Geographic Coverage and Verification of Trauma Centers in a Rural State: Highlighting the Utility of Location Allocationfor Trauma System Planning.

BACKGROUND: Care at verified trauma centers has improved survival and functional outcomes, yet determining the appropriate location of potential trauma centers is often driven by factors other than optimizing system-level patient care. Given the importance of transport time in trauma, we analyzed trauma transport patterns in a rural state lacking an organized trauma system and implemented a geographic information system to inform potential future trauma center locations. STUDY DESIGN: Data were collected on trauma ground transport during a 3-year period (2014 through 2016) from the Statewide Incident Reporting Network database. Geographic information system mapping and location-allocation modeling of the best-fit facility for trauma center verification was computed using trauma transport patterns, population density, road network layout, and 60-minute emergency medical services transport time based on current transport protocols. RESULTS: Location-allocation modeling identified 2 regional facilities positioned to become the next verified trauma centers. The proportion of the Vermont population without access to trauma center care within 60 minutes would be reduced from the current 29.68% to 5.81% if the identified facilities become verified centers. CONCLUSIONS: Through geospatial mapping and location-allocation modeling, we were able to identify gaps and suggest optimal trauma center locations to maximize population coverage in a rural state lacking a formal, organized trauma system. These findings could inform future decision-making for targeted capacity improvement and system design that emphasizes more equitable access to trauma center care in Vermont.

A trauma mortality prediction model based on the ICD-10-CM lexicon: TMPM-ICD10.

BACKGROUND: Outcome prediction models allow risk adjustment required for trauma research and the evaluation of outcomes. The advent of ICD-10-CM has rendered risk adjustment based on ICD-9-CM codes moot, but as yet no risk adjustment model based on ICD-10-CM codes has been described. METHODS: The National Trauma Data Bank provided data from 773,388 injured patients who presented to one of 747 trauma centers in 2016 with traumatic injuries ICD-10-CM codes and Injury Severity Score (ISS). We constructed an outcome prediction model using only ICD-10-CM acute injury codes and compared its performance with that of the ISS. RESULTS: Compared with ISS, the TMPM-ICD-10 discriminated survivors from non-survivors better (ROC TMPM-ICD-10 = 0.861 [0.860-0.872], ROC [reviever operating curve] ISS = 0.830 [0.823-0.836]), was better calibrated (HL [Hosmer-Lemeshow statistic] TMPM-ICD-10 = 49.01, HL ISS = 788.79), and had a lower Akaike information criteria (AIC TMPM-ICD10 = 30579.49; AIC ISS = 31802.18). CONCLUSIONS: Because TMPM-ICD10 provides better discrimination and calibration than the ISS and can be computed without recourse to Abbreviated Injury Scale coding, the TMPM-ICD10 should replace the ISS as the standard measure of overall injury severity for data coded in the ICD-10-CM lexicon. LEVEL OF EVIDENCE: Prognostic/Epidemiologic, level II.

Surgeons and Surgical Trainees Underestimate the Total Charges and Reimbursements Associated With Commonly Performed General Surgery Procedures✰.

