Head Injury in Children: Has a Change in Circumstances Caused an Increase in Treatment Numbers?

Incidence and severity of head injury hospitalisations in Australian children over a 10-year period

ISSUE ADDRESSED

Child head injuries can cause life-long disability and are a major cause of mortality globally. The incidence and impact of child head injuries in Australia is unknown. This study aimed to quantify the incidence, characteristics and treatment cost and to identify factors associated with the severity of hospitalisations of head injuries in Australian children.

METHODS

Linked hospitalisation and mortality data were used to retrospectively examine hospitalisation trends for head injury in children aged ≤16 years and associated factors, in Australia, from 1 July 2002 to 30 June 2012.

RESULTS

There were 164 126 hospitalisations of children for head injury during the 10-year period, commonly male (65.5%), or aged ≤5 years (48.3%). The incidence among children aged <1 year and 1-5 years significantly increased by 1.7% (95% CI 0.9-2.6; P < 0.0001) and 1.5% (95% CI 1.1-1.9; P < 0.0001) annually during the study period, respectively. The most common injury mechanisms across all age groups were falls (45.2%) and road trauma (16.0%). Head injury hospitalisations cost $468.9 million, with the higher costs found for children aged 11-16 years, and for the most severe injuries.

CONCLUSION

Head injury hospitalisations cost the Australian health system close to half a billion dollars over a 10-year period, with the most serious injuries resulting in lifelong health implications. SO WHAT?: Targeted health promotion strategies such as the promotion of helmet wearing during scooter use, the introduction of cycleways, and impact absorbing surfaces on playgrounds, need to be implemented to reduce the occurrence of head injuries in children.

MRI in mild pediatric traumatic brain injury: diagnostic overkill or useful tool?

PURPOSE

Magnetic resonance imaging (MRI) is a sensitive imaging tool which lacks the burden of ionizing radiation. It is not established as primary diagnostic tool in traumatic brain injury (TBI). The purpose of this study was to evaluate the usefulness of MRI as initial imaging modality in the emergency management of mild pediatric TBI.

METHODS

Children (0-18 years, sub-divided in four age-groups) with mild TBI who received MRI in the emergency department were identified. Clinical characteristics and trauma mechanisms were evaluated retrospectively. Univariate and multivariate logistic regression analyses were used to identify clinical factors that might be indicative for trauma sequelae on MRI scans.

RESULTS

An institutional case series of 569 patients (322 male/247 female; age < 18years; (GCS ≥ 13), who received MRI for mild TBI, was analyzed. Multi-sequence imaging (including T2, T2*, FLAIR, and diffusion-weighted sequences) was feasible without sedation in 96.8% of cases (sedation, 1.8%; general anesthesia, 1.4%). MRI revealed trauma-associated findings in 13% of all cases; incidental findings were detected in 4.7%. In our cohort, GCS deterioration, scalp hematoma, clinical signs of skull base fractures, and horseback riding accidents were related to structural trauma sequelae on MRI.

CONCLUSIONS

MRI is a practical primary imaging tool for evaluating children with mild TBI in the emergency department. The presented analyses demonstrated that in our institution, MRI imaging is performed frequently in the emergency department. It resulted mostly in normal findings. This may reflect uneasiness of when to perform imaging in mild TBI and appears retrospectively as an "overdo." There are clinical factors that are more likely associated with MRI-positive findings. Their reliability has to be evaluated in prospective studies in order to formulate further decision rules of when to perform MRI imaging or not.

Patterns of Intracranial Hemorrhage in Pediatric Patients with Facial Fractures

Intracranial hemorrhage (ICH) is a potentially fatal injury accompanying fractures of the cranium and facial skeleton. When occurring at a young age, ICH can lead to developmental delay, cerebral palsy, epilepsy, and death. It is therefore important for clinicians to recognize the presence of ICH early, and understand the factors that affect its prognosis. In this study, we aim to identify diagnostic and prognostic signs for ICH in pediatric facial fracture patients by examining aspects of patient presentation, concomitant injuries, and fracture patterns. Data were collected for all radiologically diagnosed facial fractures between January 2000 and December 2012 at a level I trauma center in Newark, NJ. This was then further refined to include only patients 18 years of age or younger who had a documented ICH. Patient age, Glasgow coma scale (GCS) on presentation, fracture location, type of hemorrhage, and certain aspects of management were collected from these records. Data were then analyzed by either Pearson chi-square test or a t-test to determine significant relationships. A total of 285 pediatric patients were found to have sustained a facial fracture during this time period, 67 of which had concomitant ICH; 46 of these patients were male and 21 were female, with average ages of 14.26 and 9.52 (p < 0.01), respectively. Causes of injury included motor vehicle accidents, pedestrians struck, assault, falls, gunshot injuries, and sports-related injuries. All patients who suffered injuries as a result of violent crimes (assault and gunshot injuries) were male. Although nearly all fracture patterns were significantly associated with the presence of ICH, mandibular fractures showed a significant negative association with the presence of ICH. In addition, patients who received surgical intervention were significantly younger than those who did not (7.7 vs. 13.7, p < 0.05). The GCS was significantly lower in patients who underwent ICP (intracranial pressure) monitoring or EVD (external ventricular drain) placement, suffered intraventricular hemorrhage, experienced worsening of hemorrhage on repeat imaging, and suffered fatal injuries. Our data also showed a significant association between the need for intubation in the emergency department and fatality. Because the consequence of ICH can be life threatening, proper diagnosis and management are imperative. The purpose of this study is to describe patterns associated with ICH in pediatric facial fracture patients to promote early recognition of the injury and understanding of poor prognostic signs.

Predictive modeling in pediatric traumatic brain injury using machine learning

BACKGROUND

Pediatric traumatic brain injury (TBI) constitutes a significant burden and diagnostic challenge in the emergency department (ED). While large North American research networks have derived clinical prediction rules for the head injured child, these may not be generalizable to practices in countries with traditionally low rates of computed tomography (CT). We aim to study predictors for moderate to severe TBI in our ED population aged < 16 years.

METHODS

This was a retrospective case-control study based on data from a prospective surveillance head injury database. Cases were included if patients presented from 2006 to 2014, with moderate to severe TBI. Controls were age-matched head injured children from the registry, obtained in a 4 control: 1 case ratio. These children remained well on diagnosis and follow up. Demographics, history, and physical examination findings were analyzed and patients followed up for the clinical course and outcome measures of death and neurosurgical intervention. To predict moderate to severe TBI, we built a machine learning (ML) model and a multivariable logistic regression model and compared their performances by means of Receiver Operating Characteristic (ROC) analysis.

RESULTS

There were 39 cases and 156 age-matched controls. The following 4 predictors remained statistically significant after multivariable analysis: Involvement in road traffic accident, a history of loss of consciousness, vomiting and signs of base of skull fracture. The logistic regression model was created with these 4 variables while the ML model was built with 3 extra variables, namely the presence of seizure, confusion and clinical signs of skull fracture. At the optimal cutoff scores, the ML method improved upon the logistic regression method with respect to the area under the ROC curve (0.98 vs 0.93), sensitivity (94.9% vs 82.1%), specificity (97.4% vs 92.3%), PPV (90.2% vs 72.7%), and NPV (98.7% vs 95.4%).

CONCLUSIONS

In this study, we demonstrated the feasibility of using machine learning as a tool to predict moderate to severe TBI. If validated on a large scale, the ML method has the potential not only to guide discretionary use of CT, but also a more careful selection of head injured children who warrant closer monitoring in the hospital.