DÄ internationalArchive33-34/2018The Effect of Ambulance Response Time on Survival Following Out-of-Hospital Cardiac Arrest

Original article

The Effect of Ambulance Response Time on Survival Following Out-of-Hospital Cardiac Arrest

An analysis from the German resuscitation registry

Dtsch Arztebl Int 2018; 115(33-34): 541-8; DOI: 10.3238/arztebl.2018.0541

Bürger, A; Wnent, J; Bohn, A; Jantzen, T; Brenner, S; Lefering, R; Seewald, S; Gräsner, J; Fischer, M

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.

LNSLNS

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) (16). 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) (711). 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 (1214). 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 (1822). 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

Data and facts on the German resuscitation registry
Data and facts on the German resuscitation registry
Box
Data and facts on the German resuscitation registry

Methods

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.

Results

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
resuscitation success

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).

The effect of ambulance response time and bystander CPR on the percentage survival rate following out-of-hospital resuscitation
The effect of ambulance response time and bystander CPR on the percentage survival rate following out-of-hospital resuscitation
Table
The effect of ambulance response time and bystander CPR on the percentage survival rate following out-of-hospital resuscitation

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.

The effect of ambulance response time on the percentage hospital discharge rates of OHCA patients in emergency medical services (N = 10 853) depending on cardiopulmonary resuscitation performed by a bystander
The effect of ambulance response time on the percentage hospital discharge rates of OHCA patients in emergency medical services (N = 10 853) depending on cardiopulmonary resuscitation performed by a bystander
Figure 1
The effect of ambulance response time on the percentage hospital discharge rates of OHCA patients in emergency medical services (N = 10 853) depending on cardiopulmonary resuscitation performed by a bystander
Multivariate logistic regression analysis with the binary outcome measure “Discharge following OHCA.”
Multivariate logistic regression analysis with the binary outcome measure “Discharge following OHCA.”
Figure 2
Multivariate logistic regression analysis with the binary outcome measure “Discharge following OHCA.”
Multivariate logistical regression analysis of the effect of ambulance response time “raising of the alarm to arrival of the 1st vehicle” and bystander cardiopulmonary resuscitation (CPR) on the percentage discharge rate following out-of-hospital resuscitation
Multivariate logistical regression analysis of the effect of ambulance response time “raising of the alarm to arrival of the 1st vehicle” and bystander cardiopulmonary resuscitation (CPR) on the percentage discharge rate following out-of-hospital resuscitation
eTable 1
Multivariate logistical regression analysis of the effect of ambulance response time “raising of the alarm to arrival of the 1st vehicle” and bystander cardiopulmonary resuscitation (CPR) on the percentage discharge rate following out-of-hospital resuscitation

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 number of patients that survive annually following resuscitation by EMS systems as a projection and simulation on the basis of the logistic regression model
The number of patients that survive annually following resuscitation by EMS systems as a projection and simulation on the basis of the logistic regression model
eFigure 1
The number of patients that survive annually following resuscitation by EMS systems as a projection and simulation on the basis of the logistic regression model

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 at the EMS systems level on CPR incidence and resuscitation success in a comparison of 12 faster (9669 resuscitation patients) and 13 slower (7865 resuscitation patients) EMS systems
The effect of ambulance response time at the EMS systems level on CPR incidence and resuscitation success in a comparison of 12 faster (9669 resuscitation patients) and 13 slower (7865 resuscitation patients) EMS systems
eFigure 2
The effect of ambulance response time at the EMS systems level on CPR incidence and resuscitation success in a comparison of 12 faster (9669 resuscitation patients) and 13 slower (7865 resuscitation patients) EMS systems

The effect of ambulance response time on
resuscitation success

Short-term resuscitation success is greater in faster EMS systems (eTable 2):

The effect of ambulance response time at the emergency medical services (EMS) systems level on survival rates (incidence and percent) following pre-hospital resuscitation: a comparison of fast versus slow EMS systems
The effect of ambulance response time at the emergency medical services (EMS) systems level on survival rates (incidence and percent) following pre-hospital resuscitation: a comparison of fast versus slow EMS systems
eTable 2
The effect of ambulance response time at the emergency medical services (EMS) systems level on survival rates (incidence and percent) following pre-hospital resuscitation: a comparison of fast versus slow EMS systems
Survival rates for index patients (VF + CA): a comparison of fast versus slow EMS systems
Survival rates for index patients (VF + CA): a comparison of fast versus slow EMS systems
eTable 3
Survival rates for index patients (VF + CA): a comparison of fast versus slow EMS systems
  • 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).

Discussion

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).

