Trauma Data Bank National Sample Research Articles

Divergence in the epidemiological estimates of traumatic brain injury in the United States: comparison of two national databases.

Traumatic brain injury (TBI) is a leading cause of morbidity and mortality in the US, but the true incidence of TBI is unknown. The National Trauma Data Bank National Sample Program (NTDB NSP) was queried for 2007 and 2013, and population-based weighted estimates of TBI-related emergency department (ED) visits, hospitalizations, and deaths were calculated. These data were compared to the 2017 Centers for Disease Control and Prevention (CDC) report on TBI, which used the Healthcare Cost and Utilization Project's National ("Nationwide" before 2012) Inpatient Sample and National Emergency Department Sample. In the NTDB NSP the incidence of TBI-related ED visits was 59/100,000 in 2007 and 62/100,000 in 2013. However, in the CDC report there were 534/100,000 in 2007 and 787/100,000 in 2013. The CDC estimate for ED visits was 805% higher in 2007 and 1169% higher in 2013. In the NTDB NSP, the incidence of TBI-related deaths was 5/100,000 in 2007 and 4/100,000 in 2013. In the CDC report, the incidence was 18/100,000 in both years. The CDC estimate for deaths was 260% higher in 2007 and 325% higher in 2013. The databases disagreed widely in their weighted estimates of TBI incidence: CDC estimates were consistently higher than NTDB NSP estimates, by an average of 448%. Although such a discrepancy may be intuitive, this is the first study to quantify the magnitude of disagreement between these databases. Given that research, funding, and policy decisions are made based on these estimates, there is a need for a more accurate estimate of the true national incidence of TBI.

Hospitalized Traumatic Brain Injury: Low Trauma Center Utilization and High Interfacility Transfers among Older Adults

Objective: Guidelines suggest that Traumatic Brain Injury (TBI) related hospitalizations are best treated at Level I or II trauma centers because of continuous neurosurgical care in these settings. This population-based study examines TBI hospitalization treatment paths by age groups. Methods: Trauma center utilization and transfers by age groups were captured by examining the total number of TBI hospitalizations from National Inpatient Sample (NIS) and the number of TBI hospitalizations and transfers in the Trauma Data Bank National Sample Population (NTDB-NSP). TBI cases were defined using diagnostic codes. Results: Of the 351,555 TBI related hospitalizations in 2012, 47.9% (n = 168,317) were directly treated in a Level I or II trauma center, and an additional 20.3% (n = 71,286) were transferred to a Level I or II trauma center. The portion of the population treated at a trauma center (68.2%) was significantly lower than the portion of the U.S. population who has access to a major trauma center (90%). Further, nearly half of all transfers to a Level I or II trauma center were adults aged 55 and older (p < 0.001) and that 20.2% of pediatric patients arrive by non-ambulatory means. Conclusion: Utilization of trauma center resources for hospitalized TBIs may be low considering the established lower mortality rate associated with treatment at Level I or II trauma centers. The higher transfer rate for older adults may suggest rapid decline amid an unrecognized initial need for a trauma center care. A better understanding of hospital destination decision making is needed for patients with TBI.

Effects of leading mortality risk factors among trauma patients vary by age

Patient age is well recognized as a factor that contributes to increased mortality risk among trauma patients. Less well recognized is the potential that the strength of the effects of other risk factors that increase mortality risk may depend on a patient's age. This study examines whether the statistical relationship between trauma patient survival and key mortality risk factors varies significantly by patient age in years, across mechanisms of injury. The statistical interaction between age and values of key risk factors included in the Trauma Quality Improvement Program mortality risk adjustment model is assessed using patient data included in the 2008 National Trauma Data Bank National Sample Program. Multivariable logistic regression analysis is used to assess the statistical significance of the interaction effect on patient morality risk for key mortality risk factors and patient age in years, across mechanisms of injury. Statistically significant interactions (p < 0.01) occurred between age and each of the selected risk factors, for each common mechanism of injury. Differences also occurred in the direction of the interactions between age and selected risk factors, across mechanism of injury. The effects of key risk factors included in trauma patient mortality risk adjustment models vary depending on patient age in years, for each commonly occurring mechanism of injury. Statistical models assessing patient mortality risk could be meaningfully improved by accounting for these interaction effects. Prognostic and epidemiologic study, level III.

