The Decrease in Traumatic Brain Injury Epidemics Deriving from Road Traffic Collision Following Strengthened Legislative Measures in France

Epidemiological characteristics for patients with traumatic brain injury and the nomogram model for poor prognosis: an 18-year hospital-based study

OBJECTIVE

Traumatic brain injury (TBI) is a global social, economic, and health challenge that is associated with premature death and long-term disability. In the context of rapid development of urbanization, the analysis of TBI rate and mortality trend could provide abundant diagnosis and treatment suggestions, which helps to form future reference on public health strategies.

METHODS

In this study, as one of major neurosurgical centers in China, we focused on the regime shift of TBI based on 18-year consecutive clinical data and evaluated the epidemiological features. In our current study, a total of 11,068 TBI patients were reviewed.

RESULTS

The major cause of TBI was road traffic injuries (44.%), while the main type of injury was cerebral contusion (n = 4,974 [44.94%]). Regarding to temporal changes, a decreasing trend in TBI incidence for patients under 44 years old was observed, while an increasing trend for those aged over 45 years was indicated. Incidences of RTI and assaults decreased, while ground level fall presented increasing incidences. The total number of deaths was 933 (8.43%), with a decreasing trend in overall mortality since 2011. Age, cause of injury, GCS at admission, Injury Severity Score, shock state at admission, trauma-related diagnoses and treatments were significantly associated with mortality. A predictive nomogram model for poor prognosis was developed based on patient's GOS scores at discharge.

CONCLUSIONS

The trends and characteristics of TBI patients changed with rapid development of urbanization in the past 18 years. Further larger studies are warranted to verify its clinical suggestions.

The relationship between road traffic collision dynamics and traumatic brain injury pathology

Road traffic collisions are a major cause of traumatic brain injury. However, the relationship between road traffic collision dynamics and traumatic brain injury risk for different road users is unknown. We investigated 2065 collisions from Great Britain's Road Accident In-depth Studies collision database involving 5374 subjects (2013-20). Five hundred and ninety-five subjects sustained a traumatic brain injury (20.2% of 2940 casualties), including 315 moderate-severe and 133 mild-probable injuries. Key pathologies included skull fracture (179, 31.9%), subarachnoid haemorrhage (171, 30.5%), focal brain injury (168, 29.9%) and subdural haematoma (96, 17.1%). These results were extended nationally using >1 000 000 police-reported collision casualties. Extrapolating from the in-depth data we estimate that there are ∼20 000 traumatic brain injury casualties (∼5000 moderate-severe) annually on Great Britain's roads, accounting for severity differences. Detailed collision investigation allows vehicle collision dynamics to be understood and the change in velocity (known as delta-V) to be estimated for a subset of in-depth collision data. Higher delta-V increased the risk of moderate-severe brain injury for all road users. The four key pathologies were not observed below 8 km/h delta-V for pedestrians/cyclists and 19 km/h delta-V for car occupants (higher delta-V threshold for focal injury in both groups). Traumatic brain injury risk depended on road user type, delta-V and impact direction. Accounting for delta-V, pedestrians/cyclists had a 6-times higher likelihood of moderate-severe brain injury than car occupants. Wearing a cycle helmet during a collision was protective against overall and mild-to-moderate-to-severe brain injury, particularly skull fracture and subdural haematoma. Cycle helmet protection was not due to travel or impact speed differences between helmeted and non-helmeted cyclist groups. We additionally examined the influence of the delta-V direction. Car occupants exposed to a higher lateral delta-V component had a greater prevalence of moderate-severe brain injury, particularly subarachnoid haemorrhage. Multivariate logistic regression models created using total delta-V value and whether lateral delta-V was dominant had the best prediction capabilities (area under the receiver operator curve as high as 0.95). Collision notification systems are routinely fitted in new cars. These record delta-V and automatically alert emergency services to a collision in real-time. These risk relationships could, therefore, inform how routinely fitted automatic collision notification systems alert the emergency services to collisions with a high brain injury risk. Early notification of high-risk scenarios would enable quicker activation of the highest level of emergency service response. Identifying those that require neurosurgical care and ensuring they are transported directly to a centre with neuro-specialist provisions could improve patient outcomes.