Can a Machine Learning Recognise of Out-of-Hospital Cardiac Arrest During Emergency Calls and Assist Medical Dispatchers
Phase: NA
Conditions: Out-Of-Hospital Cardiac Arrest
Sponsor: Emergency Medical Services, Capital Region, Denmark

 
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Randomized clinical trial of machine learning assisted 
recognition of Out-of-Hospital Cardiac Arrest during 
emergency calls versus non-assisted recognition of Out-of-
Hospital Cardiac Arrest during emergency calls. The study 
protocol. 
 
NCT number – not yet assigned 
August 2019 
 
Background .................................................................................................................................................... 3 
Aim ................................................................................................................................................................. 4 
Methodology ................................................................................................................................................. 5 
Participants ................................................................................................................................................ 5 
Flow ........................................................................................................................................................... 5 
Intervention ............................................................................................................................................... 6 
Primary outcome measure ........................................................................................................................ 6 
Secondary outcome measures .................................................................................................................. 7 
Sample size ................................................................................................................................................ 7 
Randomization ........................................................................................................................................... 7 
Blinding ...................................................................................................................................................... 7 
Statistical analysis plan .............................................................................................................................. 7 
Timeline and ethics .................................................................................................................................... 8 

 
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List of abbreviations ...................................................................................................................................... 8 
Project organisation ...................................................................................................................................... 9 
Funding .......................................................................................................................................................... 9 
Acknowledgements ..................................................................................................................................... 10 
 
 
 

 
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Background 
Survival from Out-of-Hospital Cardiac Arrest (OHCA) depends on four links in the chain of survival: 
Recognition of the event, cardiopulmonary resuscitation (CPR), defibrillation and post resuscitation care1,2. 
 
 
Early recognition and intervention are critical for patient survival. High quality CPR and defibrillation by 
automated external defibrillator (AED) prior to Emergency Medical Services (EMS) arrival improves survival 
after OHCA3-5. Therefore, the chance of surviving OHCA is highly correlated with bystander and medical 
dispatchers’ recognition of the condition during the emergency calls. 
Previous studies have investigated patterns in medical dispatchers’ recognition of OHCA6-10. 
However, still not all OHCAs are recognised on the telephone when incorporating these patterns in the 
algorithms used by medical dispatchers11. In two recent studies, the recognition rate of OHCA in mergency 
medical dispatch centre Copenhagen (EMDC Copenhagen) was established to be 70 % and 81 %12,13. While 
this is considered a high accuracy, it still leaves room for improvement. 
Bystanders recognise about 30 % of OHCA before calling the emergency number. The remaining OHCA are 
recognised during the interview with the medical dispatcher13. Medical dispatchers often have special 
training in identifying cardiac arrest, however, OHCA constitutes only few percentages of the total call 
volume. The individual medical dispatcher thereby gets little experience in recognising OHCA. To support 
the medical dispatcher in decision-making, most dispatch centre have included algorisms in their dispatch 
system to guide the medical dispatcher. Some dispatch centres supply with systematic ongoing feedback as 
quality assurance. However, the combination of human experiences and use of algorithm does not nearly 
identify all cardiac arrests.     

