Machine Learning Assisted Recognition of Out-of-Hospital Cardiac Arrest During Emergency Calls.
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|ClinicalTrials.gov Identifier: NCT04219306|
Recruitment Status : Recruiting
First Posted : January 7, 2020
Last Update Posted : January 7, 2020
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.
|Condition or disease||Intervention/treatment||Phase|
|Out-Of-Hospital Cardiac Arrest||Other: Alert on dispatchers screen 'Suspect cardiac arrest'||Not Applicable|
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 Type :||Interventional (Clinical Trial)|
|Estimated Enrollment :||800 participants|
|Intervention Model:||Parallel Assignment|
|Intervention 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 (Participant, Care Provider, Outcomes Assessor)|
|Official Title:||Can a Machine Learning Recognise of Out-of-Hospital Cardiac Arrest During Emergency Calls and Assist Medical Dispatchers|
|Actual Study Start Date :||September 1, 2018|
|Estimated Primary Completion Date :||March 1, 2020|
|Estimated Study Completion Date :||December 31, 2020|
Experimental: Machine alert
These cardiac suspected cardiac arrest will have had an alert generated by the machine learning model in addition to standard Emergency Medical Services response.
Other: Alert on dispatchers screen 'Suspect cardiac arrest'
Alert on dispatchers screen 'Suspect cardiac arrest'
No Intervention: Usual care
These suspected cardiac arrests will receive standard Emergency Medical Services response.
- 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.
- Time to recognition [ Time Frame: During call to emergency Medical Services, up to 15 minutes from call start. ]Time from call-start until dispatcher recognition of cardiac arrest
- Dispatcher assisted telephone CPR [ Time Frame: During call to emergency Medical Services, up to 15 minutes from call start. ]Does the dispatcher ask caller to initiate CPR.
- Time to T-CPR [ Time Frame: During call to emergency Medical Services, up to 15 minutes from call start. ]Time from call-start until dispatcher starts guiding caller in cpr
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04219306
|Contact: Stig Nikolaj F Blomberg, MSCemail@example.com|
|Contact: Mette Wenoeefirstname.lastname@example.org|
|Emergency Medical Services Copenhagen||Recruiting|
|Ballerup, Danmark, Denmark, DK-2750|
|Contact: Stig Nikolaj F Blomberg, MSC +4560144875 email@example.com|
|Principal Investigator: Stig Nikolaj F Blomberg, MSC|
|Study Director:||Freddy Lippert, MD||Copenhagen Emergency Medical Services|