Out-Of-Hospital Cardiac ArrestNCT04219306
Machine Learning Assisted Recognition of Out-of-Hospital Cardiac Arrest During Emergency Calls.
A study testing Alert on dispatchers screen 'Suspect cardiac arrest' in people with Out-Of-Hospital Cardiac Arrest, run by Emergency Medical Services, Capital Region, Denmark at 1 site in Denmark.
Trial summary
Emergency medical Services Copenhagen has developed a machine learning model that analyzes the calls to 1-1-2 (9-1-1) in real time. The model are able to recognize calls where a cardiac arrest is suspected. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Out-Of-Hospital Cardiac Arrest
- What is being tested?
- Alert on dispatchers screen 'Suspect cardiac arrest'
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 5,242 participants
- Where
- 1 siteDenmark
- Study dates
- 1 Sep 2018to 2 Apr 2020
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Who may be able to join
- 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
Who cannot join
- OHCA Emergency Medical Services - witnessed
- Call is from another authority (police or fire brigade)
- Call is a repeat call
- Call has been on hold for conference
Trial locations and contacts
1 site in 1 country.
Denmark1 site
Emergency Medical Services Copenhagen
Ballerup Municipality, Danmark, DK-2750
About this study
Official title: Can a Machine Learning Recognise of Out-of-Hospital Cardiac Arrest During Emergency Calls and Assist Medical Dispatchers
Emergency medical Services Copenhagen has developed a machine learning model that analyzes the calls to 1-1-2 (9-1-1) in real time. The model are able to recognize calls where a cardiac arrest is suspected. The aim of the study is to investigate the effect of a computer generated alert in calls where cardiac arrest is suspected.
The study will investigate
- whether a potential increase in recognitions is due to machine alerts or the increased focus of the medical dispatcher on recognizing Out-of-Hospital cardiac Arrest (OHCA) when implementing the machine
- 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.
- increased use of automated external defibrillators (AED), cardiopulmonary resuscitation (CPR) or dispatch of citizen responders in cases of OHCA on machine recognised OHCA vs. medical dispatcher recognised OHCA.
Read the detailed description
Chances of survival after out-of-hospital cardiac arrest decrease 10% per minute from collapse until CPR is initiated. dispatcher assisted telephone CPR will be initiated only in cases where the dispatcher recognizes the cardiac arrest.
In a previous project "Can a computer through machine learning recognise of Out-of-Hospital Cardiac Arrest during emergency calls" (supported by TrygFoundation), the investigators found, it was possible to create a Machine Learning (ML) model, which could recognise OHCA with higher precision than medical dispatchers at the Emergency Medical Dispatch Center (EMDC-Copenhagen).
In this study the model andt is effect is to be documented in the EMDC-Copenhagen. For this purpose, a computer server running the ML-model are created. 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.
- Conditions
- Out-Of-Hospital Cardiac Arrest
- Being tested
- Alert on dispatchers screen 'Suspect cardiac arrest'
- Keywords
- Machine learning, Artificial intelligence, Dispatcher assisted telephone CPR, Heart Arrest, Heart Diseases, Cardiovascular Diseases
- Medical subject headings
- Out-of-Hospital Cardiac Arrest, Heart Arrest, Heart Diseases, Cardiovascular Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Alert on dispatchers screen 'Suspect cardiac arrest'Other
Alert on dispatchers screen 'Suspect cardiac arrest'
| Study group | Role | What they receive |
|---|---|---|
| Machine alert | Experimental | These cardiac suspected cardiac arrest will have had an alert generated by the machine learning model in addition to standard Emergency Medical Services response. Receives: Other: Alert on dispatchers screen 'Suspect cardiac arrest' |
| Usual care | No intervention | These suspected cardiac arrests will receive standard Emergency Medical Services response. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Dispatcher recognition of cardiac arrest
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 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.
Measured: During call to emergency Medical Services, up to 15 minutes from call start.
Secondary outcomes (3)
Time to recognition
Time from call-start until dispatcher recognition of cardiac arrest
Measured: During call to emergency Medical Services, up to 15 minutes from call start.
Dispatcher assisted telephone CPR
Does the dispatcher ask caller to initiate CPR.
Measured: During call to emergency Medical Services, up to 15 minutes from call start.
Time to T-CPR
Time from call-start until dispatcher starts guiding caller in cpr
Measured: During call to emergency Medical Services, up to 15 minutes from call start.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Jan 2020
- Study start1 Sep 2018
- Primary completion1 Apr 2020
- Study completion2 Apr 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Out-Of-Hospital Cardiac ArrestStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- 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
- Masking
- Triple blind
- Who is masked
- Participant, Care provider, Outcomes assessor
- Enrolment
- 5,242 participants (actual)
Sponsor and investigators
- Lead sponsor
- Emergency Medical Services, Capital Region, Denmark
- Sponsor type
- Other government
- Responsible party
- Principal investigator, Stig Nikolaj Fasmer Blomberg, Emergency Medical Services, Capital Region, Denmark
- Investigators
- Freddy Lippert, MD, Study director, Copenhagen Emergency Medical Services
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04219306
- Sponsor's protocol ID
- F-35101-01
- Status last verified
- Apr 2020
- First submitted
- 27 Dec 2019
- Last updated
- 16 Apr 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Data will be available upon reasonable request by mail to primary investigator.
Publications
Blomberg SN, Folke F, Ersboll AK, Christensen HC, Torp-Pedersen C, Sayre MR, Counts CR, Lippert FK. Machine learning as a supportive tool to recognize cardiac arrest in emergency calls. Resuscitation. 2019 May;138:322-329. doi: 10.1016/j.resuscitation.2019.01.015. Epub 2019 Jan 18.