 
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Although improvements have been made during the past few decades, survival after OHCA remains low3,14-
16. The period immediately after OHCA is critical; each instance that passes in which the patient does not 
receive resuscitation, greatly decreases their chance of survival17,18. The time from collapse to EMS arrival is 
often more than 5 minutes; this delay, coupled with the need for immediate resuscitative support, 
emphasises the critical importance of having early interventions performed by bystanders with guided 
assistance from medical dispatchers.  
At the EMDC-Copenhagen, the medical dispatcher will by the use of ‘Danish Index’, and basic algorithms 
“No-No-Go” usually recognise OHCA. The “No-No-Go” approach is a potential tool for improving OHCA 
recognition. This method is a two-question approach, where the medical dispatcher in every emergency call 
asks the caller if the patient is conscious and if the patient is breathing normally. If the answer for both 
questions is “No”, then relevant EMS units are dispatched, and medical dispatcher-assisted CPR protocols 
are initiated (“Go”). A novel approach to improve recognition of OHCA is to apply machine learning directly 
to the dialogue. 
In a previous project “Can a computer through machine learning recognise of Out-of-Hospital Cardiac 
Arrest during emergency calls” (supported by TrygFoundation), we found, it was possible to create a 
Machine Learning (ML) model, which could recognise OHCA with higher precision than medical dispatchers 
at the EMDC-Copenhagen. 
We now wish to test and document the effect of the model in the EMDC-Copenhagen. For this purpose, we 
have built a computer server running the ML-model. This server is integrated in the network at EMDC-
Copenhagen, making it possible to push alerts to the medical dispatcher, when a cardiac arrest is 
recognised by the model. 
With aid of machine learning, the hypothesis is, that recognition of OHCA is improved, and happen both 
more frequent and faster than present. 
An instruction for the medical dispatchers is developed, which guides the medical dispatcher in instance of 
an alert from the machine.  
Aim 
1. To investigate whether a potential increase in recognitions is due to machine alerts or the 
increased focus of the medical dispatcher on recognizing OHCA when implementing the machine 
2. To investigate if a machine learning model based on neural networks, when alerting medical 
dispatchers will increase overall recognition of OHCA and increase dispatch of citizen responders. 

 
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3. To investigate increased use of AEDs, CPR or dispatch of citizen responders in cases of OHCA on 
machine recognised OHCA vs. medical dispatcher recognised OHCA. 
 
Methodology 
The study has been designed as a prospective, blinded, randomized clinical trial (RCT). Each call where the 
machine learning model suspects a cardiac arrest is by lot (1:1) randomized to either alert on dispatchers’ 
screen or no alert on dispatchers’ screen. 
 This trial will comply with the CONSORT 2010 Statement19 and the SPIRIT guidelines20 and a Statistical 
Analysis Plan (SAP)21 will be included. 
Participants 
800 participants/calls will be included consecutively in the study, from EMS-Copenhagen covering all the 
capital region. 
Inclusion/exclusion criteria are listed in Table 1. 
Table 1 
Inclusion and Exclusion Criteria 
Inclusion Criteria 
Exclusion Criteria: 
- Call regarding a cardiac arrest registered in 
the national Danish Cardiac Arrest Registry 
- OHCA is recognized by machine-learning 
model 
- Call originates from 1-1-2 
 
- OHCA EMS-witnessed 
- Call is from another authority (police or fire 
brigade) 
- Call is a repeat call 
- Call has been on hold for conference 
 
Flow 
All calls to 1-1-2 EMS-Copenhagen are analysed by the machine-learning model. When the model 
recognizes a suspected OHCA, lot is drawn (1:1), and an alert will be shown on screen for the intervention 
group. For the control-group call is answered following usual guidelines and routines. 

 
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Fig. 2  
Intervention 
Based on the randomization of the patient/call, the dispatcher/call taker will either have 1) alert on the 
screen, 2) no help from the machine learning model, and the dispatcher will proceed along the usual 
guidelines. 
All dispatchers from EMS-Copenhagen will participate, and all dispatchers are medically trained call takers, 
and either trained as paramedics or nurse. 
Primary outcome measure 
Dispatcher recognition of out-of-hospital cardiac arrest is the primary outcome. Recognition is reported by 
a questionnaire filled in by a group of auditors listening to recordings of all included calls. The questionnaire 

