The Effect of Ambulance Response Time on Survival Following Out-of-Hospital Cardiac Arrest
An analysis from the German resuscitation registry
; ; ; ; ; ; ; ;
Background: Out of hospital cardiac arrest (OHCA) is one of the more common causes of death in Germany. Ambulance response time is an important planning parameter for emergency medical services (EMS) systems. We studied the effect of ambulance response time on survival after resuscitation from OHCA.
Methods: We analyzed data from the German Resuscitation Registry for the years 2010–2016. First, we used a multivariate logistic regression analysis to determine the effect of ambulance response time (defined as the interval from the alarm to the arrival of the first rescue vehicle) on the hospital-discharge rate (in percent), depending on various factors, including resuscitation by bystanders. Second, we compared faster and slower EMS systems (defined as those arriving on the scene within 8 minutes in more than 75% of cases or in ≤ 75% of cases) with respect to the frequency of resuscitation and the number of surviving patients.
Results: Our analysis of data from a total of 10 853 patients in the logistical regression model revealed that the rate of hospital discharge was significantly affected by the ambulance response time, bystander resuscitation, past medical history, age, witnessed vs. unwitnessed collapse, the initial heart rhythm, and the site of the collapse. The success of resuscitation was inversely related to the ambulance response time; thus, among patients who did not receive bystander resuscitation, the discharge rate declined from 12.9% at a mean response time of 1 minute and 10 seconds to 6.4% at a mean response time of 9 minutes and 47 seconds. Twelve faster EMS systems and 13 slower ones were identified, with a total of 9669 and 7865 resuscitated patients, respectively. The faster EMS systems initiated resuscitation more frequently and also had a higher discharge rate with good neurological outcome in proportion to the population of the catchment area (7.7 versus 5.6 persons per 100 000 population per year, odds ratio [OR] 0.72, 95% confidence interval [0.66; 0.79], p<0.001).
Conclusion: Rapid ambulance response is associated with a higher rate of survival from OHCA with good neurological outcome. The response time, independently of whether bystander resuscitation measures are provided, ha^ a significant independent effect on the survival rate. In drawing conclusions from these findings, one should bear in mind that this was a retrospective registry study, with the corresponding limitations.
With a mean annual incidence of 84 events per 100 000 population (range, 28–244), out-of-hospital cardiac arrest (OHCA) is one of the commonest causes of death in Germany and Europe (1). Approximately 50% of patients with sudden cardiac arrest die without any attempt being made at resuscitation, since either the event is not observed by bystanders or the emergency medical services (EMS) reach the scene of the emergency too late. The annual incidence of resuscitation is on average 49 per 100 000 population (range, 19–104) (1–6). EMS response time is an important planning factor for emergency medical services. A well-well-functioning chain of survival—starting with resuscitation by bystanders or the emergency control center dispatcher who takes the emergency call, up to the hospital team that provides the patient with intensive care treatment—is a prerequisite for good long-term results. In order to improve the treatment of patients with OHCA and integrate current research results, the European Resuscitation Council regularly publishes new guidelines on cardiopulmonary resuscitation (CPR) (7–11). Despite the implementation of these guidelines and EMS systems having comparable training and equipment, the results of out-of-hospital CPR are subject to considerable variation between the different EMS systems in Germany (12–14). The German Society for Anesthesiology and Intensive Care Medicine (Deutsche Gesellschaft für Anästhesiologie und Intensivmedizin) set up the German Resuscitation Registry (Deutsches Reanimationsregister) in 2007 as a comprehensive quality management instrument designed to measure and continuously improve treatment success. A number of studies suggest that the interval without CPR affects the outcome of resuscitation (18–22). To date, it has been demonstrated that the ambulance response time affects CPR incidence as well as short-term and long-term survival (14, 18). The aim of this study was to investigate the effect of ambulance response time on survival rates, particularly on neurological recovery following out-of-hospital cardiac arrest, in Germany
This cohort study was based on anonymized patient data from the German Resuscitation Registry for the period 1 January 2010 to 31 December 2016. All prehospital patients that had experienced out-of-hospital cardiac arrest were included, irrespective of whether CPR was initiated by trained emergency medical services personnel or by bystanders and, in particular, irrespective of the success of CPR. Data collection by the German Resuscitation Registry is voluntary and takes place anonymously. Data is entered by emergency physicians or emergency service personnel, and it is usually released by the medical directors of the emergency medical service or by persons assigned by them. EMS systems that had carried out resuscitation treatment fewer than 100 times during the observation period and had less than 12 reporting months, as well as centers with incomplete documentation on follow-up treatment (response rate after hospital admission <80%), were excluded from the study, as were helicopter centers, since emergency medical service laws make no provision for these in terms of evaluating response time intervals. In addition, patients in whom the emergency medical services witnessed the collapse, a non-cardiac cause was responsible, or for whom the information on bystander CPR or on the “time to arrival of first vehicle” was lacking were excluded during the first part of the study.
