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Are first responders first? The rally to the suspected out-of-hospital cardiac arrest

Open AccessPublished:September 23, 2022DOI:https://doi.org/10.1016/j.resuscitation.2022.09.012

      Abstract

      Background

      Time is the crucial factor in the “chain of survival” treatment concept for out-of-hospital cardiac arrest (OHCA). We aimed to measure different response time intervals by comparing emergency medical system (EMS), fire fighters and smartphone aided volunteer responders.

      Methods

      In two large Swedish regions, volunteer responders were timed from the alert until they arrived at the scene of the suspected OHCA. The first arriving volunteer responders who tried to fetch an automated external defibrillator (AED-responder) and who ran to perform bystander cardiopulmonary resuscitation (CPR-responder) were compared to both the first arriving EMS and fire fighters. Three-time intervals were measured, from call to dispatch, the unit response time (from dispatch to arrival) and the total response time.

      Results

      During 22 months, 2631 suspected OHCAs were included. The median time from call to dispatch was in minutes 1.8 (95% CI = 1.7–1.8) for EMS, 2.9 (95% CI = 2.8–3.0) for fire-fighters and 3.0 (95% CI = 2.9–3.1) for volunteer responders. The median unit response time was 8.3 (95% CI = 8.1–8.5) for EMS, 6.8 (95% CI = 6.7–6.9) for fire fighters and 6.0 (95% CI = 5.7–6.2) for AED-responders and 4.6 (95% CI = 4.5–4.8) for CPR-responders. The total response time was 10.4 (95% CI = 10.1–10.6) for EMS, 10.2 (95% CI = 9.9–10.4) for fire fighters, 9.6 (95% CI = 9.1–9.8) for AED-responders and 8.2 (95% CI = 8.0–8.3) for CPR-responders.

      Conclusion

      First arriving volunteer responders had the shortest unit response time when compared to both fire fighters and EMS, however this advantage was reduced by delays introduced at the dispatch center. Earlier automatic dispatch should be considered in further studies.

      Keywords

      Introduction

      Out-of-hospital cardiac arrest (OHCA) is a time-critical medical emergency, affecting approximately 300 000 people a year in Europe.
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      Global incidences of out-of-hospital cardiac arrest and survival rates: Systematic review of 67 prospective studies.
      The expenditure of time is the common denominator of the four links in the “chain-of-survival” concept.
      • Nolan J.
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      The chain of survival.
      An initial shockable rhythm will deteriorate into asystole if treatment is not initiated promptly.
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      Prevention of deterioration of ventricular fibrillation by basic life support during out-of-hospital cardiac arrest.
      Early cardiopulmonary resuscitation
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      Survival in Out-of-Hospital Cardiac Arrest after Standard Cardiopulmonary Resuscitation or Chest Compressions only before Arrival of Emergency Medical Services: Nationwide Study during Three Guideline Periods.
      • Hasselqvist-Ax I.
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      • Herlitz J.
      • et al.
      Early Cardiopulmonary Resuscitation in Out-of-Hospital Cardiac Arrest.
      (CPR) and early defibrillation before arrival of the emergency medical service (EMS)
      • Valenzuela T.D.
      • Roe D.J.
      • Nichol G.
      • Clark L.L.
      • Spaite D.W.
      • Hardman R.G.
      Outcomes of rapid defibrillation by security officers after cardiac arrest in casinos.
      • Ringh M.
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      • et al.
      Survival after Public Access Defibrillation in Stockholm, Sweden – A striking success.
      are known to be associated with increased survival rates. EMS response time –proxy for time to advanced treatment– is associated with survival,
      • Holmén J.
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      • et al.
      Shortening ambulance response time increases survival in out-of-hospital cardiac arrest.
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      Influence of advanced life support response time on out-of-hospital cardiac arrest patient outcomes in Taipei.
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      Association of Bystander Cardiopulmonary Resuscitation and Survival According to Ambulance Response Times after Out-of-Hospital Cardiac Arrest.
      but has unfortunately increased over time.

