Standardized reporting of data is crucial for out-of-hospital cardiac arrest (OHCA) research. While the implementation of first responder systems dispatching volunteers to OHCA is encouraged, there is currently no uniform reporting standard for describing these systems.A steering committee established a literature search to identify experts in smartphone alerting systems. These international experts were invited to a conference held in Hinterzarten, Germany, with 40 researchers from 13 countries in attendance. Prior to the conference, participants submitted proposals for parameters to be included in the reporting standard. The conference comprised five workshops covering different aspects of smartphone alerting systems. Proposed parameters were discussed, clarified, and consensus was achieved using the Nominal Group Technique. Participants voted in a modified Delphi approach on including each category as a core or supplementary element in the reporting standard. Results were presented, and a writing group developed definitions for all categories and items, which were sent to participants for revision and final voting using LimeSurvey web-based software.The resulting reporting standard consists of 68 core items and 21 supplementary items grouped into five topics (first responder system, first responder network, technology/ algorithm/ strategies, reporting data, and automated external defibrillators (AED)).This proposed reporting standard generated by an expert opinion group fills the gap in describing first responder systems. Its adoption in future research will facilitate comparison of systems and research outcomes, enhancing the transfer of scientific findings to clinical practice.
Introduction: Public housing areas have a high incidence of out-of-hospital cardiac arrest (OHCA) and are potential targets to improve OHCA survival. A fast emergency medical services (EMS) response is crucial to achieve OHCA survival. However, EMS response times in public housing areas remain unexplored. Research Question: Are EMS response times longer in public housing areas than in other residential areas? Aim: The primary objective was to investigate EMS response times for OHCA in public housing compared to other residential areas; differences in initial shockable rhythm and 30-day survival were secondary objectives. Method: Non-EMS witnessed OHCAs within residential areas from Amsterdam (2016-2021), Copenhagen (2016-2021), and Vienna (2018-2021) were included from the Amsterdam Resuscitation Studies and the Danish and Viennese Cardiac Arrest registries, excluding missing data on age, sex, and EMS response times. OHCAs were divided into public housing and other residential areas. Early dispatch was defined as <90 seconds from incoming call until dispatch of EMS vehicle; early arrival as < 6 minutes from dispatch of EMS vehicle until arrival on scene. We compared early dispatch, early arrival, initial shockable rhythm, and 30-day survival in public housing vs. other residential areas using a generalized estimation equation model adjusted for age, sex, and city and presented as adjusted odds ratios (aOR). Results: We included 8,659 patients, of which 2,883 (33,3%) occurred in public housing areas. OHCA patients in public housing areas were younger, more often female, less likely bystander witnessed, and less often received bystander interventions compared to other residential areas (Table 1). Comparing OHCAs in public housing vs. other residential areas (reference), early dispatch was 38.0% vs. 39.3%, aOR 0.83 [95% CI 0.73-0.93]; early arrival, 41.6% vs. 45.7%, aOR 0.84 [95% CI 0.76-0.92]; initial shockable rhythm, 17.0% vs. 22.8%, aOR 0.70 [95% CI 0.62-0.78]; and 30-day survival, 8.7% vs. 14.1%, aOR 0.57 [95% CI 0.48-0.66] (Figure 1). Conclusion: Dispatch and arrival of EMS vehicles took longer for OHCA patients in public housing compared to other residential areas, followed by a lower probability of initial shockable rhythm and 30-day survival. These findings suggest the EMS system as a relevant target for improving OHCA survival in public housing areas.
but is often lacking.Previous studies have shown that a probability distribution of spatial incidence of OHCA can be used as input to analytical models to find the best locations for AEDs [14][15][16].In addition, identifying geographical areas that have disproportionally many nonsurvivors compared to survivors (and vice versa) allows for further specific targeting interventions to increase survival, like local public awareness campaigns.Previous spatial analysis methods have provided significant, but limited insight.Recent studies that analysed spatial or spatiotemporal OHCA risk used spatial analysis methods [17][18][19] or Bayesian methods [20][21][22][23][24][25].All these methods require aggregating data in spatial cells or administrative areas, which means that results and granularity are influenced by that choice.Furthermore, several studies investigated how the spatial distribution developed over the years [19][20][21][22].It is known that temporal (in)accessibility of AEDs is an important aspect in effective defibrillation by bystanders [10].Additionally, (spatial) availability of volunteer responders may depend on the time of day, as people go to work and change locations throughout the day.Therefore, we are interested in how spatial incidence develops throughout the day, instead of over the years.The objective of this study was to propose a methodology (1) to analyse how incidence of OHCA is distributed throughout a study region, (2) to analyse how incidence changes over time of day, and (3) to identify which areas have significantly more survivors or non-survivors and explore to what extend basic case characteristics explain any difference found.We applied this method to a case study of OHCA in Amsterdam, the Netherlands, to show how the method works in practice.
Abstract Background: Over the past decade Smartphone-based activation (SBA) of Community First Responders (CFR) to out-of-hospital cardiac arrests (OHCA) has gained much attention and popularity throughout Europe. Various programmes have been established, and interestingly there are considerable differences in technology, responder spectrum and the degree of integration into the prehospital emergency services. It is unclear whether these dissimilarities affect outcome. This consensus paper reviews the current state in Europe, reveals similarities and controversies, and aims to help aligning future strategies for SBA of CFR in Europe. Methods: In a consensus conference a three-step approach was used: (i) presentation of current research from five European countries; (ii) workshops discussing evidence amongst the audience to generate consensus statements; (iii) anonymous real-time voting applying the modified RAND-UCLA methodology to adopt or reject the statements. The consensus panel aimed to represent all stakeholders involved in this topic. Results: While 21 of 25 generated statements gained approval, consensus was only found for 5 of them. One statement was rejected but without consensus. Members of the consensus conference confirmed that CFR save lives. They further acknowledged the crucial role of emergency medical control centres and called for nationwide strategies. Conclusions: Smartphone-based alert of CFR to OHCA saves lives. The statement generated by the consensus conference help to advise the public, healthcare services and governments to utilise these systems to their full potential, and direct the research community towards fields that still need to be addressed.
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