The outcome measure was the success of resuscitation treatment administered by the emergency medical services. To this end, the following were each calculated as a percentage of resuscitated patients
- Return of spontaneous circulation (ROSC rate)
- Hospital admission rate
- Hospital discharge rate
- Discharge rate with good neurological outcome (CPC [cerebral performance categories] 1 or 2)
The second part of the study determined not only the effect of ambulance response time on survival rates as percentages, but also the annual number of resuscitated and surviving patients per 100 000 population, in two groups of faster and slower EMS systems (see the eMethods section for a detailed description).
The variable ambulance response time was measured as the time between “raising the alarm and arrival of the first vehicle at the scene.”
The first part of the study evaluated the effect of ambulance response time and bystander CPR on the dependent variable “discharge from hospital” by means of a multivariate logistic regression analysis. The following additional variables—based on the ROSC after cardiac arrest score (RACA score) (23, 24)—were included in the analysis: age, sex, location of cardiac arrest, event observed, first ECG rhythm, and pre-emergency status, which includes information on pre-existing diseases. Categorized variables were created for the multivariate logistic regression analyses: ambulance response time (0–2, 3–5, 6–8, 9–11, and ≥ 12 min), bystander CPR (without, with and without telephone assistance), age (<60, 60–69, 70–79, and ≥ 80 years), pre-existing diseases (unknown, without pre-existing disease, pre-existing disease without impairment, pre-existing disease with impairment, and pre-existing disease that renders normal life impossible), initial shockable rhythm (yes/no), witnessed event (yes/no), location of event: in public/medical practice (yes/no), and male sex (yes/no).
The Ethics Committee of the University of Lübeck, Germany, voted to approve structural analyses based on the German Resuscitation Registry (file number 12–226).
Data was processed using Excel 2017 software (Microsoft Corporation, Redmond, WA, USA) and IBM SPSS Statistics (IBM, Armonk, NY, USA). The multivariate logistic regression analysis was carried out in such a way that potential variables were included at p<0.05 and excluded at p>0.1. Regression coefficients, odd ratios, and confidence intervals were calculated for the variables included. Using the regression coefficients determined, a prediction model for the personalized probability of discharge was created. This model was used to simulate the effects of shortened ambulance response times and increased rates of bystander CPR on the number of survivors. To this end, the potential ambulance response time was reduced on a percentage basis or the bystander CPR that had not been performed was randomized as performed in the respective datasets. The projection for Germany was based on the calculated or simulated discharge rates, an annual resuscitation incidence of 66/100 000 population (1), and a total population of 82.67 million.
The tables show values as mean values or weighted mean values. Odds ratios and confidence intervals were calculated for group comparisons. Other statistical analyses were performed using T-tests, chi-square tests, and Bonferroni correction where required; statistical significance was set at p<0.05.
According to the inclusion and exclusion criteria,
10 853 CPR patients from 25 emergency service areas were included in the first part of the study and 17 534 in the second part. Of these 17 534 data sets, the following were excluded from the multivariate logistic regression analysis:
- 3537 Patients due to non-cardiac cause
- 1036 Observed by emergency medical services (irrelevant to ambulance response time)
- 2108 Due to lack of information on individual ambulance response time
The effect of ambulance response time on
The raw data showed that the rate of resuscitation success decreased with increasing ambulance response time (Table). The discharge rate fell from 22.0% to 14.0% if patients received bystander CPR and the mean ambulance response time rose from 1:04 to 9:47 min (odds ratio [OR]: 1.73; 95% confidence interval: [1.26; 2.37]; p<0.001). If no bystander CPR was performed, the discharge rate dropped from 12.9% to 6.4% if the mean ambulance response time rose from 1:10 to 9:47 min (OR: 2.18 [1.53; 3.12]; p<0.001).