INTRODUCTION: Surgical care contributes significantly to the fiscal challenges facing the US health care system. Multiple studies have demonstrated surgeons' lack of awareness of the costs associated with individual portions of surgical care, namely operating room supplies. We sought to assess surgeon and trainee awareness of the comprehensive charges and reimbursements associated with procedures they perform. METHODS: We administered a voluntary anonymous survey to attending surgeons, general surgery residents, and fourth-year medical students who applied to general surgery residencies. We compared charge and reimbursement estimates for laparoscopic cholecystectomy and open inguinal hernia repair to the actual values. Additionally, we assessed the importance placed on the financial aspects of surgical care. RESULTS: We had an overall response rate of 94% (n = 45). A majority of attendings, residents, and medical students underestimated charges and reimbursements for open inguinal hernia repair and laparoscopic cholecystectomy. There was no significant difference in the accuracy of charge or reimbursement estimates between attendings, residents, and students for herniorrhaphy or cholecystectomy (Charge: hernia p = 0.08, cholecystectomy p = 0.30; Reimbursement: hernia p = 0.47, cholecystectomy p = 0.89). Years of training as an attending or resident did not predict accuracy of charge or reimbursement estimates for hernia repair or cholecystectomy (p > 0.3 for all regressions). The median (interquartile range) charge estimate for inguinal hernia repair was -$5914 (-$7914 to -$2914) from the actual charge, 45.8% of the true value, and the median reimbursement estimate was -$4519 (-$5369 to -$1218) from actual reimbursement, 27.3% of the true value. The median charge estimate for cholecystectomy was -$5734 (-$8733 to +$1266) from the actual charge, 58.3% of the true value, and the median reimbursement estimate was -$4847 (-$6847 to +$153) from actual reimbursement, 38.2% of the true value. CONCLUSIONS: Surgeons and their trainees underestimate the charges and reimbursements associated with commonly performed procedures.

A novel approach to optimal placement of new trauma centers within an existing trauma system using geospatial mapping.

BACKGROUND: Trauma system expansion is a complex process often governed by financial and health care system imperatives. We sought to propose a new, informed approach to trauma system expansion through the use of geospatial mapping. We hypothesized that geospatial mapping set to specific parameters could effectively identify optimal placement of new trauma centers (TC) within an existing trauma system. METHODS: We used Pennsylvania Trauma Systems Foundation registry data of adult (age, ≥ 15 years) trauma for calendar years 2003 to 2015 (n = 408,432), hospital demographics, road networks, and US Census data files. We included TCs and zip codes outside of Pennsylvania to account for edge effects with trauma cases aggregated to the zip code centroid of residence. Our model assumptions included existing Pennsylvania Trauma Systems Foundation Level I and II TCs, a maximum travel time of 60 minutes to the TC, capacity based on mean statewide ratios of trauma cases per hospital bed size, Injury Severity Score, candidate hospitals with 200 or more licensed beds and 30 minutes or longer or 15 minutes or longer from an existing TC in nonurban/urban areas, respectively. We used the Network Analyst Location-Allocation function in ArcGIS Desktop to generate spatial models. RESULTS: Of the 130 candidate sites, only 14 met the bed size and travel time criteria from an existing TC. Approximately 70% of zip codes and 91% of cases were within 60 minutes of an existing TC. Adding one to six new optimally paced TCs increased to a maximum of 82% of zip codes and 96% of cases within 60 minutes of an existing TC. Changes to model assumptions had an impact on which candidate sites were selected. CONCLUSION: Intelligent trauma system design should include an objective process like geospatial to determine the optimum locations for new TCs within existing trauma networks. LEVEL OF EVIDENCE: Epidemiological study, level III.

Vena Cava Filter Use in Trauma and Rates of Pulmonary Embolism, 2003-2015.