Prognosis following pre-hospital resuscitation by emergency medical services
Prognosis following pre-hospital resuscitation by emergency medical services
eTable 4
Prognosis following pre-hospital resuscitation by emergency medical services

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, 2729). 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 (3032). 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

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

Corresponding author
Prof. Dr. med. Matthias Fischer
Klinik für Anästhesiologie, Intensivmedizin,
Notfallmedizin und Schmerztherapie
Klinik am Eichert der ALB FILS Kliniken
Eichertstr. 3, 73035 Göppingen, Germany
Matthias.Fischer@af-k.de

Supplementary material
eMethods, eTables, eFigures:
www.aerzteblatt-international.de/18m0541

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* These two authors share first authorship.
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:
Sigrid Brenner
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
Data and facts on the German resuscitation registry
Data and facts on the German resuscitation registry
Box
Data and facts on the German resuscitation registry
The effect of ambulance response time on the percentage hospital discharge rates of OHCA patients in emergency medical services (N = 10 853) depending on cardiopulmonary resuscitation performed by a bystander
The effect of ambulance response time on the percentage hospital discharge rates of OHCA patients in emergency medical services (N = 10 853) depending on cardiopulmonary resuscitation performed by a bystander
Figure 1
The effect of ambulance response time on the percentage hospital discharge rates of OHCA patients in emergency medical services (N = 10 853) depending on cardiopulmonary resuscitation performed by a bystander
Multivariate logistic regression analysis with the binary outcome measure “Discharge following OHCA.”
Multivariate logistic regression analysis with the binary outcome measure “Discharge following OHCA.”
Figure 2
Multivariate logistic regression analysis with the binary outcome measure “Discharge following OHCA.”
Key messages
The effect of ambulance response time and bystander CPR on the percentage survival rate following out-of-hospital resuscitation
The effect of ambulance response time and bystander CPR on the percentage survival rate following out-of-hospital resuscitation
Table
The effect of ambulance response time and bystander CPR on the percentage survival rate following out-of-hospital resuscitation
The number of patients that survive annually following resuscitation by EMS systems as a projection and simulation on the basis of the logistic regression model
The number of patients that survive annually following resuscitation by EMS systems as a projection and simulation on the basis of the logistic regression model
eFigure 1
The number of patients that survive annually following resuscitation by EMS systems as a projection and simulation on the basis of the logistic regression model
The effect of ambulance response time at the EMS systems level on CPR incidence and resuscitation success in a comparison of 12 faster (9669 resuscitation patients) and 13 slower (7865 resuscitation patients) EMS systems
The effect of ambulance response time at the EMS systems level on CPR incidence and resuscitation success in a comparison of 12 faster (9669 resuscitation patients) and 13 slower (7865 resuscitation patients) EMS systems
eFigure 2
The effect of ambulance response time at the EMS systems level on CPR incidence and resuscitation success in a comparison of 12 faster (9669 resuscitation patients) and 13 slower (7865 resuscitation patients) EMS systems
Multivariate logistical regression analysis of the effect of ambulance response time “raising of the alarm to arrival of the 1st vehicle” and bystander cardiopulmonary resuscitation (CPR) on the percentage discharge rate following out-of-hospital resuscitation
Multivariate logistical regression analysis of the effect of ambulance response time “raising of the alarm to arrival of the 1st vehicle” and bystander cardiopulmonary resuscitation (CPR) on the percentage discharge rate following out-of-hospital resuscitation
eTable 1
Multivariate logistical regression analysis of the effect of ambulance response time “raising of the alarm to arrival of the 1st vehicle” and bystander cardiopulmonary resuscitation (CPR) on the percentage discharge rate following out-of-hospital resuscitation
The effect of ambulance response time at the emergency medical services (EMS) systems level on survival rates (incidence and percent) following pre-hospital resuscitation: a comparison of fast versus slow EMS systems
The effect of ambulance response time at the emergency medical services (EMS) systems level on survival rates (incidence and percent) following pre-hospital resuscitation: a comparison of fast versus slow EMS systems
eTable 2
The effect of ambulance response time at the emergency medical services (EMS) systems level on survival rates (incidence and percent) following pre-hospital resuscitation: a comparison of fast versus slow EMS systems
Survival rates for index patients (VF + CA): a comparison of fast versus slow EMS systems
Survival rates for index patients (VF + CA): a comparison of fast versus slow EMS systems
eTable 3
Survival rates for index patients (VF + CA): a comparison of fast versus slow EMS systems
Prognosis following pre-hospital resuscitation by emergency medical services
Prognosis following pre-hospital resuscitation by emergency medical services
eTable 4
Prognosis following pre-hospital resuscitation by emergency medical services
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