      Rawshani A, Herlitz J. Årsrapport för år 2019. Svenska Hjärt-Lungräddningsregistret - Årsrapport för år 2019. 2020.

      Several solutions have been implemented in order to reach the victim within the first few minutes, such as wide spread CPR education, online registers for public automated external defibrillators (AEDs), and additional dispatch of on-duty “first responders” such as fire fighters or police officers.
      • Eisenberg M.S.
      Leonard Cobb and Medic One.
      • van Alem A.P.
      • Vrenken R.H.
      • de Vos R.
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      Use of automated external defibrillator by first responders in out of hospital cardiac arrest: Prospective controlled trial.
      • Hasselqvist-Ax I.
      • Nordberg P.
      • Herlitz J.
      • et al.
      Dispatch of firefighters and police officers in out-of-hospital cardiac arrest: A nationwide prospective cohort trial using propensity score analysis.
      During the last decade, systems for dispatch of volunteer responders using short message or smartphone technology, have been developed with the aim to reduce time to CPR and/or attachment of an AED to suspected OHCAs. Dispatch of volunteer responders have been shown to increase bystander CPR rates
      • Ringh M.
      • Rosenqvist M.
      • Hollenberg J.
      • et al.
      Mobile-Phone Dispatch of Laypersons for CPR in Out-of-Hospital Cardiac Arrest.
      and may decrease the time to AED attachment and defibrillation.
      • Andelius L.
      • Malta Hansen C.
      • Lippert F.K.
      • et al.
      Smartphone Activation of Citizen Responders to Facilitate Defibrillation in Out-of-Hospital Cardiac Arrest.
      • Stieglis R.
      • Zijlstra J.A.
      • Riedijk F.
      • Smeekes M.
      • van der Worp W.E.
      • Koster R.W.
      AED and text message responders density in residential areas for rapid response in out-of-hospital cardiac arrest.
      On-duty first responders and/or volunteer responders are dispatched when certain criteria are fulfilled.
      • van Alem A.P.
      • Vrenken R.H.
      • de Vos R.
      • Tijssen J.G.P.
      • Koster R.W.
      Use of automated external defibrillator by first responders in out of hospital cardiac arrest: Prospective controlled trial.
      • Andelius L.
      • Malta Hansen C.
      • Lippert F.K.
      • et al.
      Smartphone Activation of Citizen Responders to Facilitate Defibrillation in Out-of-Hospital Cardiac Arrest.
      • Becker L.
      • Husain S.
      • Kudenchuk P.
      • Doll A.
      • Rea T.
      • Eisenberg M.
      Treatment of cardiac arrest with rapid defibrillation by police in King County, WA.
      • Berglund E.
      • Claesson A.
      • Nordberg P.
      • et al.
      A smartphone application for dispatch of lay responders to out-of-hospital cardiac arrests.
      • Husain S.
      • Eisenberg M.
      Police AED programs: A systematic review and meta-analysis.
      Such criteria involve a high level of suspicion for OHCA. The optimal criteria for dispatch of volunteer responders remains to be determined. Furthermore, it remains to be shown that such volunteer responders can reduce time to defibrillation.
      In this study we aim to investigate the different parts of the response time regarding first arriving volunteer responders who were dispatched either to: a) go directly (to perform CPR) or b) fetch and bring an AED and compare these two groups to the response time of both the EMS and fire fighters. Also investigated was if the response times of the three groups were affected by population density and time of day.

      Methods

      Study design

      This was an observational retrospective study analyzing data of different response time intervals for EMS, fire fighters and smartphone aided volunteer responders dispatched to cases of suspected OHCA. Data was collected from the emergency medical dispatch center (EMDC) database and from the volunteer responder application server. Patients were dispatched at the EMDC and indexed as suspected OHCAs between 1st of May 2018 through 28th of February 2020. Incoming calls to 112, the Swedish national emergency telephone number, received between 07:00 and 23:00 (when the volunteer-responder system was activated) were included.