IMPORTANCE: Vena cava filter (VCF) placement for pulmonary embolism (PE) prophylaxis in trauma is controversial. Limited research exists detailing trends in VCF use and occurrence of PE over time. OBJECTIVE: To analyze state and nationwide temporal trends in VCF placement and PE occurrence from 2003 to 2015 using available data sets. DESIGN, SETTING, AND PARTICIPANTS: A retrospective trauma cohort study was conducted using data from the Pennsylvania Trauma Outcome Study (PTOS) (461 974 patients from 2003 to 2015), the National Trauma Data Bank (NTDB) (5 755 095 patients from 2003 to 2014), and the National (Nationwide) Inpatient Sample (NIS) (24 449 476 patients from 2003 to 2013) databases. MAIN OUTCOMES AND MEASURES: Temporal trends in VCF placement and PE rates, filter type (prophylactic or therapeutic), and established predictors of PE (obesity, pregnancy, cancer, deep vein thrombosis, major procedure, spinal cord paralysis, venous injury, lower extremity fracture, pelvic fracture, central line, intracranial hemorrhage, and blood transfusion). Prophylactic filters were defined as VCFs placed before or without an existing PE, while therapeutic filters were defined as VCFs placed after a PE. RESULTS: Of the 461 974 patients in PTOS, the mean (SD) age was 47.2 (26.4) and 61.6% (284 621) were men; of the 5 755 095 patients in NTDB, the mean age (SD) was 42.0 (24.3) and 63.7% (3 666 504) were men; and of the 24 449 476 patients in NIS, the mean (SD) age was 58.0 (25.2) and 49.7% (12 160 231) were men. Of patients receiving a filter (11 405 in the PTOS, 71 029 in the NTDB, and 189 957 in the NIS), most were prophylactic VCFs (93.6% in the PTOS, 93.5% in the NTDB, and 93.3% in the NIS). Unadjusted and adjusted temporal trends for the PTOS and NTDB showed initial increases in filter placement followed by significant declines (unadjusted reductions in VCF placement rates, 76.8% in the PTOS and 53.3% in the NTDB). The NIS demonstrated a similar unadjusted trend, with a slight increase and modest decline (22.2%) in VCF placement rates over time; however, adjusted trends showed a slight but significant increase in filter rates. Adjusted PE rates for the PTOS and NTDB showed significant initial increases followed by slight decreases, with limited variation during the declining filter use periods. The NIS showed an initial increase in PE rates followed by a period of stagnation. CONCLUSIONS AND RELEVANCE: Despite a precipitous decline of VCF use in trauma, PE rates remained unchanged during this period. Taking this association into consideration, VCFs may have limited utility in influencing rates of PE. More judicious identification of at-risk patients is warranted to determine individuals who would most benefit from a VCF.

Implementation of the surgical safety checklist at a tertiary academic center: Impact on safety culture and patient outcomes.

BACKGROUND: The impact and efficacy of the World Health Organization Surgery Safety Checklist (SSC) is uncertain. We sought to determine if the SSC decreases complications and examined the attitudes of the surgical team members following implementation of the SSC. METHODS: A 28-question survey was developed to assess perspectives of surgical team members at the University of Vermont Medical Center (UVMC). The University Health System Consortium database was examined to compare the rates of nine complications before and after SSC implementation using Chi square analysis and Fisher's exact test. RESULTS: There was no significant decrease in any of the nine complications 2 years after SSC implementation. There was overall agreement that the SSC improved communication, safety, and prevented errors in the operating room. However, there was disagreement between nursing and surgeons over whether all three parts of the SSC were always completed. CONCLUSIONS: Implementation of the SSC did not result in a significant decrease in perioperative morbidity or mortality. However, it did improve the perception of safety culture by operating room staff.

Rectal cancer in patients under the age of 50 years: the delayed diagnosis.

BACKGROUND: The incidence of rectal cancer in younger patients continues to increase. Because most of these patients do not meet criteria for routine colorectal cancer screening, diagnosis may be delayed, potentially resulting in adverse outcomes. The aim of this study was to determine whether patients under the age of 50 years with rectal cancer have a delay in diagnosis and treatment leading to a worse overall prognosis. METHODS: A case control study of patients diagnosed with rectal adenocarcinoma in an academic medical center from 1997 to 2007 under 50 years of age were matched 1:1 to randomly selected patients over the age of 50 years by sex and date of diagnosis. Time to diagnosis, time to treatment, staging of the American Joint Committee on Cancer, and 5-year overall survival were compared. RESULTS: The overall time to treatment from symptom onset was 217 days for patients under the age of 50 years versus 29.5 days if over 50 years of age (P < .0001). The primary delay occurred between the onset of symptoms and presentation to the initial physician. There was no difference in stage at the time of diagnosis or 5-year survival (64% vs 71%, P = .39 and P = .54, respectively). CONCLUSIONS: Patients with rectal cancer under the age of 50 years have symptoms for a considerable period of time before seeking medical care and are referred in less timely manner to specialists. However, the delay in diagnosis did not adversely impact stage on presentation or 5-year survival.

