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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
|
| Usual care | No Intervention | These suspected cardiac arrests will receive standard Emergency Medical Services response. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Alert on dispatchers screen 'Suspect cardiac arrest' | Other | Alert on dispatchers screen 'Suspect cardiac arrest' |
|
| 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. |
| 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 |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Freddy Lippert, MD | Copenhagen Emergency Medical Services | Study Director |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Emergency Medical Services Copenhagen | Ballerup Municipality | Danmark | DK-2750 | Denmark |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 30664917 | Background | 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. | |
| 33404620 | Derived | 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. |
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Data will be available upon reasonable request by mail to primary investigator.
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Aug 3, 2019 | Dec 27, 2019 | Prot_SAP_000.pdf |
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| ID | Term |
|---|---|
| D058687 | Out-of-Hospital Cardiac Arrest |
| D006323 | Heart Arrest |
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
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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
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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. |