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Prehospital technologies for early stroke detection - A review
Deepsha Agrawal1, Permesh Dhillon2, Isabel Siow3
1Department of Radiology, Oxford University Hospitals NHS Trust, Oxford, UK.
Insights
Time is Brain in stroke care. Optimizing prehospital diagnosis in large vessel occlusion strokes is crucial for saving neurons and synapses, improving patient outcomes.
Area of Science:
- Neurology
- Emergency Medicine
- Neuroscience
Background:
- Large vessel occlusion strokes cause rapid neural circuitry loss, with 'Time is Brain' highlighting the urgency.
- Every minute of delay in treatment results in significant neuronal and synaptic loss (1.9 million neurons and 13.8 billion synapses per minute).
Purpose of the Study:
- To review existing technologies for improving prehospital stroke diagnosis.
- To identify bottlenecks in the current stroke care pathway and propose optimizations.
Main Methods:
- Review of current diagnostic protocols for large vessel occlusion strokes.
- Analysis of the 'stroke chain of survival' and identification of prehospital care limitations.
- Examination of available technologies for enhancing early stroke detection.
Main Results:
- Prehospital diagnosis represents a critical bottleneck in the stroke chain of survival.
- Emergency Medical Services are the initial point of contact for over 50% of stroke patients.
- Existing technologies offer potential solutions to improve prehospital stroke assessment.
Conclusions:
- Optimizing prehospital stroke diagnosis is essential for improving outcomes in large vessel occlusion strokes.
- Technological advancements in prehospital care can significantly enhance the 'stroke chain of survival'.
Abstract:
The rate of neural circuitry loss in a typical large vessel occlusion well emphasizes that 'Time is Brain'. Every untreated minute in a large vessel ischaemic stroke results in loss of 1.9 million neurons and 13.8 billion synapses. As such, it is essential to optimize the flow-limiting steps in delivering the current standard of care. The current diagnostic model involves recognition of symptoms by patients, followed by access to Emergency Medical Services and subsequent physical examination and neuroimaging in the Emergency Department. With more than 50% of stroke patients using Emergency Medical Services as the first point of care contact, it can be deduced that the outcome of the 'stroke chain of survival' can be improved by addressing the bottleneck of prehospital stroke diagnosis. Here we present a review of the existing technologies.