      Setting

      This study was conducted within Sweden’s two largest counties: Region Stockholm (area of 6519 km2 and 2.4 million inhabitants) and Region Västra Götaland (area 23,942 km2 and 1.7 million inhabitants). Together the two regions cover 40% of Sweden’s population and includes the two largest cities: Stockholm and Gothenburg, as well as more sparsely populated areas.

      Organizational structure

      Emergency medical dispatch center (EMDC)

      When the medical dispatcher received an emergency call it was categorized by the dispatcher in accordance with a criteria-based medical index. The index guided the dispatcher based on the described symptoms and provided direction and assistance in determining a suitable priority. In case of a potentially life-threatening medical condition regularly-one ambulance was dispatched at the earliest suspicion. When an OHCA was more confirmed – defined as “an unconscious adult with no, or abnormal breathing”, a second ambulance was dispatched. In parallel, the dispatcher transmitted an alarm to engage the dispatch of fire fighters and also activated the volunteer responder system with the exclusion of children (≤8 years), traumatic cases, hazardous situations, and calls from health care facilities.

      Emergency medical services (EMS)

      All EMS-ambulances were each staffed with at least one registered nurse capable of providing advanced life support (ALS) care to OHCAs. Dependent on weekday and time of day, there were a maximum of 73 ambulances in Stockholm region, and 102 in Västra Götaland.

      Fire fighters

      Fire fighters were on-duty and trained in basic life support (BLS) and equipped with an AED. They were dispatched 24/7 within their organization as a complement to standard care i.e., EMS. There were approximately 39 fire stations in region Stockholm and 89 in Västra Götaland.

      Volunteer responders

      Volunteer responders enrolled by installing a smartphone application integrated with the national register of AEDs, developed for map-aided dispatch to OHCAs (Heartrunner).
      • Berglund E.
      • Claesson A.
      • Nordberg P.
      • et al.
      A smartphone application for dispatch of lay responders to out-of-hospital cardiac arrests.
      • Berglund E.
      • Olsson E.
      • Jonsson M.
      • et al.
      Wellbeing, emotional response and stress among lay responders dispatched to suspected out-of-hospital cardiac arrests.
      The volunteers stated that they had undergone a basic course in CPR. They were dispatched through a smartphone application when they were in the vicinity of suspected OHCAs and asked either to go directly to perform CPR or to first collect an AED on their way to the scene. A maximum of 30 volunteer responders within 1.3 kilometers from the suspected OHCA received an alert and were requested to either accept or decline the mission. When accepting, the volunteer responders were provided with a map and pedestrian route directions either directly to the scene, or via a public AED. In the two regions at study start there were 43 600 registered volunteers and the AED register contained 5982 AEDs of which 22% were available 24/7.
      A link to an online survey was distributed via a text message to the accepting volunteer responders 90 minutes after the alert, asking questions about actions taken during the mission. Volunteer responders who stated that they tried to fetch an AED on the way to the victim were grouped as an AED-responder, and volunteer responders who stated that they went directly to perform CPR were grouped as a CPR-responder.

      Definitions and data sources

      When the EMS and the fire-fighters arrived at their assigned destination the staff paged back the arrival time to the EMDC. These pages represent the arrival times and were collected from the EMDC database. The volunteer responder server collected coordinates and related time stamps from the time of accept, and during the mission for each accepting volunteer responder. The volunteer responder’s arrival time were measured by creating a buffer zone with a 25-meter radius at the coordinates of the suspected OHCA. The first coordinates (and time stamp) that appeared within this buffer were regarded as the arrival time.
      • Jonsson M.
      • Berglund E.
      • Djärv T.
      • et al.
      A brisk walk-Real-life travelling speed of lay responders in out-of-hospital cardiac arrest.
      Cases where at least one volunteer responder arrived at the scene were compared with the EMS and fire fighters’ response time. Three different time frames were measured:
      • (a)
        Time to dispatch defined as the time from the incoming call to the dispatch of EMS, fire fighters or volunteer responders
      • (b)
        Unit response time defined as the time from dispatch to the arrival of EMS, fire fighters or volunteer responders
      • (c)
        Total response time defined as time from the incoming call to the arrival (a + b)
      Population density was measured in Demographical statistics areas (DeSO), which are neighborhood (census) areas developed by Statistics Sweden.

