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Completed

NCT Number: NCT04219306

Machine Learning Assisted Recognition of Out-of-Hospital Cardiac Arrest During Emergency Calls.

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.

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Key information

Conditions

Out-Of-Hospital Cardiac Arrest Cardiovascular Diseases Heart Arrest Heart Diseases

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Emergency Medical Services Copenhagen

Ballerup Municipality, Danmark, DK-2750, Denmark

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Alert on dispatchers screen 'Suspect cardiac arrest'

Other

Alert on dispatchers screen 'Suspect cardiac arrest'

Primary outcomes

  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 outcomes

  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

Sponsors and collaborators

Lead sponsor

Emergency Medical Services, Capital Region, Denmark

Other Gov

Registry information

Official study title

Can a Machine Learning Recognise of Out-of-Hospital Cardiac Arrest During Emergency Calls and Assist Medical Dispatchers

Important dates

Study start
2018
Primary completion
2020
Study completion
2020
First posted
Jan 7, 2020
Registry last updated
Apr 16, 2020

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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