On the other hand, the short-term and long-term survival after resuscitation was greater in both time groups if patients had received bystander/first responder resuscitation. For example, 18.4% of patients were discharged with good neurological outcome after an ambulance response time of 0–2 min if they had received bystander resuscitation and only 10.2% if bystander resuscitation had not been performed (OR: 2.0 [1.41; 2.83]; p<0.001). If the mean ambulance response time extended to 9:47 min, only 10.4% of patients with bystander resuscitation, and as little as 4.5% without bystander resuscitation, could be discharged with good neurological outcome (OR: 2.47 [1.64; 3.73]; p<0.001).
eTable 1 and Figure 1 show the results of the multivariate logistic regression analysis. In a seven-factor model, ambulance response time, bystander resuscitation, age, pre-existing disease, location of collapse, witnessed status, and initial heart rhythm significantly affect the probability of being discharged alive from the hospital following resuscitation by EMS systems. Sex has no effect on the discharge rate in this model. The model achieves a value of 0.296 according to Nagelkerke‘s R squared. eTable 1 shows the corresponding regression coefficients, standard errors, and significance levels for this model. Figure 2 gives the odds ratio and confidence interval for each variable and category in a forest plot. Discharge rates according to the raw data on the 10 853 patients and adjusted by the regression model are shown in Figure 1.
eFigure 1 gives the number of patients that survive annually following resuscitation by emergency medical services as a projection and simulation on the basis of the logistic regression model. At a measured discharge rate of 13%, this means that 7091 patients survive per year in Germany. By reducing the individual ambulance response times by 10%, 20%, or 30%, the number of survivors increases annually by 370, 515, or 634 patients, respectively. If the rate of bystander resuscitation could be raised by 20 or 40 percentage points to 47% or 67%, an additional 245 or 426 patients could be saved each year. By simulating a 20% reduction in ambulance response time combined with an increase in the rate of bystander resuscitation to 47%, as many as 771 more patients/year could be saved, amounting to an annual total of 7862 patients saved in Germany by EMS systems following sudden cardiac arrest and resuscitation
The effect of ambulance response time on the
EMS system level
The 12 faster (n = 9669) and 13 slower (n = 7865) EMS systems reached 85.8% versus 67.9% of patients, respectively, with the first vehicle arriving within 8 min. Slower emergency medical services initiated resuscitation significantly more rarely, i.e., with an annual incidence of 59.3 compared with 70.3 patients/100 000 population (OR = 0.84 [0.82; 0.87]; p <0.001) (eFigure 2).
The effect of ambulance response time on
Short-term resuscitation success is greater in faster EMS systems (eTable 2):
- All-time incidence of ROSC: 26.7 patients/100 000 population/year (slower EMS systems) versus 32.9 (OR: 0.81 [0.78; 0.85]; p<0.001)
- Incidence of hospital admission: 22.6 patients/
100 000 population/year (slower EMS systems) versus 27.9 (OR: 0.81 [0.77; 0.85]; p<0.001).
More patients show good long-term results in faster EMS systems:
- Incidence of patients discharged alive: 7.3 patients/100 000 population/year (slower EMS systems) versus 9.7 (OR: 0.75 [0.69; 0.82]; p<0.001)
- Incidence of discharge with good neurological outcome (CPC 1/2): 5.6 patients/100 000 population/year (slower EMS systems) versus 7.7 (OR: 0.72 [0.66; 0.79]; p<0.001).
The results of the multivariate logistic regression analysis showed that ambulance response time and bystander resuscitation significantly affect survival following out-of-hospital cardiac arrest, in addition to age, pre-existing disease, witnessed status, location of collapse, and initial heart rhythm. The simulation showed that both reduced ambulance response times and higher bystander resuscitation rates can significantly increase the number of patients that survive. Categorizing the variable “ambulance response time” confers the advantage that non-linear associations can also be readily modeled. When considered as a continuous variable, on the other hand, one calculates an effect “per minute.” However, in further logistic regression analyses, the effect of ambulance response time—also as a continuous variable—on both the probability of discharge and on good neurological recovery was the same; there was a reduction in the probability of survival of 5% per minute prolongation of ambulance response time (eTables 4a and 4b).
