Algorithm Development Through Artificial Intelligence for the Triage of Stroke Patients in the Ambulance With Electroencephalography
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 1,192
- 试验地点
- 2
- 主要终点
- One or more novel AI-based EEG algorithms based on dry electrode EEG-data with optimal diagnostic accuracy for LVO-a
研究概览
简要总结
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.
详细描述
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
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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).
排除标准
- •Skin defect or active infection of the scalp in the area of the electrode cap placement;
- •(Suspected) COVID-19 infection.
结局指标
主要结局
One or more novel AI-based EEG algorithms based on dry electrode EEG-data with optimal diagnostic accuracy for LVO-a
时间窗: EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well
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.
次要结局
- 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)
- AUC of existing EEG 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 existing EEG 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 existing EEG 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 existing EEG 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 existing EEG 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)
- AUC of existing and newly developed EEG algorithms for detection of LVO-p, intracerebral hemorrhage, transient ischemic attack, and stroke mimics(EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well)
- Sensitivity of existing and newly developed EEG algorithms for detection of LVO-p, intracerebral hemorrhage, transient ischemic attack, and stroke mimics(EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well)
- Specificity of existing and newly developed EEG algorithms for detection of LVO-p, intracerebral hemorrhage, transient ischemic attack, and stroke mimics(EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well)
- PPV of existing and newly developed EEG algorithms for detection of LVO-p, intracerebral hemorrhage, transient ischemic attack, and stroke mimics(EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well)
- Technical feasibility of performing ambulant EEGs in the prehospital setting(Feedback on technical issues by the paramedic that performs the EEG and by the EEG-expert, will be collected directly at arrival in the emergency department (within 24 hours after the patient is included in the study))
- Logistical feasibility of performing ambulant EEGs in the prehospital setting(Feedback on logistical issues by the paramedic that performs the EEG, will be collected directly at arrival in the emergency department (within 24 hours after the patient is included in the study))
- NPV of existing and newly developed EEG algorithms for detection of LVO-p, intracerebral hemorrhage, transient ischemic attack, and stroke mimics(EEG-data for development of the algorithm will be recorded within 24 hours after onset of symptoms or last seen well)
研究者
Jonathan Coutinho
Principal Investigator
Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)