PubMed 30664917Blomberg SN, Christensen HC, Lippert F, Ersboll AK, Torp-Petersen C, Sayre MR, Kudenchuk PJ, Folke F. Effect of Machine Learning on Dispatcher Recognition of Out-of-Hospital Cardiac Arrest During Calls to Emergency Medical Services: A Randomized Clinical Trial. JAMA Netw Open. 2021 Jan 4;4(1):e2032320. doi: 10.1001/jamanetworkopen.2020.32320.
PubMed 33404620
Study at a glance
From the registry record, last updated by the sponsor on 16 Apr 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Can a Machine Learning Recognise of Out-of-Hospital Cardiac Arrest During Emergency Calls and Assist Medical Dispatchers
- ClinicalTrials.gov ID
- NCT04219306
- Acronym / sponsor ID
- F-35101-01
- Conditions
- Out-Of-Hospital Cardiac Arrest
- Keywords
- Machine learning, Artificial intelligence, Dispatcher assisted telephone CPR, Heart Arrest, Heart Diseases, Cardiovascular Diseases
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 5,242 participants (actual)
- Lead sponsor
- Emergency Medical Services, Capital Region, DenmarkOther government
- Study start
- 1 Sep 2018 (actual)
- Primary completion
- 1 Apr 2020 (actual)
- Study completion
- 2 Apr 2020 (actual)
- First posted
- 7 Jan 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Emergency medical Services Copenhagen has developed a machine learning model that analyzes the calls to 1-1-2 (9-1-1) in real time. The model are able to recognize calls where a cardiac arrest is suspected. The aim of the study is to investigate the effect of a computer generated alert in calls where cardiac arrest is suspected.
The study will investigate
- whether a potential increase in recognitions is due to machine alerts or the increased focus of the medical dispatcher on recognizing Out-of-Hospital cardiac Arrest (OHCA) when implementing the machine
- 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.
- increased use of automated external defibrillators (AED), cardiopulmonary resuscitation (CPR) or dispatch of citizen responders in cases of OHCA on machine recognised OHCA vs. medical dispatcher recognised OHCA.
Detailed description
Chances of survival after out-of-hospital cardiac arrest decrease 10% per minute from collapse until CPR is initiated. dispatcher assisted telephone CPR will be initiated only in cases where the dispatcher recognizes the cardiac arrest.
In a previous project "Can a computer through machine learning recognise of Out-of-Hospital Cardiac Arrest during emergency calls" (supported by TrygFoundation), the investigators found, it was possible to create a Machine Learning (ML) model, which could recognise OHCA with higher precision than medical dispatchers at the Emergency Medical Dispatch Center (EMDC-Copenhagen).
In this study the model andt is effect is to be documented in the EMDC-Copenhagen. For this purpose, a computer server running the ML-model are created. 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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Triple blind (participant, care provider, outcomes assessor)
- Number of arms
- 2
- Model description
- 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
| Arm | Type | Receives |
|---|---|---|
| Machine alert | Experimental | Other: Alert on dispatchers screen 'Suspect cardiac arrest'These cardiac suspected cardiac arrest will have had an alert generated by the machine learning model in addition to standard Emergency Medical Services response. |
| Usual care | No intervention | –These suspected cardiac arrests will receive standard Emergency Medical Services response. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (4 listed)
- OHCA Emergency Medical Services - witnessed
- Call is from another authority (police or fire brigade)
- Call is a repeat call
- Call has been on hold for conference
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Alert on dispatchers screen 'Suspect cardiac arrest'Alert on dispatchers screen 'Suspect cardiac arrest' | Other | Machine alert |
Primary outcome
Dispatcher recognition of cardiac arrest
Time frame: During call to emergency Medical Services, up to 15 minutes from call start.
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 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.
3 secondary outcomes
Time to recognition
Time frame: During call to emergency Medical Services, up to 15 minutes from call start.
Dispatcher assisted telephone CPR
Time frame: During call to emergency Medical Services, up to 15 minutes from call start.
Time to T-CPR
Time frame: During call to emergency Medical Services, up to 15 minutes from call start.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 Apr 2020.
- Background: PubMed 30664917Blomberg SN, Folke F, Ersboll AK, Christensen HC, Torp-Pedersen C, Sayre MR, Counts CR, Lippert FK. Machine learning as a supportive tool to recognize cardiac arrest in emergency calls. Resuscitation. 2019 May;138:322-329. doi: 10.1016/j.resuscitation.2019.01.015. Epub 2019 Jan 18.
- Derived from this study: PubMed 33404620Blomberg SN, Christensen HC, Lippert F, Ersboll AK, Torp-Petersen C, Sayre MR, Kudenchuk PJ, Folke F. Effect of Machine Learning on Dispatcher Recognition of Out-of-Hospital Cardiac Arrest During Calls to Emergency Medical Services: A Randomized Clinical Trial. JAMA Netw Open. 2021 Jan 4;4(1):e2032320. doi: 10.1001/jamanetworkopen.2020.32320.
- Individual participant dataSharing planned: No. Data will be available upon reasonable request by mail to primary investigator.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Freddy Lippert, MD study director
Copenhagen Emergency Medical Services
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Alert on dispatchers screen 'Suspect cardiac arrest' in people with Out-Of-Hospital Cardiac Arrest, run by Emergency Medical Services, Capital Region, Denmark at 1 site in Denmark.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 16 Apr 2020. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Denmark. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Dispatcher recognition of cardiac arrest (time frame: During call to emergency Medical Services, up to 15 minutes from call start.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04219306, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 16 Apr 2020.