Marie-Carmelle Elie-Turenne

TBI surveillance using the common data elements for traumatic brain injury: a population study

BackgroundTo characterize the patterns of presentation of adults with head injury to the Emergency Department.MethodsThis is a cohort study that sought to collect injury and outcome variables with the goal of characterizing the very early natural history of traumatic brain injury in adults. This IRB-approved project was conducted in collaboration with our Institution’s Center for Translational Science Institute. Data were entered in REDCap, a secure database. Statistical analyses were performed using JMP 10.0 pro for Windows.ResultsThe cohort consisted of 2,394 adults, with 40% being women and 79% Caucasian. The most common mechanism was fall (47%) followed by motor vehicle collision (MVC) (36%). Patients sustaining an MVC were significantly younger than those whose head injury was secondary to a fall (P < 0.0001). Ninety-one percent had CT imaging; hemorrhage was significantly more likely with worse severity as measured by the Glasgow Coma Score (chi-square, P < 0.0001). Forty-four percent were admitted to the hospital, with half requiring ICU admission. In-hospital death was observed in 5.4%, while neurosurgical intervention was required in 8%. For all outcomes, worse TBI severity per GCS was significantly associated with worse outcomes (logistic regression, P < 0.0001, adjusted for age).ConclusionThese cohort data highlight the burden of TBI in the Emergency Department and provide important demographic trends for further research.

Impact of Helmet Use in Traumatic Brain Injuries Associated with Recreational Vehicles

Objective. To study the impact of helmet use on outcomes after recreational vehicle accidents. Methods. This is an observational cohort of adult and pediatric patients who sustained a TBI while riding a recreational vehicle. Recreational vehicles included bicycles, motorcycles, and all-terrain vehicles (ATVs), as well as a category for other vehicles such as skateboards and scooters. Results. Lack of helmet use was significantly associated with having a more severe traumatic brain injury and being admitted to the hospital. Similarly, 25% of those who did wearing a helmet were admitted to the ICU versus 36% of those who did not (P = 0.0489). The hospital length of stay was significantly greater for patients who did not use helmets. Conclusion. Lack of helmet use is significantly correlated with abnormal neuroimaging and admission to the hospital and ICU; these data support a call for action to implement more widespread injury prevention and helmet safety education and advocacy.

Towards Prevention of Acute Lung Injury: Frequency and Outcomes of Emergency Department Patients At-Risk: A Multicenter Cohort Study

BackgroundFew emergency department (ED) evaluations on acute lung injury (ALI) have been carried out; hence, we sought to describe a cohort of hospitalized ED patients at risk for ALI development.MethodsPatients presenting to the ED with at least one predisposing condition to ALI were included in this study, a subgroup analysis of a multicenter observational cohort study (USCIITG-LIPS 1). Patients who met ALI criteria within 6 h of initial ED assessment, received end-of-life care, or were readmitted during the study period were excluded. Primary outcome was frequency of ALI development; secondary outcomes were ICU and hospital mortality.ResultsTwenty-two hospitals enrolled 4,361 patients who were followed from the ED to hospital discharge. ALI developed in 303 (7.0 %) patients at a median onset of 2 days (IQR 2–5). Of the predisposing conditions, frequency of ALI development was highest in patients who had aortic surgery (43 %) and lowest in patients with pancreatitis (2.8 %). Compared to patients who did not develop ALI, those who did had higher ICU (24 % vs. 3.0 %, p < 0.001) and hospital (28 % vs. 4.6 %, p < 0.001) mortality, and longer hospital length of stay (16 vs. 5 days, p < 0.001). Among the 22 study sites, frequency of ALI development varied from less than 1 % to more than 12 % after adjustment for APACHE II.ConclusionsSeven percent of hospitalized ED patients with at least one predisposing condition developed ALI. The frequency of ALI development varied significantly according to predisposing conditions and across institutions. Further research is warranted to determine the factors contributing to ALI development.

Lung injury prediction score for the emergency department: first step towards prevention in patients at risk

BackgroundEarly identification of patients at risk of developing acute lung injury (ALI) is critical for potential preventive strategies. We aimed to derive and validate an acute lung injury prediction score (EDLIPS) in a multicenter sample of emergency department (ED) patients.MethodsWe performed a subgroup analysis of 4,361 ED patients enrolled in the previously reported multicenter observational study. ED risk factors and conditions associated with subsequent ALI development were identified and included in the EDLIPS model. Scores were derived and validated using logistic regression analyses. The model was assessed with the area under the receiver-operating curve (AUC) and compared to the original LIPS model (derived from a population of elective high-risk surgical and ED patients) and the Acute Physiology and Chronic Health Evaluation (APACHE II) score.ResultsThe incidence of ALI was 7.0% (303/4361). EDLIPS discriminated patients who developed ALI from those who did not with an AUC of 0.78 (95% CI 0.75, 0.82), better than the APACHE II AUC 0.70 (p ≤ 0.001) and similar to the original LIPS score AUC 0.80 (p = 0.07). At an EDLIPS cutoff of 5 (range −0.5, 15) positive and negative likelihood ratios (95% CI) for ALI development were 2.74 (2.43, 3.07) and 0.39 (0.30, 0.49), respectively, with a sensitivity 0.72(0.64, 0.78), specificity 0.74 (0.72, 0.76), and positive and negative predictive value of 0.18 (0.15, 0.21) and 0.97 (0.96, 0.98).ConclusionEDLIPS may help identify patients at risk for ALI development early in the course of their ED presentation. This novel model may detect at-risk patients for treatment optimization and identify potential patients for ALI prevention trials.