Patients with severe traumatic brain injury transferred to a Level I or II trauma center

Patients with severe traumatic brain injury (TBI), head Abbreviated Injury Scale (AIS) score of 3 or greater, who are indirectly transported from the scene of injury to a nontrauma center can experience delays to definitive neurosurgical management. Transport to a hospital with appropriate initial emergency department treatment and rapid admission has been shown to reduce mortality in a state's trauma system. This study was conducted to see if the same finding holds with a nationally representative sample of patients with severe TBI seen at Level I and II trauma centers. This study is based on adult (≥18 years), severe TBI patients treated in a nationally representative sample of Level I and II trauma centers, submitting data to the National Trauma Databank National Sample Program from 2007 to 2009. We analyzed independent variables including age, sex, primary payer, race, ethnicity, mode of transport, injury type (blunt vs. penetrating), mechanism of injury, trauma center level, head AIS, initial Glasgow Coma Scale (GCS), Injury Severity Score (ISS), and systolic blood pressure by transfer status. The primary outcome variable was inpatient death, with discharge disposition, neurosurgical procedures, and mean hospital, intensive care unit, and ventilator days serving as secondary outcomes. After exclusion criteria were applied (ISS < 16; age < 18 years; GCS motor score = 6; non-head AIS score ≥ 3; head AIS < 3; patients with missing transfer status, and death on arrival), a weighted sample of 51,300 (16%) patients was eligible for analysis. In bivariate analyses, transferred patients were older (≥60 years), white, insured, less severely injured (head AIS score ≤ 4, ISS ≤ 25), and less likely to have sustained penetrating trauma (p < 0.001). After controlling for all variables, direct transport, 1 or more comorbidities, advanced age, head AIS score, intracranial hemorrhage, and firearm injury remained significant predictors of death. Being transferred (adjusted odds ratio, 0.79; 95% confidence interval, 0.64-0.96) lowered the risk of death. Patients with severe TBI who were transferred to a Level I or II trauma center had lower injury severity, including less penetrating trauma, and, as a result, were less likely to die compared with patients who were directly admitted to a Level I or II trauma center. The results may demonstrate adherence with the current Guidelines for Prehospital Management of Traumatic Brain Injury and Guidelines for Field Triage of Injured Patients, which recommend the direct transport of patients with severe TBI to the highest level trauma center. Patients with severe TBI who cannot be taken to a trauma center should be stabilized at a nontrauma center and then transferred to a Level I or II trauma center. Regional and national trauma databases should consider collecting information on patient outcomes at referral facilities and total transport time after injury, to better address the outcomes of patient triage decisions. Prognostic study, level III; therapeutic study, level IV.

Ocular injuries in trauma patients

Trauma-induced eye injuries are the leading cause of monocular blindness in the United States. Few studies to date have focused on ocular injuries in the trauma population. Our intent was to determine the annual percentage of ocular injury, types of injuries, and percentage with ocular injury-related procedures performed during the same hospitalization. This study was a retrospective analysis of 28,340 patient records included in the National Trauma Data Bank National Sample Program from 2003 to 2007. Patients with ocular injuries and related procedures were identified using International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis and procedure codes and were subsequently grouped into categories of ocular injury using the criteria of the Barell body region injury diagnosis matrix. Weighted national estimates for the proportion of patients with ocular trauma were calculated based on the relative weights for patients in each facility within the sample universe. Weighted frequencies were expressed as a percentage of the total population of trauma admissions, with 95% confidence intervals calculated for precision. During the time frame examined, 1.97% to 6.00% of annual trauma patient admissions included ocular injuries. The most common injuries were contusions or superficial injuries and then closed orbit fractures accounting for 0.95% to 2.48% and 0.58% to 2.37% of all injuries, respectively. Between 0.56% and 1.52% of annual trauma admission had both ocular trauma and related procedures during their hospitalization. Popular treatments were therapeutic procedures on eyelids, conjunctiva, and/or cornea occurring in 0.15% to 0.84% of all trauma patients. Facial fracture-related procedures were reported for between 0.16% and 0.65% of all trauma patient admissions. The National Trauma Data Bank National Sample Program can be used to create useful estimates of ocular injury characteristics among patients seen in the population seen in trauma centers, including types of ocular injury and related procedures performed during the same admission. Epidemiologic study, level V.