The second part of the study additionally showed that faster EMS systems—standardized to the number of inhabitants—perform CPR on more patients and that more patients leave the hospital with good neurological outcome following OHCA. This is all the more remarkable given that the likelihood of ROSC did not differ between faster and slower EMS systems and that the quality of medical treatment was comparable
Therefore, it is highly likely that shorter ambulance response time intervals result in higher survival rates, individually and as a planning factor, since shorter response time intervals enable trained emergency service personnel to carry out optimal chest compression earlier, ensure oxygenation earlier by means of airway management, O2 administration, and ventilation, and administer vasopressors earlier. In addition, more patients are found to be in a shockable rhythm at shorter ambulance response times, and defibrillation is performed earlier (21, 27–29). In summary, these measures result in better oxygen supply to the heart and brain, earlier defibrillation, and therefore to better resuscitation outcomes.
In line with this, a recent study based on the Danish resuscitation registry showed that, with increasing ambulance response times, the 30-day survival rate drops comparably fast as does the discharge rate in this study (18). In both studies, bystander resuscitation doubled the chances of survival at ambulance response times of under 5 min (OR: 1.8–2.4). In contrast to the data in this study, the Danish study showed that bystander resuscitation no longer conferred a significant benefit at an ambulance response time of 13 min. It was possible to simulate for Denmark, with a population of around 5.75 million, that shortening the average ambulance response time from 7 to 5 min could save 119 lives or 2.1 lives/100 000 population per year. This is higher than the simulation here and comparable to our data at the emergency medical services level, which showed an increase of 2.4 patients discharged alive/100 000 population per year at shorter EMS system response time intervals
The results of the two studies highlight the fact that short ambulance response times are vital and that further efforts need to be made to shorten both ambulance response times and the time to resuscitation (30–32). The German Resuscitation Council (Deutscher Rat für Wiederbelebung) White Paper on resuscitation management calls for ambulance response times within 8 min to be achieved in 85% of cases uniformly across Germany (33). Due to different legislations in the German federal states, there are 16 regulations on ambulance response times in Germany to date, the significance and influence of which have been subjected to only scant scientific examination. Shortening ambulance response times is needed and also feasible by increasing the provision of emergency service units and optimizing logistics (e.g., location optimization or a “comprehensive next vehicle strategy”).
Since the considerable importance of bystander CPR has been demonstrated, the rate and quality of bystander resuscitation needs to be improved. Emergency medical services are called upon to implement telephone-guided CPR throughout Germany (7, 34, 35). Emergency medical services can also introduce new systems that use smartphone location data to guide pre-registered and trained laypersons to CPR patients (36, 37). The performance of resuscitation by bystanders should become a matter of course. To achieve this, training should be provided as early on as at school age (11). Since cardiac arrest occurs more frequently in the homes of older people, all age groups need to be mobilized to perform bystander resuscitation. Initiatives such as the German “Woche der Wiederbelebung” (“Resuscitation Week”) are designed to encourage ever more individuals to save lives in cases of out-of-hospital cardiac arrest (38).
The study clearly highlights the positive effect of shorter ambulance response times and bystander resuscitation on survival following out-of-hospital cardiac arrest and resuscitation. Further joint efforts need to be made to broadly disseminate resuscitation knowledge in the general population and to bring ambulance response times in line with medical requirements.
Limitations need to be taken into account when evaluating results. The German Resuscitation Registry operates on a voluntary basis, which explains why 134 of 241 emergency medical services in Germany currently submit data to the registry. At present, only 41 emergency medical services are able to supply follow-up data on hospital treatment for the evaluation of long-term outcomes, in part due to data protection requirements. This limits representativeness as a result. Participants are responsible for ensuring data quality; this can no longer be verified following data entry, since data are transmitted to the registry in anonymized form. Resuscitation outcome is affected by the quality of chest compression (frequency, depth, pauses), on which the resuscitation registry does not collect data. As such, it is possible that unrecorded variables and confounders might have affected the outcome.
Conflict of interests
Dr. Wnent, Dr. Bohn, Prof. Jantzen, Mrs. Brenner, Prof. Gräsner, Dr. Seewald und Prof. Fischer are members of the steering committee of the German Resuscitation Registry.
The remaining authors state that they have no conflicts of interest.
