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

Study Overview
=================
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 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 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. 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.

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.

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

Conditions
-----------------
Out-Of-Hospital Cardiac Arrest

Intervention / Treatment
-----------------
* Other: Alert on dispatchers screen 'Suspect cardiac arrest'


Participation Criteria
=================
Eligibility 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
Sexes Eligible for Study
-----------------
All

Accepts Healthy Volunteers
-----------------
No

Study Plan
=================
How is the study designed?
-----------------

Design Details

Primary Purpose: Diagnostic
Allocation: Randomized
Intervention Model: Parallel Assignment
Interventional 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


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Machine alert<br>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'<br>* Alert on dispatchers screen 'Suspect cardiac arrest'<br>|
| No Intervention: Usual care<br>These suspected cardiac arrests will receive standard Emergency Medical Services response. | |

What is the study measuring?
-----------------
Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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. | 
| 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. | 
| 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. | 

 Terms related to the study
=================
Keywords Provided by Centre Hospitalier Valida
-----------------
Machine learning, Artificial intelligence, Dispatcher assisted telephone CPR, Heart Arrest, Heart Diseases, Cardiovascular Diseases