontaneous circulation group ( p<0.024). Among the 142 cases of return of spontaneous circulation, 29 (5.9%) survived to hospital discharge. There was a smaller likelihood of return of spontaneous circulation when patients were female (odds ratio 0.61, 0.40-0.93). Patients who had an out-of-hospital cardiac arrest with an initial shockable rhythm (odds ratio 6.33, 3.86-10.39) or an age <60 years (odds ratio 2.91, 1.86-4.57) had a greater likelihood of return of spontaneous circulation. In addition, bystander cardiopulmonary resuscitation (odds ratio 1.56, 1.04-2.33) was associated with an increased chance of return of spontaneous circulation.

CONCLUSION

The incidence of out-of-hospital cardiac arrest and survival rate lies within the known range. A wider database is necessary to achieve a better knowledge of out-of-hospital cardiac arrest and to drive future investments in the healthcare system.

A comparison of two emergency medical dispatch protocols with respect to accuracy

Background

Emergency medical dispatching should be as accurate as possible in order to ensure patient safety and optimize the use of ambulance resources. This study aimed to compare the accuracy, measured as priority level, between two Swedish dispatch protocols – the three-graded priority protocol Medical Index and a newly developed prototype, the four-graded priority protocol, RETTS-A.

Methods

A simulation study was carried out at the Emergency Medical Communication Centre (EMCC) in Stockholm, Sweden, between October and March 2016. Fifty-three voluntary telecommunicators working at SOS Alarm were recruited nationally. Each telecommunicator handled 26 emergency medical calls, simulated by experienced standard patients. Manuscripts for the scenarios were based on recorded real-life calls, representing the six most common complaints. A cross-over design with 13 + 13 calls was used. Priority level and medical condition for each scenario was set through expert consensus and used as gold standard in the study.

Results

A total of 1293 calls were included in the analysis. For priority level, n = 349 (54.0%) of the calls were assessed correctly with Medical Index and n = 309 (48.0%) with RETTS-A (p = 0.012). Sensitivity for the highest priority level was 82.6% (95% confidence interval: 76.6–87.3%) in the Medical Index and 54.0% (44.3–63.4%) in RETTS-A. Overtriage was 37.9% (34.2–41.7%) in the Medical Index and 28.6% (25.2–32.2%) in RETTS-A. The corresponding proportion of undertriage was 6.3% (4.7–8.5%) and 23.4% (20.3–26.9%) respectively.

Conclusion

In this simulation study we demonstrate that Medical Index had a higher accuracy for priority level and less undertriage than the new prototype RETTS-A. The overall accuracy of both protocols is to be considered as low. Overtriage challenges resource utilization while undertriage threatens patient safety. The results suggest that in order to improve patient safety both protocols need revisions in order to guarantee safe emergency medical dispatching.

Dispatch of Firefighters and Police Officers in Out-of-Hospital Cardiac Arrest: A Nationwide Prospective Cohort Trial Using Propensity Score Analysis

BACKGROUND

Dispatch of basic life support-trained first responders equipped with automated external defibrillators in addition to advanced life support-trained emergency medical services personnel in out-of-hospital cardiac arrest (OHCA) has, in some minor cohort studies, been associated with improved survival. The aim of this study was to evaluate the association between basic life support plus advanced life support response and survival in OHCA at a national level.

METHODS AND RESULTS

This prospective cohort study was conducted from January 1, 2012, to December 31, 2014. People who experienced OHCA in 9 Swedish counties covered by basic life support plus advanced life support response were compared with a propensity-matched contemporary control group of people who experienced OHCA in 12 counties where only emergency medical services was dispatched, providing advanced life support. Primary outcome was survival to 30 days. The analytic sample consisted of 2786 pairs (n=5572) derived from the total cohort of 7308 complete cases. The median time from emergency call to arrival of emergency medical services or first responder was 9 minutes in the intervention group versus 10 minutes in the controls (P<0.001). The proportion of patients admitted alive to the hospital after resuscitation was 31.4% (875/2786) in the intervention group versus 24.9% (694/2786) in the controls (conditional odds ratio, 1.40; 95% confidence interval, 1.24-1.57). Thirty-day survival was 9.5% (266/2786) in the intervention group versus 7.7% (214/2786) in the controls (conditional odds ratio, 1.27; 95% confidence interval, 1.05-1.54).

