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

NCT04219306 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 5242

Last updated 2020-04-16

No results posted yet for this study

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.

Conditions

  • Out-Of-Hospital Cardiac Arrest

Interventions

OTHER

Alert on dispatchers screen 'Suspect cardiac arrest'

Alert on dispatchers screen 'Suspect cardiac arrest'

Sponsors & Collaborators

  • Emergency Medical Services, Capital Region, Denmark

    lead OTHER_GOV

Principal Investigators

  • Freddy Lippert, MD · Copenhagen Emergency Medical Services

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
TRIPLE
Model
PARALLEL

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2018-09-01
Primary Completion
2020-04-01
Completion
2020-04-02

Countries

  • Denmark

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04219306 on ClinicalTrials.gov