Improving the prehospital acute care process for road traffic injuries
Every year 1.3 million people are killed in road traffic accidents around the world, and 20−50 million are injured. MedTech West is developing methodologies for improving the rescue process for traffic accidents, by gathering and interpreting valuable crash information from vehicle sensors and making diagnostic tools for detection of internal bleedings.
We take a holistic view of traffic accidents and trauma care and use novel methodologies to address the clinical problems, such as pattern recognition algorithms for improving field triage protocols (used to assess the status of injured patients, to decide where they should be transported and how urgent treatment is) and microwave technology for detection of intracranial bleedings, which makes this project unique. In the future, the emergency center will receive more precise and accurate information about the accidents for planning emergency dispatch, and the hospital will receive more specific information about the status of a patient, to ensure the each patient gets the best possible treatment as soon as possible after the accident.
Traffic accidents is the most common cause of death with exception for diseases . The world health organization estimates that the number of traffic deaths will reach 2.4 million in 2030 . If traffic safety and emergency medical care of accident victims are not markedly improved road traffic injury will put an even heavier burden on our society in the near future. It is of utmost importance that severely injured victims receive appropriate medical care as soon as possible after an accident. Swift treatment saves lives [3, 4]. Effective emergency response requires a well functioning prehospital workflow and information exchange, so that the stabilized patient is transported to the hospital without unnecessary delay and the receiving trauma team has a good understanding of the accident, the vital status of the patient, and what possible occult injuries that can be expected. The transportation time to the hospital can be decreased with the upcoming public service eCall in the European Union. Vehicles involved in an accident will automatically transmit location coordinates and establish a voice connection to the emergency call center . eCall is expected to save up to 2500 lives, and increase the prospects for good recovery for tens of thousands severely injured, every year . Although eCall holds much promise, no indication of the severity of the accident is included in the transmitted set of minimum data currently recommended . Having this information available would help to plan the emergency service operations so that all necessary resources are dispatched early on, e.g. for deciding when to send out an ambulance helicopter and whether extrication equipment is needed. The probability that an occupant is severely injured can be estimated from deceleration and other parameters measured by on-board sensors [6-9]. Prediction of injury severity would in particular be valuable when there is no voice response from the crashed vehicle . Furthermore, it is not uncommon that occupants do not realize the extent of their injuries .
To make an early decision on what treatment is needed it is advantageous to collect as much information about the status of the patient as possible already in the ambulance. Patients with severe injuries should be transported to a specialized trauma center, which increases their chances of survival . The accurate identification of those patients relies on the field triage protocol, which is a standardized procedure to assess the clinical priority level of patients . Development of decision support algorithms for field triage has potential to reduce over- and undertriage, i.e. giving a too high and too low priority level for a patient, respectively. Patients with occult injuries are at higher risk of being undertriaged , and patients that look and feel fine but still have life-threatening occult injuries have become more common due to the use of effective restraint systems with airbags in modern vehicle fleets . Furthermore, there is a high rate of overtriage of trauma patients today , which consumes scarce hospital resources unnecessary. The decision support algorithms could use predictions of accident severity in combination with current field triage criteria, such as assessment of consciousness level and measuring blood pressure and respiratory rate , as input. New diagnostic tools for early detection of occult injuries could also be integrated into an improved field triage protocol. We are investigating the possibility of using microwave technology for detection of internal bleedings in the head and the chest, and this equipment can be fitted into air/road ambulances.
Head injuries are a common cause of death in road accidents, especially for children in motor vehicle crashes , in motorcycle crashes , and in accidents involving vulnerable road users such as pedestrians . The chances of surviving life-threatening head injuries are much better when treatment is received without significant delay after trauma [3, 4]. Severe head injuries can often be asymptomatic at first, and these patients are therefore at increased risk of being undertriaged and treated too late [12, 19]. Microwave technology has shown promising results for detection of intracranial bleedings in stroke patients . The method may also be valuable for detecting typical intracranial hemorrhages, such as subdural hematomas, in road accident victims. If bleedings could be detected already in the ambulance the time to treatment may be shortened and the clinical outcome improved.
MedTech West Partner
Dr. Stefan Candefjord, Department of Signal Processing and Biomedical Engineering, S2, Chalmers University of Technology.
Technical research partners
Prof. Mikael Persson, Department of Signal Processing and Biomedical Engineering, S2, Chalmers University of Technology
Dr. Andreas Fhager, Department of Signal Processing and Biomedical Engineering, S2, Chalmers University of Technology
Dr. Ants Silberberg, Department of Signal Processing and Biomedical Engineering, S2, Chalmers University of Technology
Prof. Tomas McKelvey, Department of Signal Processing and Biomedical Engineering, S2, Chalmers University of Technology
Adj. Prof. Bengt Arne Sjöqvist, Department of Signal Processing and Biomedical Engineering, S2, Chalmers University of Technology
Dr. Anna Nilsson - Ehle, SAFER
Lotta Jakobsson, SAFER
Prof. Mikael Elam, Institute of Neuroscience and Physiology, Clinical Neurophysiology, Sahlgrenska University Hospital
Dr. Per Örtenwall, Swedish Armed Forces, Centre for Defence Medicine, Sahlgrenska University of Technology
Patrik Dahlqvist, Medfield Diagnostics
Stefan Kilborg, Medfield Diagnostics
For more information on this project, contact Henrik Mindedal at firstname.lastname@example.org
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