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

NCT04219306 | Completed

• 1 other identifier: F-35101-01
• Study Type: interventional
• Target: 5,242
• Locations: 1 country
• Sites: 1

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. 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. 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. 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.

Conditions

Out-Of-Hospital Cardiac Arrest

Keywords

Machine learning; Artificial intelligence; Dispatcher assisted telephone CPR; Heart Arrest; Heart Diseases; Cardiovascular Diseases

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (3)

• Time to recognition

  During call to emergency Medical Services, up to 15 minutes from call start.

• Dispatcher assisted telephone CPR

  During call to emergency Medical Services, up to 15 minutes from call start.

• Time to T-CPR

  During call to emergency Medical Services, up to 15 minutes from call start.

Study Arms (2)

Machine alert

EXPERIMENTAL

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'

Usual care

NO INTERVENTION

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

Interventions

Alert on dispatchers screen 'Suspect cardiac arrest' OTHER

Alert on dispatchers screen 'Suspect cardiac arrest'

Machine alert

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Emergency Medical Services, Capital Region, Denmark: lead

Study Sites (1)

Emergency Medical Services Copenhagen

Ballerup Municipality, Danmark, DK-2750, Denmark

Location

Related Publications (2)

• 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.

  PMID: 30664917 BACKGROUND

• 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.

  PMID: 33404620 DERIVED

MeSH Terms

Conditions

Out-of-Hospital Cardiac Arrest; Heart Arrest; Heart Diseases; Cardiovascular Diseases

Study Officials

• Freddy Lippert, MD

  Copenhagen Emergency Medical Services

  STUDY DIRECTOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: TRIPLE
• Who Masked: PARTICIPANT, CARE PROVIDER, OUTCOMES ASSESSOR
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER GOV
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: PHD-fellow

Model Details: 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

Study Record Dates

• First Submitted: December 27, 2019
• First Posted: January 7, 2020
• Study Start: September 1, 2018
• Primary Completion: April 1, 2020
• Study Completion: April 2, 2020
• Last Updated: April 16, 2020

Record last verified: 2020-04

Data Sharing

• IPD Sharing: Will not share

Locations

DK (1)