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

Out-Of-Hospital Cardiac Arrest Clinical Trial

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04219306
• Other study ID #: F-35101-01
• Status: Completed
• Phase: N/A
• Start date: September 1, 2018
• Est. completion date: April 2, 2020

Study information

• Verified date: April 2020
• Source: Emergency Medical Services, Capital Region, Denmark
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical 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. 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 5242
• Est. completion date: April 2, 2020
• Est. primary completion date: April 1, 2020
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

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

Related Conditions & MeSH terms

• Emergencies
• Heart Arrest
• Out-of-Hospital Cardiac Arrest

Intervention

Other:
• Alert on dispatchers screen 'Suspect cardiac arrest'
Alert on dispatchers screen 'Suspect cardiac arrest'

Locations

Country	Name	City	State
Denmark	Emergency Medical Services Copenhagen	Ballerup	Danmark

Sponsors (1)

Lead Sponsor	Collaborator
Emergency Medical Services, Capital Region, Denmark

Country where clinical trial is conducted

Denmark, 

References & Publications (1)

Blomberg SN, Folke F, Ersbøll 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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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.	During call to emergency Medical Services, up to 15 minutes from call start.
Secondary	Time to recognition	Time from call-start until dispatcher recognition of cardiac arrest	During call to emergency Medical Services, up to 15 minutes from call start.
Secondary	Dispatcher assisted telephone CPR	Does the dispatcher ask caller to initiate CPR.	During call to emergency Medical Services, up to 15 minutes from call start.
Secondary	Time to T-CPR	Time from call-start until dispatcher starts guiding caller in cpr	During call to emergency Medical Services, up to 15 minutes from call start.