      Statistical analysis

      Descriptive data are presented as medians with 95% confidence intervals together with interquartile Q1 and Q3. Differences between groups are calculated using Hodges Lehmann location shift with 95% confidence intervals. All analyses were performed in R version 4.0.3 (R Foundation for Statistical Computing, Vienna, Austria).
      This study is a sub-study to the SAMBA studies (NCT02992873 and NCT04165668), approved by the regional ethics board (DNRs: 2016-1531-31/4 and 2019-03315).

      Results

      During the study period the system was activated in 3756 cases of suspected OHCA. After exclusion of cases where no volunteer responder was assigned (n = 182), incorrect coordinates (n = 2), cases where only fire fighters were dispatched (n = 9) and cases where no volunteer responder reached the scene (n = 932) 2631 cases remained. In 2206 of these cases at least one volunteer responder stated that they ran directly to perform CPR (CPR-responder) and in 1198 cases at least one volunteer responder stated that they tried to fetch an AED (AED-responder), see Fig. 1.

      Time to dispatch

      In Fig. 2 panel A, the time to dispatch is presented. EMS hade the shortest time to dispatch with a median time of 1.8 (95% CI = 1.7–1.8) minutes. The corresponding time for fire fighters was 2.9 (95% CI 2.8–3.0) and 3.0 (95% CI = 2.9–3.1) minutes for both volunteer responder groups. Volunteer responders were dispatched 1.1 (95% CI = 1.0–1.2) minutes later than EMS.
      Figure thumbnail gr2
      Fig. 2Response time for dispatched units. Time to dispatch (panel A), unit response time (panel B) and total response time (panel C).

      Unit response time

      In Fig. 2 panel B, the unit response time is presented. The median time from dispatch to arrival was 8.3 (95% CI = 8.1–8.5) for EMS compared to 6.8 (95% CI = 6.7–6.9) for fire fighters, 6.0 (95% CI = 5.7–6.2) for AED-responders and 4.6 (95% CI = 4.5–4.8) minutes for CPR-responders. The unit response time compared to EMS was 1.8 (95% CI = 1.4–2.1) minutes shorter for AED-responders and 3.6 (95% CI = 3.4–3.8) minutes shorter for CPR-responders.

      Total response time

      In Fig. 2 panel C, the total response times are presented. EMS had a median total response time of 10.4 (95% CI = 10.1–10.6) minutes. The corresponding time was 10.2 (95% CI = 9.9–10.4) for fire fighters, 9.6 (95% CI = 9.1–9.8) for AED-responders and 8.2 (95% CI = 8.0–8.3) minutes for CPR-responders. AED-responders had a total response time that was 0.3 (95% CI = 0.1–0.7) minutes shorter than EMS. The corresponding number for CPR-responders was 2.2 minutes (95% CI = 2.0–2.5).

      By population densities

      In Fig. 3A the time to dispatch is presented in different population densities. EMS had the shortest time to dispatch with a median time under 2 minutes regardless of population density. Fire fighters and both volunteer responder groups had similar median time to dispatch of around 3 minutes that did not differ by population density.
      Figure thumbnail gr3
      Fig. 3Time to dispatch and unit response time divided by population density.
      In Fig. 3B the unit response times are presented for the different groups. In areas where the population density was below 1000 persons/km2 the EMS had a response time of 10.9 (95% CI = 10.3–11.4) minutes. The unit response time for EMS was shorter, 6.6 (95% CI = 6.33–6.97) minutes in areas with more than 12,000 persons/km2. The corresponding times for fire fighters were 8.1 (95% CI = 7.8–8.4) in areas with the lowest population density compared to 5.9 (95% CI = 5.5–6.1) minutes in areas with the highest population density. Among the AED-responder group the unit response time by population density varied but did not follow a stepwise pattern. Among the CPR-responder group the median unit response time was 4.9 (95% CI = 4.6–5.2) in the areas with low population density and 3.9 (95% CI = 3.6–4.2) minutes in the areas with the highest population density.

