Algorithm Development Through AI for the Triage of Stroke Patients in the Ambulance With EEG

NCT05437237 | Recruiting DMC

• 1 other identifier: NL75429.018.20
• Study Type: interventional
• Target: 1,192
• Locations: 1 country
• Sites: 1

Brief Summary

Endovascular thrombectomy (EVT) enormously improves the prognosis of patients with large vessel occlusion (LVO) stroke, but its effect is highly time-dependent. Direct presentation of patients with an LVO stroke to an EVT-capable hospital reduces onset-to-treatment time by 40-115 minutes and thereby improves clinical outcome. Electroencephalography (EEG) may be a suitable prehospital stroke triage instrument for identifying LVO stroke, as differences have been found between EEG recordings of patients with an LVO stroke and those of suspected acute ischemic stroke patients with a smaller or no vessel occlusion. The investigators expect EEG can be performed in less than five minutes in the prehospital setting using a dry electrode EEG cap. An automatic LVO-detection algorithm will be the key to reliable, simple and fast interpretation of EEG recordings by ambulance paramedics. The primary objective of this study is to develop one or more novel AI-based algorithms (the AI-STROKE algorithms) with optimal diagnostic accuracy for identification of LVO stroke in patients with a suspected acute ischemic stroke in the prehospital setting, based on ambulant EEG data.

Conditions

Ischemic Stroke

Outcome Measures

Primary Outcomes (1)

• One or more novel AI-based EEG algorithms based on dry electrode EEG-data with optimal diagnostic accuracy for LVO-a

  One or more novel artificial intelligence (AI) based electroencephalography (EEG) algorithms (the AI-STROKE algorithms) with maximal diagnostic accuracy to identify patients with an large vessel occlusion of the anterior circulation (LVO-a) in a population of patients with suspected acute ischemic stroke. For each patient a single dry electrode electroencephalography (EEG) will be performed and the presence or absence of an LVO-a will be assessed based on CT angiography data acquired at the emergency department.

  EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well

Secondary Outcomes (17)

• AUC of the AI-STROKE algorithms for diagnosis of LVO-a

  EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well

• Sensitivity of the AI-STROKE algorithms for diagnosis of LVO-a

  EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well

• Specificity of the AI-STROKE algorithms for diagnosis of LVO-a

  EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well

• PPV of the AI-STROKE algorithms for diagnosis of LVO-a

  EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well

• NPV of the AI-STROKE algorithms for diagnosis of LVO-a

  EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well

Study Arms (1)

Dry electrode cap EEG

EXPERIMENTAL

All patients that are included in the study will undergo a dry electrode electroencephalography (EEG).

Diagnostic Test: Dry electrode EEG

Interventions

Dry electrode EEG DIAGNOSTIC_TEST

A single dry electrode electroencephalography (EEG) will be performed in each patient that is included in this study. For this purpose the Waveguard touch dry electrode EEG cap and compatible eego mini amplifier (ANT Neuro B.V., Hengelo, Netherlands) are used to record and amplify the EEG signal, respectively. Both products are CE marked as medical devices in the European Union and will be used within the intended use as described in the user manuals.

Dry electrode cap EEG

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Suspected AIS, as assessed by the attending ambulance paramedic, or a known LVO stroke;
• Onset of symptoms or last seen well \< 24 hours before EEG acquisition;
• Age of 18 years or older;
• Written informed consent by patient or legal representative (deferred).

You may not qualify if:

• Skin defect or active infection of the scalp in the area of the electrode cap placement;
• (Suspected) COVID-19 infection.

Sponsors & Collaborators

• Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA): lead

Study Sites (1)

Amsterdam University Medical Centers, location AMC

Amsterdam, North Holland, 1105AZ, Netherlands

RECRUITING

MeSH Terms

Conditions

Ischemic Stroke

Condition Hierarchy (Ancestors)

Stroke; Cerebrovascular Disorders; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Vascular Diseases; Cardiovascular Diseases

Study Officials

• Jonathan M Coutinho, MD, PhD

  Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Maritta N van Stigt, MSc

CONTACT

0031 20 566 8417; m.n.vanstigt@amterdamumc.nl

Jonathan M Coutinho, MD, PhD

CONTACT

0031 20 566 2004; j.coutinho@amsterdamumc.nl

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: June 7, 2022
• First Posted: June 29, 2022
• Study Start: June 19, 2022
• Primary Completion (Estimated): June 1, 2026
• Study Completion (Estimated): June 1, 2026
• Last Updated: June 29, 2022

Record last verified: 2022-06

Data Sharing

• IPD Sharing: Will not share

Locations

NL (1)