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IMPORTANCE: The GCS was created forty years ago as a measure of impaired consciousness following head injury and thus the association of GCS with mortality in patients with traumatic brain injury (TBI) is expected. The association of GCS with mortality in patients without TBI (non-TBI) has been assumed to be similar. However, if this assumption is incorrect mortality prediction models incorporating GCS as a predictor will need to be revised. OBJECTIVE: To determine if the association of GCS with mortality is influenced by the presence of TBI. DESIGN/SETTING/PARTICIPANTS: Using the National Trauma Data Bank (2012; N=639,549) we categorized patients as isolated TBI (12.8%), isolated non-TBI (33%), both (4.8%), or neither (49.4%) based on the presence of AIS codes of severity 3 or greater. We compared the ability GCS to discriminate survivors from non-survivors in TBI and in non-TBI patients using logistic models. We also estimated the odds ratios of death for TBI and non-TBI patients at each value of GCS using linear combinations of coefficients. MAIN OUTCOME MEASURE: Death during hospital admission. RESULTS: As the sole predictor in a logistic model GCS discriminated survivors from non-survivors at an acceptable level (c-statistic=0.76), but discriminated better in the case of TBI patients (c-statistic=0.81) than non-TBI patients (c-statistic=0.70). In both unadjusted and covariate adjusted models TBI patients were about twice as likely to die as non-TBI patients with the same GCS for GCS values<8; for GCS values>8 TBI and non-TBI patients were at similar risk of dying. CONCLUSIONS: A depressed GCS predicts death better in TBI patients than non-TBI patients, likely because in non-TBI patients a depressed GCS may simply be the result of entirely reversible intoxication by alcohol or drugs; in TBI patients, by contrast, a depressed GCS is more ominous because it is likely due to a head injury with its attendant threat to survival. Accounting for this observation into trauma mortality datasets and models may improve the accuracy of outcome prediction.

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IMPORTANCE: Massachusetts introduced health care reform (HCR) in 2006, expecting to expand health insurance coverage and improve outcomes. Because traumatic injury is a common acute condition with important health, disability, and economic consequences, examination of the effect of HCR on patients hospitalized following injury may help inform the national HCR debate. OBJECTIVE: To examine the effect of Massachusetts HCR on survival rates of injured patients. DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort study of 1,520,599 patients hospitalized following traumatic injury in Massachusetts or New York during the 10 years (2002-2011) surrounding Massachusetts HCR using data from the State Inpatient Databases. We assessed the effect of HCR on mortality rates using a difference-in-differences approach to control for temporal trends in mortality. INTERVENTION: Health care reform in Massachusetts in 2006. MAIN OUTCOME AND MEASURE: Survival until hospital discharge. RESULTS: During the 10-year study period, the rates of uninsured trauma patients in Massachusetts decreased steadily from 14.9% in 2002 to 5.0.% in 2011. In New York, the rates of uninsured trauma patients fell from 14.9% in 2002 to 10.5% in 2011. The risk-adjusted difference-in-difference assessment revealed a transient increase of 604 excess deaths (95% CI, 419-790) in Massachusetts in the 3 years following implementation of HCR. CONCLUSIONS AND RELEVANCE: Health care reform did not affect health insurance coverage for patients hospitalized following injury but was associated with a transient increase in adjusted mortality rates. Reducing mortality rates for acutely injured patients may require more comprehensive interventions than simply promoting health insurance coverage through legislation.

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BACKGROUND: Previous studies have reported that black race and lack of health insurance coverage are associated with increased mortality following traumatic injury. However, the association of race and insurance status with trauma outcomes has not been examined using contemporary, national, population-based data. METHODS: We used data from the National Inpatient Sample on 215,615 patients admitted to 1 of 836 hospitals following traumatic injury in 2010. We examined the effects of race and insurance coverage on mortality using two logistic regression models, one for patients younger than 65 years and the other for older patients. RESULTS: Unadjusted mortality was low for white (2.71%), black (2.54%), and Hispanic (2.03%) patients. We found no difference in adjusted survival for nonelderly black patients compared with white patients (adjusted odds ratio [AOR], 1.04; 95% confidence interval [CI], 0.90-1.19; p = 0.550). Elderly black patients had a 25% lower odds of mortality compared with elderly white patients (AOR, 0.75; 95% CI, 0.63-0.90; p = 0.002). After accounting for survivor bias, insurance coverage was not associated with improved survival in younger patients (AOR, 0.91; 95% CI, 0.77-1.07; p = 0.233). CONCLUSION: Black race is not associated with higher mortality following injury. Health insurance coverage is associated with lower mortality, but this may be the result of hospitals' inability to quickly obtain insurance coverage for uninsured patients who die early in their hospital stay. Increasing insurance coverage may not improve survival for patients hospitalized following injury. LEVEL OF EVIDENCE: Epidemiologic and prognostic study, level III.

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OBJECTIVE: To develop a statistically rigorous trauma mortality prediction model based on empiric estimates of severity for each injury in the abbreviated injury scale (AIS) and compare the performance of this new model with the injury severity score (ISS). SUMMARY BACKGROUND DATA: Mortality rates at trauma centers should only be compared after adjusting for differences in injury severity, but no reliable measure of injury severity currently exists. The ISS has served as the standard measure of anatomic injury for 30 years. However, it relies on the individual injury severities assigned by experts in the AIS, is nonmonotonic with respect to mortality, and fails to perform even as well as a far simpler model based on the single worst injury a patient has sustained. METHODS: This study is based on data from 702,229 injured patients in the National Trauma Data Bank (NTDB 6.1) hospitalized between 2001 and 2005. Sixty percent of the data was used to derive an empiric measure of severity of each of the 1322 injuries in the AIS lexicon by taking the weighted average of coefficients estimated using 2 separate regression models. The remaining 40% of the data was use to create 3 exploratory mortality prediction models and compare their performance with the ISS using measures of discrimination (C statistic), calibration (Hosmer Lemeshow statistic and calibration curves), and the Akaike information criterion. RESULTS: Three new models based on empiric AIS injury severities were developed. All of these new models discriminated survivors from nonsurvivors better than the ISS, but one, the trauma mortality prediction model (TMPM), had both better discrimination [ROCTMPM = 0.901 (0.898-0.905), ROCISS = 0.871 (0.866-0.877)] and better calibration [HLTMPM = 58 (35-91), HLISS = 296 (228-357)] than the ISS. The addition of age, gender, and mechanism of injury improved all models, but the augmented TMPM dominated ISS by every measure [ROCTMPM = 0.925(0.921-0.928), ROCISS = 0.904(0.901-0.909), HLTMPM = 18 (12-31), HLISS = 54 (30-64)]. CONCLUSIONS: Trauma mortality models based on empirical estimates of individual injury severity better discriminate between survivors and nonsurvivors than does the current standard, ISS. One such model, the TMPM, has both superior discrimination and calibration when compared with the ISS. The TMPM should replace the ISS as the standard measure of overall injury severity.

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