The Trauma and Injury Severity Score (TRISS) revised

BackgroundThe Trauma and Injury Severity Score (TRISS) remains the most commonly used tool for benchmarking trauma fatality outcome. Recently, it was demonstrated that the predictive power of TRISS could be substantially improved by re-classifying the component variables and treating the variable categories nominally. This study aims to develop revised TRISS models using re-classified variables, to assess these models’ predictive performances against existing TRISS models, and to identify and recommend a preferred TRISS model. Materials and methodsRevised TRISS models for blunt and penetrating injury mechanism were developed on an adult (aged ≥15 years) sample from the National Trauma Data Bank National Sample Project (NSP), using 5-category variable classifications and weighted logistic regression. Their predictive performances were then assessed against existing TRISS models on the unweighted NSP, National Trauma Data Bank (NTDB), and New Zealand Database (NZDB) samples using area under the Receiver Operating Characteristic curve (AUC) and Bayesian Information Criterion (BIC) statistics. ResultsThe weighted NSP sample included 1,124,001 adults with blunt or penetrating injury mechanism events and known discharge status, of whom 1,061,709 (94.5%) survived to discharge. Complete information for all TRISS variables was available for 896,212 (79.7%). Revised TRISS models that included main-effects and two-factor interaction terms had superior AUC and BIC statistics to main-effects models and existing TRISS models for patients with complete data in NSP, NTDB and NZDB samples. Predictive performance decreased as the number of variables with missing values included within revised TRISS models increased, but model performances generally remained superior to existing TRISS models. DiscussionRevised TRISS models had importantly improved predictive capacities over existing TRISS models. Additionally, they were easily computed, utilised only those variables already collected for existing TRISS models, and could be applied and produce meaningful survival probabilities when one or more of the predictor variables contained missing values. The preferred revised TRISS model included main-effects and two-factor interaction terms and allowed for missing values in all predictor variables. A strong case exists for replacing existing TRISS models in trauma scoring systems benchmarking software with this preferred revised TRISS model.

Trauma and Injury Severity Score (TRISS): Is it time for variable re-categorisations and re-characterisations?

BackgroundDespite its limitations, the Trauma and Injury Severity Score (TRISS) continues to be the most commonly used tool for benchmarking trauma outcome. Since its inception, considerable energy has been devoted to improving TRISS. However, there has been no investigation into the classification or characterisation of the TRISS variables. Using a major nationally representative database, this study aims to explore the adequacy of the existing TRISS model by investigating variable re-categorisations and alternative characterisations in a logistic model used to predict survival in adults after traumatic injury. Materials and methodsData were obtained from the National Trauma Data Bank National Sample Project (NSP). Each variable in the TRISS model was related to discharge status and various categorisations considered using weighted logistic regression. Categorisations were treated nominally, using a series of indicator variables. For each variable and classification level, the best category combination was ascertained using the Bayesian Information Criterion (BIC). All best 5-category classified TRISS variables were combined, as were all best 10-category classified TRISS variables, and their predictive performance assessed against two conventionally defined TRISS models on the unweighted NSP sample using area under the Receiver Operating Characteristic curve (AUC) and BIC statistics. ResultsOverall, the weighted sample included 1,124,001 adults with injury events and known discharge status, of whom 1,061,709 (94.5%) were alive at discharge. When separately related to discharge status, each re-classified TRISS variable yielded a superior BIC statistic to its original specification. When investigating predictive performance, complete information was available for 167,239 (79.9%) adults with blunt and 20,643 (82.3%) adults with penetrating injury mechanisms. AUC and BIC estimates for the re-classified TRISS models were superior to the conventionally defined TRISS models. While having better predictive precision, the complexity associated with the best 10-category model resulted in the best 5-category model being preferred for penetrating mechanism injuries and being negligibly inferior for blunt mechanism injuries. DiscussionSubstantial improvements in the predictive power of TRISS were demonstrated by re-classifying the component variables and treating the variable categories nominally. However, before a new TRISS model with updated coefficients can be published, variable interactions and the effect of missing data needs thorough statistical evaluation.