      By day and hour

      In Fig. 4A the time to dispatch by hour of the day is presented. The differences in time to dispatch between the groups remained but were generally stable during the day. In Fig. 4B the unit response time followed the same pattern with similar stable median times over the day except for the AED-responder group where the median unit response time was longer during 07:00–07:59 and shortest around lunch time.
      Figure thumbnail gr4
      Fig. 4Time to dispatch and unit response time divided by hour of the day (07:00–22:59).

      Discussion

      The aim of this study was to compare three different time frames for different dispatched resources in suspected OHCA. The main finding was that the EMS had a 1-minute advantage in time to dispatch compared to both fire fighter and volunteer “first” responders. Although the unit response time for the EMS was longer (8.3 minutes) than for the other groups: fire fighters (6.8), AED-responders (6.1) and CPR-responders (4.6), the total response time for all units lead to a rally where all resources arrived mainly at the same time with a moderate exception for the CPR-responders. The disparity in time to dispatch between different units has been recognized before. In a randomized controlled trial by van Alem,
      • van Alem A.P.
      • Vrenken R.H.
      • de Vos R.
      • Tijssen J.G.P.
      • Koster R.W.
      Use of automated external defibrillator by first responders in out of hospital cardiac arrest: Prospective controlled trial.
      Dutch police officers did not arrive earlier, and authors concluded that the delay in dispatch severely reduced the potential benefit of a first responder police program. Similarly, Becker et al.
      • Becker L.
      • Husain S.
      • Kudenchuk P.
      • Doll A.
      • Rea T.
      • Eisenberg M.
      Treatment of cardiac arrest with rapid defibrillation by police in King County, WA.
      also saw a more than one minute delay before dispatch of police compared to EMS. Husain et al.
      • Husain S.
      • Eisenberg M.
      Police AED programs: A systematic review and meta-analysis.
      in a meta-analysis concluded that if the police and EMS are not dispatched simultaneously the police will not arrive before EMS. In essence, it is questionable if we can call the additional resources first responders. A “true” first responder such as BLS trained EMTs in King County, US, are dispatched first, also being first at the scene within a median total response time of 5.8 minutes.

      Chatalas H. 2019 Annual Report, Division of Emergency Medical Services [Internet]. Public Health Seattle King County. Available from: https://kingcounty.gov/depts/health/emergency-medical-services/∼/media/depts/health/emergency-medical-services/documents/reports/2019-Annual-Report.ashx.

      The results from this present study summons down to some important factors to reach the OHCA victim in time to facilitate early defibrillation and initiation of CPR.

