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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
Sponsor:
Information provided by (Responsible Party):
Stig Nikolaj Fasmer Blomberg, Emergency Medical Services, Capital Region, Denmark

Brief 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. 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

  1. 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
  2. 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.
  3. 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

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.

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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 800 participants
Allocation: Randomized
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)
Primary Purpose: Diagnostic
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

Resource links provided by the National Library of Medicine


Arm Intervention/treatment
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.



Primary Outcome Measures :
  1. 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.


Secondary Outcome Measures :
  1. 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

  2. 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.

  3. 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



Information from the National Library of Medicine

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Ages Eligible for Study:   Child, Adult, Older Adult
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Criteria

Inclusion 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

Exclusion Criteria:

  • 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

Information from the National Library of Medicine

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


Contacts
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Contact: Stig Nikolaj F Blomberg, MSC +4560144875 stig.nikolaj.fasmer.blomberg@regionh.dk
Contact: Mette Wenoee +4524911432 mette.wenoee@regionh.dk

Locations
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Denmark
Emergency Medical Services Copenhagen Recruiting
Ballerup, Danmark, Denmark, DK-2750
Contact: Stig Nikolaj F Blomberg, MSC    +4560144875    nikolaj.blomberg@gmail.com   
Principal Investigator: Stig Nikolaj F Blomberg, MSC         
Sponsors and Collaborators
Emergency Medical Services, Capital Region, Denmark
Investigators
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Study Director: Freddy Lippert, MD Copenhagen Emergency Medical Services
  Study Documents (Full-Text)

Documents provided by Stig Nikolaj Fasmer Blomberg, Emergency Medical Services, Capital Region, Denmark:

Publications:
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Responsible Party: Stig Nikolaj Fasmer Blomberg, PHD-fellow, Emergency Medical Services, Capital Region, Denmark
ClinicalTrials.gov Identifier: NCT04219306    
Other Study ID Numbers: F-35101-01
First Posted: January 7, 2020    Key Record Dates
Last Update Posted: January 7, 2020
Last Verified: January 2020
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No
Plan Description: Data will be available upon reasonable request by mail to primary investigator.

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Stig Nikolaj Fasmer Blomberg, Emergency Medical Services, Capital Region, Denmark:
Machine learning
Artificial intelligence
Dispatcher assisted telephone CPR
Heart Arrest
Heart Diseases
Cardiovascular Diseases
Additional relevant MeSH terms:
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Heart Arrest
Out-of-Hospital Cardiac Arrest
Emergencies
Disease Attributes
Pathologic Processes
Heart Diseases
Cardiovascular Diseases