Manuscript submitted on 30 December 2017, revised version accepted on
22 May 2018
Translated from the original German by Christine Schaefer-Tsorpatzidis
Prof. Dr. med. Matthias Fischer
Klinik für Anästhesiologie, Intensivmedizin,
Notfallmedizin und Schmerztherapie
Klinik am Eichert der ALB FILS Kliniken
Eichertstr. 3, 73035 Göppingen, Germany
eMethods, eTables, eFigures:
Department of Anesthesiology, Intensive Care Medicine, Emergency Medicine,
and Pain Therapy, Klinik am Eichert, ALB FILS Kliniken, Göppingen:
Andreas Bürger, Prof. Dr. med. Matthias Fischer
Institute for Emergency Medicine and Department of Anesthesiology and Intensive Care Medicine, Kiel Campus, University Hospital Schleswig-Holstein:
Dr. med. Jan Wnent, Dr. med. Stephan Seewald, Prof. Dr. med. Jan-Thorsten Gräsner
City of Münster, Fire Department: PD Dr. med. Andreas Bohn
Intensive Care Transport Mecklenburg-Vorpommern, German Red Cross Parchim:
Prof. Dr. med. Tanja Jantzen
Department of Anesthesiology, Carl Gustav Carus University Hospital, Dresden:
Faculty of Medicine, Institute for Research in Operative Medicine, Department of Statistics and Registry Research, Witten/Herdecke University, Cologne, Germany:
Prof. Dr. rer. medic. Rolf Lefering
|1.||Gräsner JT, Lefering R, Koster RW, et al.: EuReCa ONE-27 Nations, ONE Europe, ONE Registry: a prospective one month analysis of out-of-hospital cardiac arrest outcomes in 27 countries in Europe. Resuscitation 2016; 105: 188–95 CrossRef MEDLINE|
|2.||Berdowski J, Berg RA, Tijssen JGP, Koster RW: Global incidences of out-of-hospital cardiac arrest and survival rates: systematic review of 67 prospective studies. Resuscitation 2010; 81: 1479–87 CrossRef MEDLINE|
|3.||Messelken M, Kehrberger E, Dirks B, Fischer M: The quality of emergency medical care in Baden-Württemberg (Germany): four years in focus. Dtsch Arztebl Int 2010; 107: 523–30 VOLLTEXT|
|4.||Nürnberger A, Sterz F, Malzer R, et al.: Out of hospital cardiac arrest in Vienna: incidence and outcome. Resuscitation 2013; 84: 42–7 CrossRef MEDLINE|
|5.||Atwood C, Eisenberg MS, Herlitz J, Rea TD: incidence of EMS-treated out-of-hospital cardiac arrest in Europe. Resuscitation 2005; 67: 75–80 CrossRefMEDLINE|
|6.||Hawkes C, Booth S, Ji C, et al.: Epidemiology and outcomes from out-of-hospital cardiac arrests in England. Resuscitation 2017; 110: 133–40 CrossRef MEDLINE|
|7.||Perkins GD, Handley AJ, Koster RW, et al.: European Resuscitation Council guidelines for resuscitation 2015: section 2. Adult basic life support and automated external defibrillation. Resuscitation 2015; 95: 81–99 CrossRef CrossRef|
|8.||Soar J, Nolan JP, Böttiger BW, et al.: European Resuscitation Council guidelines for resuscitation 2015: section 3. Adult advanced life support. Resuscitation 2015; 95: 100–47 CrossRef CrossRef MEDLINE|
|9.||Truhlář A, Deakin CD, Soar J, et al.: European Resuscitation Council guidelines for resuscitation 2015: section 4. Cardiac arrest in special circumstances. Resuscitation 2015; 95: 148–201 CrossRef MEDLINE|
|10.||Zideman DA, Buck EDJ de, Singletary EM, et al.: European Resuscitation Council guidelines for resuscitation 2015: section 9. First aid. Resuscitation 2015; 95: 278–87 CrossRef CrossRef MEDLINE|
|11.||Greif R, Lockey AS, Conaghan P, Lippert A, Vries W de, Monsieurs KG: European Resuscitation Council guidelines for resuscitation 2015: section 10. Education and implementation of resuscitation. Resuscitation 2015; 95: 288–301 CrossRef MEDLINE|
|12.||Weber TP, Bein B, Möllenberg O, et al.: 70.000 Todesfälle nach erfolgloser Wiederbelebung sind inakzeptabel. Notfall Rettungsmed 2014; 17: 317–8 CrossRef|
|13.||Gräsner JT, Wnent J, Seewald S, et al.: Jahresbericht Außerklinische Reanimation 2016 des Deutschen Reanimationsregisters. Anästh Intensivmed 2017; 58: 365–6.|
