This study aimed to determine the relative "weight" of risk factors known to be associated with venous thromboembolism (VTE) for patients with trauma based on injuries and comorbidities. A retrospective review of 16,608 consecutive admissions to a trauma center was performed. Patients were separated into those who developed VTE (n = 141) versus those who did not (16,467). Univariate analysis was performed for each risk factor reported in the trauma literature. Risk factors that were shown to be significant (p < 0.05) by univariate analysis underwent multivariate analysis to develop odds ratios for VTE. The Trauma Embolic Scoring System (TESS) was derived from the multivariate coefficients. The resulting TESS was compared with a data set from the National Trauma Data Bank (2002-2006) to determine its ability to predict VTE. The multivariate analysis demonstrated that age, Injury Severity Score, obesity, ventilator use for more than 3 days, and lower-extremity trauma were significant predictors of VTE in our patient population. The TESS was from 0 to 14, with the best prediction for those patients with a score of more than 6 (sensitivity, 81.6%; specificity, 84%). Overall, the model had excellent discrimination in predicting VTE with a receiver operating characteristic curve of 0.89. The VTE rates for TESS in the National Trauma Data Bank data set were similar for all integers except for 3 and 4, in which the VTE rates were significantly higher (3, 0.2% vs. 0.6%; 4, 0.4% vs. 1.0%). The TESS provides an objective measure of classifying VTE risk for patients with trauma. The TESS could allow informed decision making regarding prophylaxis strategies in patients with trauma.

The Affordable Care Act of 2010 identifies "patient experience of care" as one of five domains of excellent care. We hypothesized that there are specific demographic factors associated with higher or lower physician satisfaction (PS) scores in trauma patients. Press-Ganey PS scores for September 2004 to December 2010 were compared with trauma variables and the association of a mean PS greater than or equal to 75 (high score) or less than or equal to 50 (low score). Those variables that proved significant on univariate analysis were subjected to multivariate logistic regression analysis. Significance was at p < 0.05. There were 12,196 admissions, of whom 1,631 (13.4%) returned patient satisfaction survey. A total of 1,174 patients (75.5%) returned a high PS (≥75), and 126 patients (8.1%) returned a low PS (≤50). In the multiple logistic regression analysis, 65 years or older (odds ratio [OR], 1.7), having had a surgical procedure (OR, 1.6), and having a positive impression of the hospital care (OR, 7.0) proved significant for a high PS. Those patients who scored a low PS were significantly more likely to be younger (18-29 years: OR, 2.4; 30-64 years: OR, 1.8), to have not had surgery (OR, 2.2), had an Injury Severity Score (ISS) of 16 or lower (OR, 2.6), had a complication of care (OR, 4.4), and rated the hospital care as poor (OR, 9.2). A trauma patient who is satisfied with his or her physician care is one who is 65 years or older, requires surgery, and is predominantly satisfied with other aspects of their hospital care. Unsatisfied patients are younger, are nonoperative, had lower ISS, had a complication of care, and rated their hospital care as poor. Understanding the specific characteristics of Press-Ganey results for trauma patients will allow trauma surgeons and their hospital partners to develop strategies to improve patients' satisfaction with their trauma surgeon's care. Epidemiologic study, level III; therapeutic study, level IV.

The Trauma and Injury Severity Score (TRISS) has been the approach to trauma outcome prediction during the past 20 years and has been adopted by many commercial registries. Unfortunately, its survival predictions are based upon coefficients that were derived from a data set collected in the 1980s and updated only once using a data set collected in the early 1990s. We hypothesized that the improvements in trauma care during the past 20 years would lead to improved survival in a large database, thus making the TRISS biased. The TRISSs from the Pennsylvania statewide trauma registry (Collector, Digital Innovations) for the years 1990 to 2010. Observed-to-expected mortality ratios for each year of the study were calculated by taking the ratio of actual deaths (observed deaths, O) to the summation of the probability of mortality predicted by the TRISS taken over all patients (expected deaths, E). For reference, O/E ratio should approach 1 if the TRISS is well calibrated (i.e., has predictive accuracy). There were 408,489 patients with complete data sufficient to calculate the TRISSs. There was a significant trend toward improved outcome (i.e., decreasing O/E ratio; nonparametric test of trend, p < 0.001) over time in both the total population and the blunt trauma subpopulation. In the penetrating trauma population, there was a trend toward improved outcome (decreasing O/E ratio), but it did not quite reach significance (nonparametric test of trend p = 0.073). There is a steady trend toward improved O/E survival in the Pennsylvania database with each passing year, suggesting that the TRISS is drifting out of calibration. It is likely that improvements in care account for these changes. For the TRISS to remain an accurate outcome prediction model, new coefficients would need to be calculated periodically to keep up with trends in trauma care. This requirement for occasional updating is likely to be a requirement of any trauma prediction model, but because many other deficiencies in the TRISS have been reported, we think that rather than updating the TRISS, it would be more productive to replace the TRISS with a modern statistical model.