      Factors influencing the time to dispatch

      The first factor is dependent on the early recognition of the OHCA by the dispatcher, but also of the dispatch procedure at the EMDC. In the recent AHA policy statement, Kurz et al.
      • Kurz M.C.
      • Bobrow B.J.
      • Buckingham J.
      • et al.
      Telecommunicator Cardiopulmonary Resuscitation: A Policy Statement from the American Heart Association.
      stated that a high-quality system should dispatch the first unit within 60 seconds. The result from the present study shows that the first dispatched unit which in this case was the EMS had a median time to dispatch of 1.8 minutes and suggest that the high-performance goal was not met. Recent Scandinavian studies have shown that the time to OHCA recognition could be as long as 01:53 minutes.
      • Hardeland C.
      • Claesson A.
      • Blom M.T.
      • et al.
      Description of call handling in emergency medical dispatch centres in Scandinavia: recognition of out-of-hospital cardiac arrests and dispatcher-assisted CPR.
      • Byrsell F.
      • Claesson A.
      • Jonsson M.
      • et al.
      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.
      To shorten the time to recognition and the dispatch of first resources several steps may be taken. First, the EMDC dispatcher may receive more training in how to recognize a cardiac arrest. Training programs for dispatchers may help reduce these times. Recent studies have investigated the performance of machine learning (ML) algorithms to help in OHCA recognition with mixed results.
      • Blomberg S.N.
      • Folke F.
      • Ersbøll A.K.
      • et al.
      Machine learning as a supportive tool to recognize cardiac arrest in emergency calls.
      • Blomberg S.N.
      • Christensen H.C.
      • Lippert F.
      • et al.
      Effect of Machine Learning on Dispatcher Recognition of Out-of-Hospital Cardiac Arrest during Calls to Emergency Medical Services: A Randomized Clinical Trial.
      Moreover, a simultaneous dispatch of the volunteer responders and fire fighters with the first EMS, would lead to a one-minute gain on the total response time. Since the unit time is in fact shorter for the additional resources, arrival at the scene and start of BLS would be earlier which would benefit the victim and increase the potential for survival. A similar system (GoodSAM) uses an automatic alert based on codes within a computer-assisted dispatch system. However, there are no reports on response times.
      • Smith C.M.
      • Lall R.
      • Fothergill R.T.
      • Spaight R.
      • Perkins G.D.
      The effect of the GoodSAM volunteer first-responder app on survival to hospital discharge following out-of-hospital cardiac arrest.

      Factors influencing the density of resources

      The density of available resources may play a crucial role for a decrease in unit response time. Again, in King County, there is more than 150 fire stations compared to 39 in the Stockholm region (see supplementary figure). This may affect the distance the fire fighters need to travel, and in extension the response time. This is likely the case for the volunteer responders as well. Stieglis et al.
      • Stieglis R.
      • Zijlstra J.A.
      • Riedijk F.
      • Smeekes M.
      • van der Worp W.E.
      • Koster R.W.
      AED and text message responders density in residential areas for rapid response in out-of-hospital cardiac arrest.
      found that areas with more volunteer responders per square km had a shorter time to defibrillation compared to areas with fewer volunteer responders.
      The availability of accessible AEDs is of importance for a reduction in unit response time for volunteer responders. Karlsson et al.
      • Karlsson L.
      • Malta Hansen C.
      • Wissenberg M.
      • et al.
      Automated external defibrillator accessibility is crucial for bystander defibrillation and survival: A registry-based study.
      found that the distance to the nearest AED was associated with bystander defibrillation. The availability of AEDs depends on several factors. For example, the time of day is of importance, where most of the AEDs are unavailable to the public outside business hours (Fig. 5), a plausible reason for the increased unit response time during early morning and in the evening for the AED-responder group (Fig. 4). Previous studies have also shown that public AEDs appears to be placed in areas where only the minority of cardiac arrest occur, such as in business centers.
      • Fredman D.
      • Haas J.
      • Ban Y.
      • et al.
      Use of a geographic information system to identify differences in automated external defibrillator installation in urban areas with similar incidence of public out-of-hospital cardiac arrest: A retrospective registry-based study.
      In a recent study Jonsson et al.
      • Jonsson M.
      • Berglund E.
      • Djärv T.
      • et al.
      A brisk walk-Real-life travelling speed of lay responders in out-of-hospital cardiac arrest.
      found that volunteer responders who try to fetch an AED ran an extra 200 meters compared to those who performed CPR. An increased availability of AEDs, especially in areas with low coverage such as in residential areas, would have the possibility to reduce this distance.
      Figure thumbnail gr5
      Fig. 5Available AEDs in Stockholm and Gothenburg at different time stamps. Right (Monday 07:00), Left (Monday 16:00).

