Clinical trial NCT04219306: Machine Learning Assisted Recognition of Out-of-Hospital Cardiac Arrest During Emergency Calls.
Official title: Can a Machine Learning Recognise of Out-of-Hospital Cardiac Arrest During Emergency Calls and Assist Medical Dispatchers
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Out-Of-Hospital Cardiac Arrest
Keywords: Machine learning
Artificial intelligence
Dispatcher assisted telephone CPR
Heart Arrest
Heart Diseases
Cardiovascular Diseases
Lead sponsor: Emergency Medical Services, Capital Region, Denmark
Sponsor class: OTHER_GOV
First posted: 2020-01-07
Enrollment: 5242
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention 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
Primary purpose: DIAGNOSTIC

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

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.

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.

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

Primary outcomes:
measure: Dispatcher recognition of cardiac arrest
timeFrame: During call to emergency Medical Services, up to 15 minutes from call start.
Secondary outcomes:
measure: Time to recognition
timeFrame: During call to emergency Medical Services, up to 15 minutes from call start.
measure: Dispatcher assisted telephone CPR
timeFrame: During call to emergency Medical Services, up to 15 minutes from call start.
measure: Time to T-CPR
timeFrame: During call to emergency Medical Services, up to 15 minutes from call start.
Other outcomes:
Locations:
city: Ballerup
country: Denmark
facility: Emergency Medical Services Copenhagen
state: Danmark
Study officials:
affiliation: Copenhagen Emergency Medical Services
name: Freddy Lippert, MD
role: STUDY_DIRECTOR
References:
citation: Blomberg SN, Folke F, Ersboll 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.
citation: Blomberg SN, Christensen HC, Lippert F, Ersboll AK, Torp-Petersen C, Sayre MR, Kudenchuk PJ, Folke F. Effect of Machine Learning on Dispatcher Recognition of Out-of-Hospital Cardiac Arrest During Calls to Emergency Medical Services: A Randomized Clinical Trial. JAMA Netw Open. 2021 Jan 4;4(1):e2032320. doi: 10.1001/jamanetworkopen.2020.32320.