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

Emergency Medical Services, Capital Region, Denmark

Status: Completed

Conditions: Out-Of-Hospital Cardiac Arrest

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

Study type: Interventional

Funder types: Other

Identifiers

NCT04219306

F-35101-01

Details and patient eligibility

About

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.

Full 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.

Enrollment: 5,242 patients

Sex: All

Volunteers: No Healthy Volunteers

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

Trial design

Primary purpose: Diagnostic

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Triple Blind

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5,242 participants in 2 patient groups

Machine alert

Experimental group

Description:

These cardiac suspected cardiac arrest will have had an alert generated by the machine learning model in addition to standard Emergency Medical Services response.

Treatment:

Other: Alert on dispatchers screen 'Suspect cardiac arrest'

Usual care

No Intervention group

Description:

These suspected cardiac arrests will receive standard Emergency Medical Services response.