      Potential future actions

      To reduce the time to dispatch of additional resources, such as fire fighters and volunteer responders, the dispatch should be simultaneous to the dispatch of EMS, if not earlier. The present study may contribute as baseline data for a randomized study comparing response times for earlier automatic activation simultaneously with the first EMS versus today’s dispatcher-initiated activation. Indeed, to our and others experience a common complaint among volunteer responders are that they are not able to reach the victim before the EMS.
      • Smith C.M.
      • Griffiths F.
      • Fothergill R.T.
      • Vlaev I.
      • Perkins G.D.
      Identifying and overcoming barriers to automated external defibrillator use by GoodSAM volunteer first responders in out-of-hospital cardiac arrest using the Theoretical Domains Framework and Behaviour Change Wheel: a qualitative study.
      A disadvantage to an automatic dispatch may be that the volunteer responders could be dispatched to unsafe or objectionable situations. Another problem could be that the volunteers as a group would be dispatched more often, also when not needed. A precautious approach should be taken to ensure the wellbeing and motivation of the volunteer responder were safety measures as well as fatigue effects also should be studied.
      To reduce the unit response time the availability of AEDs should be increased. AEDs should preferably be placed outdoors, with access 24/7. An increased focus should be directed to place AEDs in residential areas where the majority of OHCAs occur. Stieglis et al.
      • Stieglis R.
      • Zijlstra J.A.
      • Riedijk F.
      • Smeekes M.
      • van der Worp W.E.
      • Koster R.W.
      AED and text message responders density in residential areas for rapid response in out-of-hospital cardiac arrest.
      found that areas with at least two AEDs per square km had a shorter time to defibrillation than areas with fewer AEDs.
      Solutions such as Unmanned Aerial Vehicles (UAV),
      • Schierbeck S.
      • Nord A.
      • Svensson L.
      • et al.
      National coverage of out-of-hospital cardiac arrests using automated external defibrillator-equipped drones—A geographical information system analysis.
      • Schierbeck S.
      • Hollenberg J.
      • Nord A.
      • et al.
      Automated external defibrillators delivered by drones to patients with suspected out-of-hospital cardiac arrest.
      known as drones could play an important part in reaching the full potential of volunteer responder systems. If the volunteer responders did not have to run the “extra mile” to fetch an AED, they could reach the scene several minutes before the EMS and fire fighters and equipment could instead be flown in to assist.

      Strengths and limitations

      The present study has several strengths. We had digital time stamps from the EMDC regarding time of call, time of dispatch and time of arrival for both EMS and fire fighters. The response time of the fastest dispatched volunteer where objectively measures using GIS methods. There are some limitations as well. We had some missing data on time of arrival for both EMS and fire fighters. In both cases they could have missed to press the button “arrived at scene”. Another explanation (in cases of fire fighters) may be that the dispatch center cancelled the call. Cases where no volunteer responder reached the scene were excluded. The EMS and fire fighter response times may be different in these cases.

      Conclusion

      First arriving volunteer responders had the shortest unit response time when compared to both fire fighters and EMS, however this advantage was reduced by delays introduced at the dispatch center. A randomized trial comparing earlier automatic activation compared to manual criterion-based dispatch should be considered.

      CRediT authorship contribution statement

      E. Berglund: Conceptualization, Methodology, Formal analysis, Investigation, Resources, Data curation, Project administration, Writing - original draft, Writing - review & editing. F. Byrsell: Validation, Resources, Writing - review & editing. A. Nord: Validation, Resources, Writing - review & editing. S. Forsberg: Supervision, Validation, Writing - review & editing. M. Jonsson: Conceptualization, Methodology, Software, Formal analysis, Data curation, Visualization, Project administration, Writing - review & editing.

      Conflict of Interest Statement

      The authors declare no conflicts of interest.

      Acknowledgement

      This work has received funding by The Swedish Heart-Lung Foundation, and the European Union’s Horizon 2020 research and innovation programme (Acronym ESCAPE-NET), under grant agreement No 733381.

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