Impact of Risk Adjustment for Socioeconomic Status on Risk-adjusted Surgical Readmission Rates.

OBJECTIVE: To assess whether differences in readmission rates between safety-net hospitals (SNH) and non-SNHs are due to differences in hospital quality, and to compare the results of hospital profiling with and without SES adjustment. BACKGROUND: In response to concerns that quality measures unfairly penalizes SNH, NQF recently recommended that performance measures adjust for socioeconomic status (SES) when SES is a risk factor for poor patient outcomes. METHODS: Multivariate regression was used to examine the association between SNH status and 30-day readmission after major surgery. The results of hospital profiling with and without SES adjustment were compared using the CMS Hospital Compare and the Hospital Readmissions Reduction Program (HRRP) methodologies. RESULTS: Adjusting for patient risk and SES, patients admitted to SNHs were not more likely to be readmitted compared with patients in in non-SNHs (AOR 1.08; 95% CI:0.95-1.23; P = 0.23). The results of hospital profiling based on Hospital Compare were nearly identical with and without SES adjustment (ICC 0.99, κ 0.96). Using the HRRP threshold approach, 61% of SNHs were assigned to the penalty group versus 50% of non-SNHs. After adjusting for SES, 51% of SNHs were assigned to the penalty group. CONCLUSIONS: Differences in surgery readmissions between SNHs and non-SNHs are due to differences in the patient case mix of low-SES patients, and not due to differences in quality. Adjusting readmission measures for SES leads to changes in hospital ranking using the HRRP threshold approach, but not using the CMS Hospital Compare methodology. CMS should consider either adjusting for the effects of SES when calculating readmission thresholds for HRRP, or replace it with the approach used in Hospital Compare.

Association between gender and outcomes of lower extremity peripheral vascular interventions.

OBJECTIVE: The purpose of this study was to evaluate the association of gender with outcomes of peripheral vascular intervention (PVI) for intermittent claudication and critical limb ischemia (CLI). METHODS: We reviewed 3338 patients (1316 [39%] women) undergoing PVI for claudication (1892; 57%) or CLI (1446; 43%) in the Vascular Study Group of New England from January 2010 to June 2012. Kaplan-Meier analysis, stratified by indication, was used to assess relationships between gender and the main outcome measures of major amputation, reintervention, and survival during the first year. RESULTS: Indications for PVI included claudication (n = 719 [38%] vs n = 1173 [62%]) and CLI (n = 597 [41%] vs n = 849 [59%]) in women and men, respectively (P = .0028). Women were older (69 vs 66 mean years; P < .00001), with less diabetes (43% vs 49%; P = .01), renal insufficiency (4.6% vs 7.3%; P = .0029), coronary artery disease (28% vs 35%; P < .00001), smoking (76% vs 86%; P = .01), and statin use (60% vs 64%; P = .0058). Technical success (95% vs 94%; P = .11), vascular injury (1.3% vs 1.0%; P = .82), and distal embolization (1.6% vs 1.3%; P = .46) were similar. Higher rates of hematoma (7.1% vs 3.4%; P ≤ .0001) and access site occlusion (0.91% vs 0.24%; P = .0085) were observed in women compared with men. There were no differences in major amputation (0.6% vs 0.6%; P = .81) or mortality (2.1% vs 1.5%; P = .20) rates at 30 days between women and men. Reinterventions (surgical and percutaneous) were similar between genders for claudicants (log-rank test, P = .75) and CLI patients (log-rank test, P = .93). Major amputation rates during the first year were not different for women and men and with claudication (log-rank test, P < .55) or CLI (log-rank test, P < .23). One-year survival was not different between women and men with claudication (95% vs 96%; P = .19) or CLI (77% vs 79%; P = .35). CONCLUSIONS: Whereas we observed higher rates of access site complications including hematoma and occlusion in women, we found no other evidence for gender disparity in reinterventions, major amputation, or survival rates after PVI for patients with claudication or CLI.

