Description

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 stroke patients with a smaller or no vessel occlusion.

We expect an EEG recording can be performed in less than five minutes in the prehospital setting using a dry electrode EEG cap. We initiated the ELECTRA-STROKE study to determine the diagnostic accuracy of dry electrode EEG for diagnosis of LVO stroke when performed by ambulance personnel in patients with a suspected stroke. Patient recruitment took place from October 2018 until September 2022. The first results of the study show that dry electrode EEG is a promising tool for LVO stroke detection.

An automatic LVO-detection algorithm will eventually be the key to reliable, simple and fast interpretation of EEG recordings by ambulance paramedics. The AI-STROKE study was initiated in June 2022, with the primary objective to develop one or more novel AI-based algorithms with optimal diagnostic accuracy for identification of LVO stroke in patients with a suspected stroke in the prehospital setting, based on ambulant EEG data.
StatusActive
Effective start/end date04/10/2018 → …

    Research areas

  • Electroencephalography, Acute ischemic stroke, Prehopsital triage, Diagnostics

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ID: 26880709