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Artificial Intelligence (AI) Support in Stroke Calls (AISIS)

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT04648449
Recruitment Status : Active, not recruiting
First Posted : December 1, 2020
Last Update Posted : December 16, 2020
Sponsor:
Collaborators:
Helse Vest
Western Norway University of Applied Sciences
Oslo University Hospital
The Norwegian Heart and Lung Patient Organization
Helsetjenestens driftsorganisasjon for nødnett HF (HDO)
The Norwegian Stroke Register
Information provided by (Responsible Party):
Haukeland University Hospital

Study Description
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Brief Summary:
More than 12.000 patients suffer acute stroke in Norway every year, but less than half of them reach hospital within the current treatment window for thrombolysis. Stroke is the third-highest cause of death and the number one cause of severe disability requiring long time care at institutions. Consequently this has a high impact on society, patients and relatives, in addition to high costs related to care estimated to approximately 10 billion NOK per year. Although there are few studies on emergency medical communication centres (EMCC) in Norway, some have shown that the performance of the emergency medical communication centres can be improved. This project will seek to amend EMCC´s handling of acute stroke inquiries using artificial intelligence (AI), thus contributing to getting the patient to hospital in time for optimal treatments.

Condition or disease Intervention/treatment
Stroke, Acute Apoplexy; Brain Emergencies Communication, Multidisciplinary Other: Artificial intelligence on emergency calls

Detailed Description:

In this project, the investigators will collect data from all stroke patients discharged from Helse Bergen in 2019 (approx. 1000 patients) via the Norwegian Stroke Registry (NSR). For these patients, structured hospital data from Helse Bergen will be retrieved, and based on these and the spoken content of their emergency call regarding the stroke, the investigators will use machine learning to calculate the stroke risk. The connection of historical hospital data to the spoken words in the emergency call, amplifies the analysis of emergency calls in a novel way, in comparison to sound analysis alone.

After retrieving and connecting stroke patient data, the investigators train the deep network using data from 2019. Accordingly, testing will be performed based on patients from the first half of 2020. A separation of the data into training, test, and validation assures that our trained network does not over fit on the training data and can reproduce similar results on previously unseen patients. Finally, the investigators will compare the performance of the AI with the current system through statistical analyses on data from a period of approximately one year of live usage of the AI in AMK Bergen. This will enable us to evaluate to what degree the system is able to improve within the decision process of the EMCC operators in terms of sensitivity and specificity.

Summarized, the primary objective is to build a robust, working prototype of an AI system capable of real-time identification of acute stroke for improved assessment in emergency medical calls.

Our secondary objectives are:

  • To implement an AI system capable of providing fast prediction of whether a patient is suffering from acute stroke or not based on audio from emergency call and available data sources within the hospital records
  • To prove that AI systems can be used to assist and improve the triage decision procedure of the EMCC operator.

The anticipated result is to deliver fast (i.e. seconds) prediction scores to assist the EMCC operator in recognizing acute stroke patients, which provides an improved sensitivity and specificity compared to manual assessment only.

Study Design
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Study Type : Observational
Estimated Enrollment : 1000 participants
Observational Model: Case-Only
Time Perspective: Prospective
Official Title: Artificial Intelligence (AI) Support in Stroke Calls - "The AISIS-study" -Can Artificial Intelligence Improve the Precision in Identifying Acute Stroke in Emergency Medical Calls?
Actual Study Start Date : September 1, 2020
Estimated Primary Completion Date : December 1, 2022
Estimated Study Completion Date : August 31, 2024
Groups and Cohorts
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Intervention Details:
  • Other: Artificial intelligence on emergency calls
    AI listens to all calls to Bergen EMCC, detecting calls regarding possible stroke.

Outcome Measures
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Primary Outcome Measures :
  1. Stroke recognition in medical emergency calls [ Time Frame: Sept. 22 - Sept. 23 ]
    Survey AI's ability to recognize stroke, compared to the current system


Eligibility Criteria
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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Probability Sample
Study Population
All calls to 113 in Bergen will be subject to the AI. A warning will be given the EMCC-operator if risk of stroke, when combining contents in the call and historical data in the hospital record, exceeds a certain limit.
Criteria

Inclusion Criteria:

  • All callers to medical emergency number 113 in Bergen

Exclusion Criteria:

  • Age <18
Contacts and Locations
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Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04648449


Locations
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Norway
Haukeland Universitetssykehus, Kirurgisk serviceklinikk, Nasjonalt kompetansesenter for helsetjenestens kommunikasjonsberedskap
Bergen, Norway, 5021
Sponsors and Collaborators
Haukeland University Hospital
Helse Vest
Western Norway University of Applied Sciences
Oslo University Hospital
The Norwegian Heart and Lung Patient Organization
Helsetjenestens driftsorganisasjon for nødnett HF (HDO)
The Norwegian Stroke Register
Investigators
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Study Director: Guttorm Brattebo, Professor II Haukeland University Hospital
More Information
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Responsible Party: Haukeland University Hospital
ClinicalTrials.gov Identifier: NCT04648449    
Other Study ID Numbers: 108573
First Posted: December 1, 2020    Key Record Dates
Last Update Posted: December 16, 2020
Last Verified: November 2020
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No
Plan Description: Not relevant at this stage.

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Haukeland University Hospital:
EMCC
Stroke
Apoplexy
Emergency Medical Service Communication Systems
 Dispatch
Artificial intelligence
Machine learning
Additional relevant MeSH terms:
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Stroke
Emergencies
Cerebrovascular Disorders
Brain Diseases
Central Nervous System Diseases
 Nervous System Diseases
Vascular Diseases
Cardiovascular Diseases
Disease Attributes
Pathologic Processes