ICD-9-CM and ICD-10-CM mapping of the AAST Emergency General Surgery disease severity grading systems: Conceptual approach, limitations, and recommendations for the future.

The American Association for the Surgery of Trauma (AAST) recently established a grading system for uniform reporting of anatomic severity of several emergency general surgery (EGS) diseases. There are five grades of severity for each disease, ranging from I (lowest severity) to V (highest severity). However, the grading process requires manual chart review. We sought to evaluate whether International Classification of Diseases, 9th and 10th Revisions, Clinical Modification (ICD-9-CM, ICD-10-CM) codes might allow estimation of AAST grades for EGS diseases. The Patient Assessment and Outcomes Committee of the AAST reviewed all available ICD-9-CM and ICD-10-CM diagnosis codes relevant to 16 EGS diseases with available AAST grades. We then matched grades for each EGS disease with one or more ICD codes. We used the Official Coding Guidelines for ICD-9-CM and ICD-10-CM and the American Hospital Association's "Coding Clinic for ICD-9-CM" for coding guidance. The ICD codes did not allow for matching all five AAST grades of severity for each of the 16 diseases. With ICD-9-CM, six diseases mapped into four categories of severity (instead of five), another six diseases into three categories of severity, and four diseases into only two categories of severity. With ICD-10-CM, five diseases mapped into four categories of severity, seven diseases into three categories, and four diseases into two categories. Two diseases mapped into discontinuous categories of grades (two in ICD-9-CM and one in ICD-10-CM). Although resolution is limited, ICD-9-CM and ICD-10-CM diagnosis codes might have some utility in roughly approximating the severity of the AAST grades in the absence of more precise information. These ICD mappings should be validated and refined before widespread use to characterize EGS disease severity. In the long-term, it may be desirable to develop alternatives to ICD-9-CM and ICD-10-CM codes for routine collection of disease severity characteristics.

Hospital readmission after noncardiac surgery: the role of major complications.

IMPORTANCE: Hospital readmissions are believed to be an indicator of suboptimal care and are the focus of efforts by the Centers for Medicare and Medicaid Services to reduce health care cost and improve quality. Strategies to reduce surgical readmissions may be most effective if applied prospectively to patients who are at increased risk for readmission. Hospitals do not currently have the means to identify surgical patients who are at high risk for unplanned rehospitalizations. OBJECTIVE: To examine whether the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) predicted risk of major complications can be used to identify surgical patients at risk for rehospitalization. DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort study of 142,232 admissions in the ACS NSQIP registry for major noncardiac surgery. MAIN OUTCOMES AND MEASURES: The association between unplanned 30-day readmission and the ACS NSQIP predicted risk of major complications, controlling for severity of disease and surgical complexity. RESULTS: Of the 143,232 patients undergoing noncardiac surgery, 6.8% had unplanned 30-day readmissions. The rate of unplanned 30-day readmissions was 78.3% for patients with any postdischarge complication, compared with 12.3% for patients with only in-hospital complications and 4.8% for patients without any complications. Patients at very high risk for major complications (predicted risk of ACS NSQIP complication >10%) had 10-fold higher odds of readmission compared with patients at very low risk for complications (adjusted odds ratio = 10.35; 95% CI, 9.16-11.70), whereas patients at high (adjusted odds ratio = 6.57; 95% CI, 5.89-7.34) and moderate (adjusted odds ratio = 3.96; 95% CI, 3.57-4.39) risk of complications had 7- and 4-fold higher odds of readmission, respectively. CONCLUSIONS AND RELEVANCE: Unplanned readmissions in surgical patients are common in patients experiencing postoperative complications and can be predicted using the ACS NSQIP risk of major complications. Prospective identification of high-risk patients, using the NSQIP complication risk index, may allow hospitals to reduce unplanned rehospitalizations.