|14.||Neukamm J, Gräsner JT, Schewe JC, et al.: The impact of response time reliability on CPR incidence and resuscitation success: a benchmark study from the German resuscitation registry. Crit Care 2011; 15: R282 CrossRef MEDLINE PubMed Central|
|15.||Fischer M, Messelken M, Wnent J, et al.: Deutsches Reanimationsregister der DGAI. Notfall Rettungsmed 2013; 16: 251–9 CrossRef|
|16.||Gräsner JT, Meybohm P, Fischer M, et al.: A national resuscitation registry of out-of-hospital cardiac arrest in Germany—a pilot study. Resuscitation 2009; 80: 199–203 CrossRef MEDLINE|
|17.||Perkins GD, Jacobs IG, Nadkarni VM, et al.: Cardiac arrest and cardiopulmonary resuscitation outcome reports: update of the Utstein Resuscitation Registry Templates for out-of-hospital cardiac arrest: a statement for healthcare professionals from a task force of the International Liaison Committee on Resuscitation. Circulation 2015; 132: 1286–300 CrossRef MEDLINE|
|18.||Rajan S, Wissenberg M, Folke F, et al.: Association of bystander cardiopulmonary resuscitation and survival according to ambulance response times after out-of-hospital cardiac arrest. Circulation 2016; 134: 2095–104 CrossRef MEDLINE|
|19.||Hasselqvist-Ax I, Riva G, Herlitz J, et al.: Early cardiopulmonary resuscitation in out-of-hospital cardiac arrest. N Engl J Med 2015; 372: 2307–15 CrossRef MEDLINE|
|20.||Hollenberg J, Bång A, Lindqvist J, et al.: Difference in survival after out-of-hospital cardiac arrest between the two largest cities in Sweden: a matter of time? J Intern Med 2005; 257: 247–54 CrossRef MEDLINE|
|21.||Vukmir RB: Survival from prehospital cardiac arrest is critically dependent upon response time. Resuscitation 2006; 69: 229–34 CrossRef MEDLINE|
|22.||Fischer M, Fischer NJ, Schüttler J: One-year survival after out-of-hospital cardiac arrest in Bonn city: outcome report according to the „Utstein style“. Resuscitation 1997; 33: 233–43 CrossRef|
|23.||Gräsner JT, Meybohm P, Lefering R, et al.: ROSC after cardiac arrest—the RACA score to predict outcome after out-of-hospital cardiac arrest. Eur Heart J 2011; 32: 1649–56 CrossRef MEDLINE|
|24.||Kupari P, Skrifvars M, Kuisma M: External validation of the ROSC after cardiac arrest (RACA) score in a physician staffed emergency medical service system. Scand J Trauma Resusc Emerg Med 2017; 25: 34 CrossRef MEDLINE PubMed Central|
|25.||Sanfilippo F, Corredor C, Santonocito C, et al.: Amiodarone or lidocaine for cardiac arrest: a systematic review and meta-analysis. Resuscitation 2016; 107: 31–7 CrossRef MEDLINE|
|26.||McLeod SL, Brignardello-Petersen R, Worster A, et al.: Comparative effectiveness of antiarrhythmics for out-of-hospital cardiac arrest: a systematic review and network meta-analysis. Resuscitation 2017; 121: 90–7 CrossRef MEDLINE|
|27.||Stein P, Spahn GH, Müller S, et al.: Impact of city police layperson education and equipment with automatic external defibrillators on patient outcome after out of hospital cardiac arrest. Resuscitation 2017; 118: 27–34 CrossRef MEDLINE|
|28.||Hansen CM, Kragholm K, Granger CB, et al.: The role of bystanders, first responders, and emergency medical service providers in timely defibrillation and related outcomes after out-of-hospital cardiac arrest: results from a statewide registry. Resuscitation 2015; 96: 303–9 CrossRef MEDLINE|
|29.||Rajan S, Folke F, Hansen SM, et al.: Incidence and survival outcome according to heart rhythm during resuscitation attempt in out-of-hospital cardiac arrest patients with presumed cardiac etiology. Resuscitation 2017; 114: 157–63 CrossRef MEDLINE|
|30.||Riddersholm S, Kragholm K, Mortensen RN, et al.: Association of bystander interventions and hospital length of stay and admission to intensive care unit in out-of-hospital cardiac arrest survivors. Resuscitation 2017; 119: 99–106 CrossRef MEDLINE|