CONCLUSIONS

In this nationwide interventional trial, using propensity score matching, dispatch of first responders in addition to emergency medical services in OHCA was associated with a moderate, but significant, increase in 30-day survival.

CLINICAL TRIAL REGISTRATION

URL: http://www.clinicaltrials.gov. Unique identifier: NCT02184468.

Dispatch and prehospital transport for acute septic patients: an observational study

Background

In order to dispatch ambulances with the correct level of urgency, the dispatch center has to balance the perceived urgency and traffic safety considerations with the available resources. As urgency is not clear in all clinical situations, some high urgency patients may end up with a suboptimal mode of transport.

Patients with severe sepsis or septic shock suffer from highly time dependent conditions but they present with a wide range of symptoms, which might be difficult to identify in the dispatch system.

The aim of the study is to investigate the modes of prehospital transport among acute admitted patients with sepsis, severe sepsis and septic shock.

Methods

We included all adult patients (≥15 years) presenting to an acute medical unit at Odense University Hospital with a first-time admission of community-acquired sepsis between September 2010-August 2011. Cases and prehospital ambulance transport were identified by structured manual chart review. In all cases it was registered, whether the ordinary ambulance was assisted by the mobile emergency care unit (MECU), manned by anesthesiologists.

Results

We included 1,713 patients median age 72 years (IQR 57–81), 793 (46.3%) male, 621 (36.3%) had sepsis, 1,071 (62.5%) severe sepsis, and 21 (1.2%) septic shock.

In the group of sepsis patients, 390 (62.8%) arrived without public prehospital transport, 197 (31.7%) were transported by ambulance, and 34 (5.5%) were assisted by MECU. In the group of severe sepsis patients, the same percentage 62.8% arrived without public pre-hospital transport, a lower percentage 28.2% were transported by ambulance, and a larger percentage 9.0% were transported by MECU. Among 21 patients with septic shock, 10 arrived without public pre-hospital transport (47.7%), 7 (33.3%) were transported by ambulance, and 4 (19.0%) by MECU.

The 30-day mortality hazard ratio was associated with mode of transport, with the adjusted highest hazard ratio found in the group of MECU transported patients 1.76 (95%Cl 1.16–2.66).

Conclusions

A substantial proportion of patients with severe sepsis and septic shock arrive to hospital without public prehospital transport or by unspecialized ambulances.

The difficult medical emergency call: A register-based study of predictors and outcomes

BACKGROUND

Pre-hospital emergency care requires proper categorization of emergency calls and assessment of emergency priority levels by the medical dispatchers. We investigated predictors for emergency call categorization as "unclear problem" in contrast to "symptom-specific" categories and the effect of categorization on mortality.

METHODS

Register-based study in a 2-year period based on emergency call data from the emergency medical dispatch center in Copenhagen combined with nationwide register data. Logistic regression analysis (N = 78,040 individuals) was used for identification of predictors of emergency call categorization as "unclear problem". Poisson regression analysis (N = 97,293 calls) was used for examining the effect of categorization as "unclear problem" on mortality.

RESULTS

"Unclear problem" was the registered category in 18% of calls. Significant predictors for "unclear problem" categorization were: age (odds ratio (OR) 1.34 for age group 76+ versus 18-30 years), ethnicity (OR 1.27 for non-Danish vs. Danish), day of week (OR 0.92 for weekend vs. weekday), and time of day (OR 0.79 for night vs. day). Emergency call categorization had no effect on mortality for emergency priority level A calls, incidence rate ratio (IRR) 0.99 (95% confidence interval (CI) 0.90-1.09). For emergency priority level B calls, an association was observed, IRR 1.26 (95% CI 1.18-1.36).

DISCUSSIONS

The results shed light on the complexity of emergency call handling, but also implicate a need for further improvement. Educational interventions at the dispatch centers may improve the call handling, but also the underlying supportive tools are modifiable. The higher mortality rate for patients with emergency priority level B calls with "unclear problem categorization" could imply lowering the threshold for dispatching a high level ambulance response when the call is considered unclear. On the other hand a "benefit of the doubt" approach could hinder the adequate response to other patients in need for an ambulance as there is an increasing demand and limited resources for ambulance services.

CONCLUSIONS

Age, ethnicity, day of week and time of day were significant predictors of emergency call categorization as "unclear problem". "Unclear problem" categorization was not associated with mortality for emergency priority level A calls, but a higher mortality was observed for emergency priority level B calls.