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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

5242 participants

Study Classification

INTERVENTIONAL

Study Start Date

2018-09-01

Study Completion Date

2020-04-02

Brief Summary

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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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Detailed Description

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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.

Conditions

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Out-Of-Hospital Cardiac Arrest

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

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

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

TRIPLE

Participants Caregivers Outcome Assessors

Study Groups

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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.

Group Type EXPERIMENTAL

Alert on dispatchers screen 'Suspect cardiac arrest'

Intervention Type OTHER

Alert on dispatchers screen 'Suspect cardiac arrest'

Usual care

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

Group Type NO_INTERVENTION

No interventions assigned to this group

Interventions

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Alert on dispatchers screen 'Suspect cardiac arrest'

Intervention Type OTHER

Eligibility Criteria

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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

Eligible Sex

ALL

Accepts Healthy Volunteers

No