Endovascular thrombectomy dramatically reduces the risk of long-term disability in acute ischemic stroke patients with large vessel occlusion (LVO). However, prehospital patient triage in the era of ET represents a major challenge. Ideally, all acute ischemic stroke patients with LVO should be directly sent to the nearest stroke center with neuro-interventional facilities, because, as with tPA, the benefit of ET is highly time-dependent. Conversely, ischemic stroke patients unlikely to have an LVO should be referred to the nearest stroke center, hence avoiding overwhelming ET-capable stroke centers and supporting adequate stroke therapy in shortest distance to patients’ homes. However, despite the development of several dedicated clinical scores, it remains very difficult to predict LVO in the prehospital setting. Indeed, using published cutoffs for triage, such clinical scores may potentially miss around 20% of LVO, which could result in a loss of opportunity for these patients who would be inappropriately sent to a center lacking ET capability. Conversely, using cutoffs reducing the falsenegative rate of LVO detection to 10% would result in sending almost every patient with acute neurological symptoms to a ET capable stroke center. Of note, no (non-imaging) biological marker, alone or in combination with clinical parameters, has been shown to be specific for LVO prediction.
By bringing imaging to the prehospital field, MSUs represent an important opportunity to improve patient triage to centres offering ET. Indeed, noncontrast brain CT scan can immediately rule out hemorrhagic stroke and can help differentiate ischemic stroke from stroke mimics, whereas intracranial CT angiography (CTA) provides prehospital information about absence or presence of LVO. Telemedicine allows neuro-interventionists to access the CT/CTA images and to determine whether ET is indicated, in which case the patient can be directly transported to the cath lab of the nearest ET-capable stroke center, with pre-notification of the endovascular team.