Preoperative thrombocytopenia and postoperative outcomes after noncardiac surgery.

BACKGROUND: Most studies examining the prognostic value of preoperative coagulation testing are too small to examine the predictive value of routine preoperative coagulation testing in patients having noncardiac surgery. METHODS: Using data from the American College of Surgeons National Surgical Quality Improvement database, the authors performed a retrospective observational study on 316,644 patients having noncardiac surgery who did not have clinical indications for preoperative coagulation testing. The authors used multivariable logistic regression analysis to explore the association between platelet count abnormalities and red cell transfusion, mortality, and major complications. RESULTS: Thrombocytopenia or thrombocytosis occurred in 1 in 14 patients without clinical indications for preoperative platelet testing. Patients with mild thrombocytopenia (101,000-150,000 µl), moderate-to-severe thrombocytopenia (<100,000 µl), and thrombocytosis (≥450,000 µl) were significantly more likely to be transfused (7.3%, 11.8%, 8.9%, 3.1%) and had significantly higher 30-day mortality rates (1.5%, 2.6%, 0.9%, 0.5%) compared with patients with a normal platelet count. In the multivariable analyses, mild thrombocytopenia (adjusted odds ratio [AOR], 1.28; 95% CI, 1.18-1.39) and moderate-to-severe thrombocytopenia (AOR, 1.76; 95% CI, 1.49-2.08), and thrombocytosis (AOR, 1.44; 95% CI, 1.30-1.60) were associated with increased risk of blood transfusion. Mild thrombocytopenia (AOR, 1.31; 95% CI, 1.11-1.56) and moderate-to-severe thrombocytopenia (AOR, 1.93; 95% CI, 1.43-2.61) were also associated with increased risk of 30-day mortality, whereas thrombocytosis was not (AOR, 0.94; 95% CI, 0.72-1.22). CONCLUSION: Platelet count abnormalities found in the course of routine preoperative screening are associated with a higher risk of blood transfusion and death.

Effectiveness of nonpublic report cards for reducing trauma mortality.

IMPORTANCE: An Institute of Medicine report on patient safety that cited medical errors as the 8th leading cause of death fueled demand to use quality measurement as a catalyst for improving health care quality. OBJECTIVE: To determine whether providing hospitals with benchmarking information on their risk-adjusted trauma mortality outcomes will decrease mortality in trauma patients. DESIGN, SETTING, AND PARTICIPANTS: Hospitals were provided confidential reports of their trauma risk-adjusted mortality rates using data from the National Trauma Data Bank. Regression discontinuity modeling was used to examine the impact of nonpublic reporting on in-hospital mortality in a cohort of 326206 trauma patients admitted to 44 hospitals, controlling for injury severity, patient case mix, hospital effects, and preexisting time trends. MAIN OUTCOMES AND MEASURES: In-hospital mortality rates. RESULTS Performance benchmarking was not significantly associated with lower in-hospital mortality (adjusted odds ratio [AOR], 0.89; 95% CI, 0.68-1.16; P=.39). Similar results were obtained in secondary analyses after stratifying patients by mechanism of trauma: blunt trauma (AOR, 0.91; 95% CI, 0.69-1.20; P=.51) and penetrating trauma (AOR, 0.75; 95% CI, 0.44-1.28; P=.29). We also did not find a significant association between nonpublic reporting and in-hospital mortality in either low-risk (AOR, 0.84; 95% CI, 0.57-1.25; P=.40) or high-risk (AOR, 0.88; 95% CI, 0.67-1.17; P=.38) patients. CONCLUSIONS AND RELEVANCE Nonpublic reporting of hospital risk-adjusted mortality rates does not lead to improved trauma mortality outcomes. The findings of this study may prove useful to the American College of Surgeons as it moves ahead to further develop and expand its national trauma benchmarking program.

Ranking trauma center quality: can past performance predict future performance?

