In-depth road traffic collision data provides novel insight into road traffic collision victims most at risk of brain injury – an output from multidisciplinary collaboration with Imperial College London
Traumatic brain injuries (TBIs) are a leading cause of death and disability worldwide and nearly one in three are caused by RTCs. A partnership between TRL and Imperial College London researchers has investigated the relationship between TBI and collision parameters such as change in speed, direction and force of vehicle impact. As similar data is recorded by modern cars, they envisage that existing crash notification systems could be enhanced to alert emergency responders to collisions with a high TBI risk. This could have the potential to significantly improve emergency TBI treatment.
The research was possible thanks to the UK's flagship in-depth RTC database designed and now managed by TRL. The Road Accident In-Depth Studies (RAIDS) database is managed by TRL and contains high quality information about all aspects of road traffic collisions, including clinical data collected on behalf of the UK Government’s Department for Transport. The RAIDS project began in 2013 and relies on close collaboration with the police, hospitals and TRL’s dedicated collision investigation team. It draws on TRL’s in-house expertise in fields such as human behaviour, injury biomechanics and collision avoidance technologies. This wealth of data facilitates unique analysis to identify how collision dynamics and level of head protection relate to TBI.
By studying more than 2000 collisions on British roads documented in the RAIDS database TRL and Imperial College London researchers have determined the likelihood of different types of brain injury. The team have shown how collision parameters like change in speed during collisions relate to different types of head trauma (skull fracture, and three types of TBI: subarachnoid haemorrhage, focal brain injury, and subdural haematoma). They found that change in speed at impact were good predictors of brain injury, as were the impact direction and the presence of head protection worn by cyclists. TBI risk depended on road user type, changes in speed, and the direction of impact. The most severe injuries, including subarachnoid haemorrhage, were linked to side-on impacts and in crashes with the largest changes in speed at the time of collision.
The findings were published in Brain Communications and provide an important foundation to in future automatically identify collisions that are most likely to cause TBI. This could provide the basis for existing crash notification systems to better predict and communicate the risk of severe injury to emergency services. This could help first responders ensure that patients quickly receive the most appropriate kind of treatment.
Lead author Claire Baker, PhD researcher at TRL and Imperial’s Dyson School of Design Engineering, said: “We looked in detail at which road traffic collisions produce brain injury. We now know which types of collisions lead to the most severe head injuries – data which can be used to inform the emergency response and post-collision care pathways.”
Claire Baker adds: “Data needed to make these predictions are routinely collected in many modern cars. This means that algorithms using this data could quickly assess and relay the likely type and severity of TBI for each crash moments after it occurs, enabling emergency operators to deploy the specific type of response needed, faster.”
Fast treatment at specialist major trauma centres is key to improving the outcomes of TBI – 34 million of which are caused by RTCs globally each year. Senior author Professor David Sharp, from Imperial’s Department of Brain Sciences, said: “Road traffic collisions often produce life-threatening brain injuries, and treating patients quickly gives us the best chance of reducing death and disability. Automatically identifying collisions that are likely to lead to severe TBI will improve the efficiency of our treatment.”
In the UK and European Union, most new cars are fitted with an automatic collision notification system called eCall, which relies on inbuilt crash sensors. eCall automatically alerts the emergency services after a collision. There are other similar systems across the world, for example D-Call Net in Japan or OnStar in the USA. As of summer 2021, 90 per cent of new cars sold in the UK were fitted with eCall.
When inbuilt sensors detect a collision, they record the vehicles’ speeds immediately before, during and after impact. They also record directional movement to ascertain impact direction, which helps piece together the circumstances surrounding an RTC. This in-vehicle data may be used to optimise the decision-making process for potential RTC care pathways by providing rapid and accurate diagnoses of head injury risks.
Co-author Dr Phil Martin, Head of Biomechanics at TRL, said: “Harnessing in-vehicle data could help first responders quickly triage the severity of an RTC and prioritise the trauma care pathway. This will enable emergency services to provide the right resources, to the right place, at the right time, saving lives and reducing the risks of long-term debilitating injuries. We call on policymakers and the automotive industry to open up access to this readily available data so that its value for reducing the irreparable harms caused by RTCs may be fully realised; this can happen either in retrospective analysis of this data or better still, by transmitting it via eCall at the time of an RTC.”
This work is part of the AutoTRIAGE Project, which is jointly funded by TRL (formerly the Transport Research Laboratory) and the Engineering and Physical Sciences Research Council.
PhD candidate Claire Baker adds: “The incredible knowledge, data and support available from TRL has been instrumental to the success of the research I have undertaken during my PhD. RAIDS is an exceptional database that has enabled us to conduct novel research into the causation and risks of TBI. Many colleagues from the collision investigation to the statistics teams have been instrumental in shaping our approach. The cross-institutional multidisciplinary collaboration between TRL and Imperial College London has not only been a highlight of my education and professional career to date, but something I also believe is instrumental to make rapid and meaningful progress towards reducing injury on the roads.”
Claire Baker explains the research in brief:
1. The full paper “The relationship between road traffic collision dynamics and traumatic brain injury pathology “by Claire E. Baker, Phil Martin, Mark H. Wilson, Mazdak Ghajari, and David J. Sharp, published 12 February 2022 in Brain Communications.
2. Article adapted from a press release by Imperial College London
