Clinical Trial Details
— Status: Active, not recruiting
Administrative data
| NCT number |
NCT04648449 |
| Other study ID # |
108573 |
| Secondary ID |
|
| Status |
Active, not recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
September 1, 2020 |
| Est. completion date |
December 31, 2025 |
Study information
| Verified date |
September 2023 |
| Source |
Haukeland University Hospital |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial 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.
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.
