Prehospital Evaluation of Large Vessel Occlusion in Acute Stroke

Rapid identification of large vessel occlusion (LVO) in the prehospital setting is critical to optimising care pathways and reducing time to reperfusion therapies. Paramedics and first responders rely on clinical scales, technological adjuncts and emerging algorithmic tools to detect LVO before hospital arrival. Traditional stroke‐scale assessments combine motor and cortical signs into succinct instruments that guide decisions about the most appropriate destination centre. More recently, machine learning algorithms and portable biosensors have been introduced to enhance the accuracy and speed of LVO detection. A coordinated regional approach integrates these prehospital assessments with transport protocols that balance the benefits of primary stroke centre admission against the need for direct transfer to comprehensive centres capable of delivering endovascular thrombectomy. Continuous refinement of these tools addresses the dual imperatives of sensitivity (to avoid missed LVO) and specificity (to prevent unnecessary bypass of nearer facilities), thereby improving functional outcomes and conserving system resources.

Research from Nature Portfolio

Investigations into machine learning approaches have yielded prehospital algorithms that distinguish stroke subtypes, including LVO, with high predictive value. One large multicentre study applied ensemble and gradient‐boosting methods to routine prehospital data—vital signs, neurological observations and basic demographics—to predict which patients would require surgical intervention across ischaemic and haemorrhagic stroke types. The resultant model achieved an area under the receiver operating characteristic curve of approximately 0.80, demonstrating robust discrimination between cases that needed endovascular or neurosurgical management and those that did not. Another prospective observational study developed and validated a suite of machine learning classifiers for stroke diagnosis at the scene. The best‐performing algorithm achieved near‐perfect accuracy in distinguishing acute ischaemic stroke with LVO from other acute neurological conditions, and demonstrated high sensitivity and specificity for intracranial haemorrhage and subarachnoid haemorrhage. These models rely on routinely collected observations, suggesting feasibility for integration into ambulance workflows without the need for additional hardware.

Prehospital Evaluation of Large Vessel Occlusion in Acute Stroke publication trend

The graph below shows the total number of articles in prehospital evaluation of large vessel occlusion in acute stroke across all publications each year (not limited to Nature Index journals).

Technical terms

Large vessel occlusion (LVO): Complete blockage of a major intracranial artery, often leading to severe ischaemic stroke and requiring urgent reperfusion.

Endovascular thrombectomy (EVT): A mechanical procedure to remove a clot from a blocked cerebral artery, typically performed in a specialised centre.

Machine learning: Computational methods that identify patterns within data to make predictions, here used to forecast stroke subtype or intervention needs from prehospital inputs.

Electroencephalogram (EEG): A non‐invasive recording of electrical brain activity, employed in portable form to detect signatures of cortical hypoperfusion associated with LVO.

References

1. Prehospital stroke-scale machine-learning model predicts the need for surgical intervention. Scientific Reports (2023).
2. A prehospital diagnostic algorithm for strokes using machine learning: a prospective observational study. Scientific Reports (2021).
3. Prehospital Detection of Large Vessel Occlusion Stroke With EEG. Neurology (2023).
4. Transport Strategy in Patients With Suspected Acute Large Vessel Occlusion Stroke: TRIAGE-STROKE, a Randomized Clinical Trial. Stroke (2023).
5. Emerging Detection Techniques for Large Vessel Occlusion Stroke: A Scoping Review. Frontiers in Neurology (2022).