Ignite Creation Date:
2024-05-06 @ 5:48 PM
Last Modification Date:
2024-10-26 @ 2:36 PM
Study NCT ID:
NCT05437237
Status:
RECRUITING
Last Update Posted:
2022-06-29
First Post:
2022-06-07
Brief Title:
Algorithm Development Through AI for the Triage of Stroke Patients in the Ambulance With EEG
Sponsor:
Academisch Medisch Centrum - Universiteit van Amsterdam AMC-UvA
Organization:
Academisch Medisch Centrum - Universiteit van Amsterdam AMC-UvA
Study Overview
Official Title:
Algorithm Development Through Artificial Intelligence for the Triage of Stroke Patients in the Ambulance With Electroencephalography
Status:
RECRUITING
Status Verified Date:
2022-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
AI-STROKE
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
Detailed Description:
RATIONALE
Large vessel occlusion LVO stroke causes around 30 of acute ischemic strokes AIS and is associated with severe deficits and poor neurological outcomes Endovascular thrombectomy EVT enormously improves the prognosis of patients with LVO stroke but its effect is highly time-dependent Because of its complexity and required resources EVT can be performed in selected hospitals only In the Netherlands approximately half of the EVT-eligible patients are initially admitted to a hospital incapable of performing EVT and - once it has been ascertained that the patient requires EVT - the patient needs to be transported a second time by ambulance to an EVT-capable hospital Interhospital transfer leads to a treatment delay of 40-115 minutes which decreases the absolute chance of a good outcome of the patient by 5-15 To solve this issue a prehospital stroke triage instrument is needed which reliably identifies LVO stroke in the ambulance so that these patients can be brought directly to an EVT-capable hospital Electroencephalography EEG may be suitable for this purpose since it shows almost instantaneous changes in response to cerebral blood flow reduction Moreover significant differences between EEGs of patients with an LVO stroke and those of suspected AIS patients with a smaller or no vessel occlusion have been found A dry electrode EEG cap enables ambulance paramedics to perform an EEG in the prehospital setting with significant reduced preparation time compared to conventional wet electrode EEG An automatic LVO-detection algorithm will be the key to reliable simple and fast interpretation of the EEG by paramedics enabling direct admission of suspected AIS patients to the right hospital
HYPOTHESIS
An EEG-based algorithm developed with artificial intelligence AI will have sufficiently high diagnostic accuracy to be used by ambulance paramedics for prehospital LVO detection
OBJECTIVE
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 AIS in the prehospital setting based on ambulant EEG data
STUDY DESIGN
AI-STROKE is an investigator-initiated multicenter diagnostic test accuracy study
STUDY POPULATION
Part A Adult patients with a suspected AIS in the prehospital setting Part B Adult patients with a suspected AIS in the in-hospital setting
INTERVENTION
A single EEG measurement with a dry electrode cap approximately 2 minutes recording duration will be performed in each patient In addition clinical and radiological data will be collected EEG data will be acquired with a CE approved portable dry electrode EEG device
MAIN END POINTS
Primary end point Based on the EEG data and using the final diagnosis based on CT angiography data established by an adjudication committee as the gold standard one or more novel AI-based EEG algorithms the AI-STROKE algorithms will be developed with maximal diagnostic accuracy ie area under the receiver operating characteristic curve AUC to identify patients with an LVO stroke of the anterior circulation in a population of patients with suspected AIS
Secondary end points
AUC sensitivity specificity positive predictive value PPV and negative predictive value NPV of the AI-STROKE algorithms based on ambulant EEG for diagnosis of LVO of the anterior circulation in suspected AIS patients in the prehospital setting
AUC sensitivity specificity PPV and NPV of existing EEG algorithms based on ambulant EEG for diagnosis of LVO stroke of the anterior circulation in suspected AIS patients in the prehospital setting
AUC sensitivity specificity PPV and NPV of existing and newly developed EEG algorithms based on ambulant EEG for detection of LVO stroke of the posterior circulation intracerebral hemorrhage transient ischemic attack and stroke mimics
Technical and logistical feasibility eg in terms of EEG channel reliability of paramedics performing ambulant EEG in patients with a suspected AIS in the prehospital setting
Large vessel occlusion LVO stroke causes around 30 of acute ischemic strokes AIS and is associated with severe deficits and poor neurological outcomes Endovascular thrombectomy EVT enormously improves the prognosis of patients with LVO stroke but its effect is highly time-dependent Because of its complexity and required resources EVT can be performed in selected hospitals only In the Netherlands approximately half of the EVT-eligible patients are initially admitted to a hospital incapable of performing EVT and - once it has been ascertained that the patient requires EVT - the patient needs to be transported a second time by ambulance to an EVT-capable hospital Interhospital transfer leads to a treatment delay of 40-115 minutes which decreases the absolute chance of a good outcome of the patient by 5-15 To solve this issue a prehospital stroke triage instrument is needed which reliably identifies LVO stroke in the ambulance so that these patients can be brought directly to an EVT-capable hospital Electroencephalography EEG may be suitable for this purpose since it shows almost instantaneous changes in response to cerebral blood flow reduction Moreover significant differences between EEGs of patients with an LVO stroke and those of suspected AIS patients with a smaller or no vessel occlusion have been found A dry electrode EEG cap enables ambulance paramedics to perform an EEG in the prehospital setting with significant reduced preparation time compared to conventional wet electrode EEG An automatic LVO-detection algorithm will be the key to reliable simple and fast interpretation of the EEG by paramedics enabling direct admission of suspected AIS patients to the right hospital
HYPOTHESIS
An EEG-based algorithm developed with artificial intelligence AI will have sufficiently high diagnostic accuracy to be used by ambulance paramedics for prehospital LVO detection
OBJECTIVE
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 AIS in the prehospital setting based on ambulant EEG data
STUDY DESIGN
AI-STROKE is an investigator-initiated multicenter diagnostic test accuracy study
STUDY POPULATION
Part A Adult patients with a suspected AIS in the prehospital setting Part B Adult patients with a suspected AIS in the in-hospital setting
INTERVENTION
A single EEG measurement with a dry electrode cap approximately 2 minutes recording duration will be performed in each patient In addition clinical and radiological data will be collected EEG data will be acquired with a CE approved portable dry electrode EEG device
MAIN END POINTS
Primary end point Based on the EEG data and using the final diagnosis based on CT angiography data established by an adjudication committee as the gold standard one or more novel AI-based EEG algorithms the AI-STROKE algorithms will be developed with maximal diagnostic accuracy ie area under the receiver operating characteristic curve AUC to identify patients with an LVO stroke of the anterior circulation in a population of patients with suspected AIS
Secondary end points
AUC sensitivity specificity positive predictive value PPV and negative predictive value NPV of the AI-STROKE algorithms based on ambulant EEG for diagnosis of LVO of the anterior circulation in suspected AIS patients in the prehospital setting
AUC sensitivity specificity PPV and NPV of existing EEG algorithms based on ambulant EEG for diagnosis of LVO stroke of the anterior circulation in suspected AIS patients in the prehospital setting
AUC sensitivity specificity PPV and NPV of existing and newly developed EEG algorithms based on ambulant EEG for detection of LVO stroke of the posterior circulation intracerebral hemorrhage transient ischemic attack and stroke mimics
Technical and logistical feasibility eg in terms of EEG channel reliability of paramedics performing ambulant EEG in patients with a suspected AIS in the prehospital setting
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None