 
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is a modified CARES protocol for the calls and consists of 21 questions whereby the quality of the call is 
evaluated. The questionnaire is validated and has been used in other studies. 
Secondary outcome measures 
Secondary outcomes measures are derived from the CARES-question are. 
Time to recognition 
Dispatcher assisted CPR initiated 
Time of CPR / first compression 
Sample size 
Based on our previous study22, the difference in recognition between machine learning model and 
dispatcher is 10 %. Setting the significance level (alpha) at 5 % and the power (1-beta) at 95 %, 356 calls is 
needed in each group, resulting in a total study population of 712 calls. This number has been rounded up 
to 800 by the research-group. 
Randomization 
Simple randomization with a 1:1 allocation between intervention and control group. Randomization is done 
at time of (machine) recognition of cardiac arrest. 
The random allocation sequence is made digitally by the machine learning model picking a random number 
between 0 and 1, allocating to the two groups depending on value being more or less than 0.5. 
Blinding 
Randomization is blinded to dispatchers, who will not know whether a call is in the control-group.  
Statistical analysis plan 
Following analysis are performed at the end of the trial for both intervention and control group 
• 
All OHCA are identified with data from the Prehospital Patient Journal to confirm whether there 
was an actual OHCA  
• 
Was OHCA recognized by medical dispatcher  
• 
Was Cardiopulmonary resuscitation (CPR) initiated 
• 
How many seconds before OHCA is recognised 
• 
How many seconds before CPR is initiated 
• 
Follow up on whether the medical dispatcher followed the alert 

 
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• 
How did medical dispatchers with alerts react upon false alerts? 
• 
Was the alert correct 
Analysis is done by comparing  
• 
Recognition of OHCA 
• 
Time of CPR initiation 
• 
Time (seconds) before OHCA is recognized 
• 
Follow up on whether the medical dispatcher followed the alert 
• 
How did medical dispatchers with alerts react upon false alerts? 
Analyse will cover 
1. Median unrecognised time – machine compared to medical dispatchers  
2. Percentage of OHCA recognised – machine compared to medical dispatcher 
3. Characterise machine recognition 
a. False positives 
b. False negatives 
4. Characterise medical dispatcher recognition 
a. False positives 
b. False negatives 
Timeline and ethics 
Recruitment of calls/patients started September 2018. Based on expected incidence of OHCA, inclusion is 
expected to last between 9 and 11 months as a conservative estimate. 
The trial protocol has been approved by the Regional Ethics Committee of the Capital region of Denmark. 
List of abbreviations 
AED — Automated external defibrillator 
CPR — Cardiopulmonary resuscitation 
EMDC — Emergency medical dispatch centre 
EMS — Emergency Medical Services 
OHCA — Out-of-Hospital cardiac arrest 

 
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Project organisation 
Primary investigator 
Stig Nikolaj Blomberg, MSc., PhD-fellow, Specialist Consultant, Emergency Medical Services, Copenhagen, 
The Capital Region of Denmark 
In this project, the primary investigator is responsible for data collection, data analysis, first-drafts of papers 
and publications. In the first year of the study, he has validated data for the algorithm, planned and 
executed the entire project, and made interim analysis. Prior to this, he has through his work in EMS 
Copenhagen extensive knowledge on the mechanisms and processes in a call to EMDC-Copenhagen. He has 
years’ experience as data manager, and an extensive knowledge on the data structure of the EMDC. He also 
has statistical knowledge with respect to both methods and statistics in prehospital research.  
The Co-investigators are from different universities and contribute with interdisciplinary skills. The 
combined group of skills within the project covers project leadership, emergency medicine, 
anaesthesiology, register-based research, register-based epidemiology, statistics,  
Co-investigators: 
Freddy K. Lippert, M.D. Associate professor, The Faculty of Health and Medical Sciences, Department of 
Clinical Medicine, University of Copenhagen, Denmark. CEO, Emergency Medical Services, Copenhagen, The 
Capital Region of Denmark. Contribute with guidance and review. 
Helle Collatz Christensen, M.D. Ph.D., Emergency Medical Services, Copenhagen, The Capital Region of 
Denmark. Contribute with guidance and review. 
Frederik Folke, M.D. Ph.D., Associate professor, Emergency Medical Services, Copenhagen and the Faculty 
of Health and Medical Sciences, Department of Clinical Medicine, University of Copenhagen, Denmark. 
Contribute with guidance, methodological and clinical counselling and review. 
Annette Kjær Ersbøll, MSc, Professor, PhD, National Institute of Public Health, University of Southern 
Denmark. Contribute with guidance, statistical analysis and review. 
 
Funding 
This study is supported by an unrestricted grant from the Danish foundation TrygFonden and The 
Laerdal Foundation. 

 
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Acknowledgements 
We thank Corti.ai who created the machine learning framework. 
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