OBJECTIVE: To explore whether trauma center quality metrics based on historical data can reliably predict future trauma center performance. BACKGROUND: The goal of the American College of Surgeons Trauma Quality Improvement Program is to create a new paradigm in which high-quality trauma centers can serve as learning laboratories to identify best practices. This approach assumes that trauma quality reporting can reliably identify high-quality centers using historical data. METHODS: We performed a retrospective observational study on 122,408 patients in 22 level I and level II trauma centers in Pennsylvania. We tested the ability of the Trauma Mortality Prediction Model to predict future hospital performance based on historical data. RESULTS: Patients admitted to the lowest performance hospital quintile had a 2-fold higher odds of mortality than patients admitted to the best performance hospital quintile using either 2-year-old data [adjusted odds ratio (AOR): 2.11; 95% confidence interval (CI): 1.36-3.27; P < 0.001] or 3-year-old data (AOR: 2.12; 95% CI: 1.34-3.21; P < 0.001). There was a trend toward increased mortality using 5-year-old data (AOR: 1.70; 95% CI: 0.98-2.95; P = 0.059). The correlation between hospital observed-to-expected mortality ratios in 2009 and 2007 demonstrated moderate agreement (intraclass correlation coefficient = 0.56; 95% CI: 0.22-0.77). The intraclass correlation coefficients for observed-to-expected mortality ratios obtained using 2009 data and 3-, 4-, or 5-year-old data were not significantly different from zero. CONCLUSIONS: Trauma center quality based on historical data is associated with subsequent patient outcomes. Patients currently admitted to trauma centers that are classified as low-quality centers using 2- to 5-year-old data are more likely to die than patients admitted to high-quality centers. However, although the future performance of individual trauma centers can be predicted using performance metrics based on 2-year-old data, the performance of individual centers cannot be predicted using data that are 3 years or older.

Impact of obesity on mortality and complications in trauma patients.

OBJECTIVE: To examine the association between obesity and outcomes in injured patients. BACKGROUND: The United States is facing an obesity epidemic affecting 1 in 3 adult Americans. Very little is known about the role of obesity in acute illness. Optimal care of obese trauma patients can only be achieved once we gain a better understanding of the impact of severe obesity on trauma outcomes. METHODS: We conducted a retrospective cohort study of 147,680 patients admitted to 28 level I and level II Pennsylvania trauma centers between 2000 and 2009. Logistic regression was used to examine the association between obesity and in-hospital mortality and major complications, adjusting for injury severity, age, gender, mechanism of injury, systolic blood pressure, and the motor component of the Glasgow Coma Scale, comorbidities, and year of admission. Patients were grouped into predefined weight categories: underweight (<1st percentile), reference (1st-74th percentile), grade 1 obesity (75th-90th percentile), grade 2 obesity (91th-95th percentile), grade 3 obesity (96th-99th percentile), and grade 4 obesity (>99th percentile). Body mass index was not calculated because height data was not available. RESULTS: After adjusting for injury severity and other risk factors, male patients with severe obesity-grade 3 obesity [adjusted odds ratio (AOR) 1.28; 95% confidence interval (CI): 1.00, 1.64; P = 0.052] or grade 4 obesity (AOR 2.30; 95% CI: 1.48, 3.58; P < 0.001)-were more likely to die than nonobese patients. Severe obesity was associated with an approximately twofold higher risk of major complications: male patients with grade 3 obesity (AOR 1.71; 95% CI: 1.48, 1.97; P < 0. 001) or grade 4 obesity (AOR 2.14; 95% CI: 1.83, 2.51; P < 0.001). Similar results were obtained for female patients. Male and female patients with severe obesity had a 2.5- to 4-fold higher risk of developing acute renal failure. Severely obese females had 2.5- to 4.5-fold higher risk of developing wound complications, and a 4-to 8-fold higher risk of developing decubiti. CONCLUSIONS: Severely obese trauma patients were at least 30% more likely to die and approximately twice as likely to have a major complication compared with nonobese patients.