Mortality In Pediatric Traumatic Brain Injury Research Articles

The epidemiology of pediatric traumatic brain injury presenting at a referral center in Moshi, Tanzania.

BackgroundOver 95% of childhood injury deaths occur in low- and middle-income countries (LMICs). Patients with severe traumatic brain injury (TBI) have twice the likelihood of dying in LMICs than in high-income countries (HICs). In Africa, TBI estimates are projected to increase to upwards of 14 million new cases in 2050; however, these estimates are based on sparse data, which underscores the need for robust injury surveillance systems. We aim to describe the clinical factors associated with morbidity and mortality in pediatric TBI at the Kilimanjaro Christian Medical Centre (KCMC) in Moshi, Tanzania to guide future prevention efforts.MethodsWe conducted a secondary analysis of a TBI registry of all pediatric (0–18 years of age) TBI patients presenting to the KCMC emergency department (ED) between May 2013 and April 2014. The variables included demographics, acute treatment and diagnostics, Glasgow Coma Scores (GCSs, severe 3–8, moderate 9–13, and mild 14–15), morbidity at discharge as measured by the Glasgow Outcome Scale (GOS, worse functional status 1–3, better functional status 4–6), and mortality status at discharge. The analysis included descriptive statistics, bivariable analysis and multivariable logistic regression to report the predictors of mortality and morbidity. The variables used in the multivariable logistic regression were selected according to their clinical validity in predicting outcomes.ResultsOf the total 419 pediatric TBI patients, 286 (69.3%) were male with an average age of 10.12 years (SD = 5.7). Road traffic injury (RTI) accounted for most TBIs (269, 64.4%), followed by falls (82, 19.62%). Of the 23 patients (5.58%) who had alcohol-involved injuries, most were male (3.6:1). Severe TBI occurred in 54 (13.0%) patients. In total, 90 (24.9%) patients underwent TBI surgery. Of the 21 (5.8%) patients who died, 11 (55.0%) had severe TBI, 6 (30.0%) had moderate TBI (GCS 9–13) and 3 (15.0%) presented with mild TBI (GCS>13). The variables most strongly associated with worse functional status included having severe TBI (OR = 9.45) and waiting on the surgery floor before being moved to the intensive care unit (ICU) (OR = 14.37).ConclusionsMost pediatric TBI patients were males who suffered RTIs or falls. Even among children under 18 years of age, alcohol was consumed by at least 5% of patients who suffered injuries, and more commonly among boys. Patients becoming unstable and having to be transferred from the surgery floor to the ICU could reflect poor risk identification in the ED or progression of injury severity. The next steps include designing interventions to reduce RTI, mitigate irresponsible alcohol use, and improve risk identification and stratification in the ED.

The application of adult traumatic brain injury models in a pediatric cohort.

OBJECTIVE There is increasing interest in the use of predictive models of outcome in adult head injury. Two international models have been identified to be reliable modalities for predicting outcome: the Corticosteroid Randomisation After Significant Head Injury (CRASH) model, and the International Mission on Prognosis and Analysis of randomized Controlled Trials in TBI (IMPACT) model. However, these models are designed only to identify outcomes in adult populations. METHODS A retrospective analysis was performed on pediatric patients with severe traumatic brain injury (TBI) admitted to the pediatric intensive care unit (PICU) of Addenbrooke's Hospital between January 2009 and December 2013. The individual risk of 14-day mortality was calculated using the CRASH-Basic and -CT models, and the risk of 6-month mortality calculated using the IMPACT-Core and -Extended (including CT findings) models. Model accuracy was determined by standardized mortality ratio (SMtR; observed/expected deaths), discrimination was evaluated as the area under the receiver operating curve (AUROC), and calibration assessed using the Hosmer-Lemeshow χ2 test. RESULTS Ninety-four patients with an average age of 7.3 years were admitted to the PICU with a TBI. The mortality rate was 12.7% at 14 days and at 6 months. For the CRASH-Basic model, the SMtR was 1.42 and both calibration (χ2 = 6.1, p = 0.64) and discrimination (AUROC = 0.92) were good. For the IMPACT-Core model, the SMtR was 1.03 and the model was also well calibrated (χ2 = 8.99, p = 0.34) and had good discrimination (AUROC = 0.85). Poor outcome was observed in 17% of the cohort and identified with the CRASH-Basic and IMPACT-Core models to varying degrees: standardized morbidity ratio = 0.89 vs 0.67, respectively; calibration = 6.5 (χ2) and 0.59 (p value) versus 8.52 (χ2) and 0.38 (p value), respectively; and discrimination (AUROC) = 0.92 versus 0.83, respectively. CONCLUSIONS Adult head injury models may be applied with sufficient accuracy to identify predictors of morbidity and mortality in pediatric TBI.

Quelle pression de perfusion cérébrale après traumatisme crânien chez l’enfant ?

The management of cerebral perfusion pressure (CPP) is the one of the main preoccupation for the care of paediatric traumatic brain injury (TBI). The physiology of cerebral autoregulation, CO2 vasoreactivity, cerebral metabolism changes with age as well as the brain compliance. Low CPP leads to high morbidity and mortality in pediatric TBI. The recent guidelines for the management of CPP for the paediatric TBI indicate a CPP threshold 40–50mmHg (infants for the lower and adolescent for the upper). But we must consider the importance of age-related differences in the arterial pressure and CPP. The best CPP is the one that allows to avoid cerebral ischaemia and oedema. In this way, the adaptation of optimal CPP must be individual. To assess this objective, interesting tools are available. Transcranial Doppler can be used to determine the best level of CPP. Other indicators can predict the impairment of autoregulation like pressure reactivity index (PRx) taking into consideration the respective changes in ICP and CPP. Measurement of brain tissue oxygen partial pressure is an other tool that can be used to determine the optimal CPP.