|31.||Nordberg P, Hollenberg J, Herlitz J, Rosenqvist M, Svensson L: Aspects on the increase in bystander CPR in Sweden and its association with outcome. Resuscitation 2009; 80: 329–33 CrossRef MEDLINE|
|32.||Wissenberg M, Lippert FK, Folke F, et al.: Association of national initiatives to improve cardiac arrest management with rates of bystander intervention and patient survival after out-of-hospital cardiac arrest. JAMA 2013; 310: 1377–84 CrossRef MEDLINE|
|33.||Beckers SK (ed.): Weißbuch Reanimationsversorgung: Empfehlungen zu Struktur, Organisation, Ausbildung und Qualitätssicherung der Reanimationsversorgung in Deutschland. Köln: Deutscher Ärzteverlag 2015.|
|34.||Wu Z, Panczyk M, Spaite DW, et al.: Telephone cardiopulmonary resuscitation is independently associated with improved survival and improved functional outcome after out-of-hospital cardiac arrest. Resuscitation 2018; 122: 135-40 CrossRef MEDLINE|
|35.||Bobrow BJ, Spaite DW, Vadeboncoeur TF, et al.: Implementation of a regional telephone cardiopulmonary resuscitation program and outcomes after out-of-hospital cardiac arrest. JAMA Cardiol 2016; 1: 294–302 CrossRef MEDLINE|
|36.||Pijls RWM, Nelemans PJ, Rahel BM, Gorgels APM: A text message alert system for trained volunteers improves out-of-hospital cardiac arrest survival. Resuscitation 2016; 105: 182–7 CrossRef MEDLINE|
|37.||Caputo ML, Muschietti S, Burkart R, et al.: Lay persons alerted by mobile application system initiate earlier cardio-pulmonary resuscitation: a comparison with SMS-based system notification. Resuscitation 2017; 114: 73–8 CrossRef MEDLINE|
|38.||Bein B, Koch T, Geldner G, Böttiger BW, Gräsner JT: Notfallversorgung in Deutschland: Transformation und Trends 2017; 114: 2152–6.|
Turkish Journal of Family Medicine and Primary Care, 202010.21763/tjfmpc.638035
Investigating the Geographic Disparities of Amenable Mortality and Related Ambulance Services in HungaryInternational Journal of Environmental Research and Public Health, 202110.3390/ijerph18031065
Frontiers in Digital Health, 202110.3389/fdgth.2021.654234
First responder systems can stay operational under pandemic conditions: results of a European survey during the COVID-19 pandemicScandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 202210.1186/s13049-022-00998-3
BMC Health Services Research, 202210.1186/s12913-022-08445-w
Clinical, Operational, and Socioeconomic Analysis of EMS Bypass of the Closest Facility for Pediatric Asthma PatientsWestern Journal of Emergency Medicine, 202110.5811/westjem.2021.4.50382
Opportunities and Risks of Resuscitation Attempts in Nursing Homes: Facts for Nursing Home Residents and CaregiversDeutsches Ärzteblatt international, 202010.3238/arztebl.2020.0757
Neurologisches Ergebnis und allgemeiner Gesundheitszustand bei Langzeitüberleben nach außerklinischer kardiopulmonaler ReanimationNotfall + Rettungsmedizin, 202110.1007/s10049-021-00929-2
Transportation Research Part D: Transport and Environment, 202010.1016/j.trd.2020.102410
ISPRS International Journal of Geo-Information, 202210.3390/ijgi11020109
Outpatient care in acute and prehospital emergency medicine by emergency medical and patient transport service over a 10-year period: a retrospective study based on dispatch data from a German emergency medical dispatch centre (OFF-RESCUE)BMC Emergency Medicine, 202110.1186/s12873-021-00424-4
2020 IEEE 20th Mediterranean Electrotechnical Conference ( MELECON)10.1109/MELECON48756.2020.9140544
Deutsches Ärzteblatt international, 202010.3238/arztebl.2020.0871
Mathematical and Computational Applications, 202110.3390/mca26030056
Journal Club AINS, 202210.1055/a-1552-8268
Analyse eines First-responder-Systems zur notfallmedizinischen Versorgung im ländlichen Raum: erste Ergebnisse und ErfahrungenDer Anaesthesist, 201910.1007/s00101-019-00635-2