An interventional study of Alert on dispatchers screen 'Suspect cardiac arrest' in Out-Of-Hospital Cardiac Arrest, sponsored by Emergency Medical Services, Capital Region, Denmark. Completed at 1 site in Denmark. Per ClinicalTrials.gov, last updated 2020-04-16.
Sponsored by Emergency Medical Services, Capital Region, Denmark · Not applicable, Interventional, and Diagnostic
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
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.
965 studies on the registry are indexed under Heart Arrest; 226 are open to participants now.
This study's enrollment of 5,242 is above the median of 100 across 557 interventional studies indexed under Heart Arrest.
Browse Heart Arrest studies →Emergency Medical Services, Capital Region, Denmark is the lead sponsor of 19 studies on the registry; 1 is open to participants now.
Counted across the registry records on this site, refreshed daily.
Exclusion Criteria:
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'
These suspected cardiac arrests will receive standard Emergency Medical Services response.
Alert on dispatchers screen 'Suspect cardiac arrest'
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.
Time frame: During call to emergency Medical Services, up to 15 minutes from call start.
Time to recognition
Time from call-start until dispatcher recognition of cardiac arrest
Time frame: 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.
Time frame: 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
Time frame: During call to emergency Medical Services, up to 15 minutes from call start.
Documents are hosted by the registry — open the source record to download them.
Plan to share: No — Data will be available upon reasonable request by mail to primary investigator.
This study is completed, as verified in Apr 2020. You cannot join it, but the record below documents what was studied.
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Emergency Medical Services, Capital Region, Denmark