Zain G. Hashmi

Hospital-based trauma quality improvement initiatives: first step toward improving trauma outcomes in the developing world.

BACKGROUND Injuries remain a leading cause of death in the developing world. Whereas new investments are welcome, quality improvement (QI) at the currently available trauma care facilities is essential. The objective of this study was to determine the effect and long-term sustainability of trauma QI initiatives on in-hospital mortality and complications at a large tertiary hospital in a developing country. METHODS In 2002, a specialized trauma team was formed (members trained using advanced trauma life support), and a western style trauma program established including a registry and quality assurance program. Patients from 1998 onward were entered in to this registry, enabling a preimplementation and postimplementation study. Adults (>15 years) with blunt or penetrating trauma were analyzed. The main outcomes of interest were (1) in-hospital mortality and (2) occurrence of any complication. Multiple logistic regression was performed to assess the impact of formalized trauma care on outcomes, controlling for covariates reaching significance in the bivariate analyses. RESULTS A total of 1,227 patient records were analyzed. Patient demographics and injury characteristics are described in Table 1. Overall in-hospital mortality rate was 6.4%, and the complication rate was 11.1%. On multivariate analysis, patients admitted during the trauma service years were 4.9 times less likely to die (95% confidence interval, 1.77–13.57) and 2.60 times (odds ratio; 95% confidence interval, 1.29–5.21) less likely to have a complication compared with those treated in the pretrauma service years. CONCLUSION Despite significant delays in hospital transit and lack of prehospital trauma care, hospital level implementation of trauma QI program greatly decreases mortality and complication rates in the developing world. LEVEL OF EVIDENCE Care management study, level IV.

Developing best practices to study trauma outcomes in large databases: an evidence-based approach to determine the best mortality risk adjustment model.

BACKGROUND The National Trauma Data Bank (NTDB) is an invaluable resource to study trauma outcomes. Recent evidence suggests the existence of great variability in covariate handling and inclusion in multivariable analyses using NTDB, leading to differences in the quality of published studies and potentially in benchmarking trauma centers. Our objectives were to identify the best possible mortality risk adjustment model (RAM) and to define the minimum number of covariates required to adequately predict trauma mortality in the NTDB. METHODS Analysis of NTDB 2009 was performed to identify the best RAM for trauma mortality. For each plausible NTDB covariate, univariate logistic regression was performed, and the area under the receiver operating characteristics curve (AUROC, with 95% confidence interval [CI]) was calculated. Covariates with p < 0.01 and an AUROC of 0.6 of greater or with strong previous evidence were included in the subsequent multivariate logistic regression analyses. Manual backward selection was then used to identify the most parsimonious RAM with a similar AUROC (overlapping 95% CI). Similar analyses were performed for penetrating and severely injured patient subsets. All models were validated using NTDB 2010. RESULTS A total of 630,307 patients from NTDB 2009 were analyzed. A total of 16 of 106 NTDB covariates tested on univariate analyses were selected for inclusion in the initial multivariate model. The best RAM included only six covariates (age, hypotension, pulse, total Glasgow Coma Scale [GCS] score, Injury Severity Score [ISS], and a need for ventilator use) yet still demonstrated excellent discrimination between survivors and nonsurvivors (AUROC, 0.9578; 95% CI, 0.9565–0.9590). In addition, this model was validated on 665,138 patients included in NTDB 2010 (AUROC, 0.9577; 95% CI, 0.9564–0.9589). Similar results were obtained for the subset analyses. CONCLUSION This quantitative synthesis proposes a framework and a set of covariates for studying trauma mortality outcomes. Such analytic standardization may prove critical in implementing best practices aimed at improving the quality and consistency of NTDB-based research. LEVEL OF EVIDENCE Prognostic study, level III.

Outcomes after emergency general surgery at teaching versus nonteaching hospitals.

BACKGROUND Previous analyses demonstrate teaching hospitals to have worse outcomes raising concerns for quality of care. The purpose of this study was to compare outcomes between teaching and nonteaching hospitals for emergency surgical conditions in a national sample. METHODS The Nationwide Inpatient Sample (2005–2011) was queried for patients with emergency general surgery (EGS) conditions as determined by the American Association for Surgery of Trauma. Outcomes of in-hospital mortality, major complications, length of stay (LOS) and hospital cost were compared between patients presenting to teaching versus nonteaching hospitals. Propensity scores were used to match both groups on demographics, clinical diagnosis, comorbidities, and disease severity. Multivariate regression analyses were performed further adjusting for hospital-level factors including EGS volume. Small effect estimates were further tested using standardized differences. RESULTS A total of 3,707,465 patients from 3,163 centers were included. A majority of patients (59%) (n = 2,187,107) were treated at nonteaching hospitals. After propensity score matching and adjustment, teaching hospitals had a slightly higher odds likelihood of mortality (odds ratio, 1.04; 95% confidence interval, 1.02–1.06), slightly lower rate of major complications (odds ratio, 0.99; 95% confidence interval, 0.98–0.99), slightly decreased LOS (5.03 days [4.98–5.09] vs. 5.22 days [5.16–5.29]), and slightly higher hospital costs [

Association Between Race and Age in Survival After Trauma

12,846 [

Outcomes of trauma care at centers treating a higher proportion of older patients: The case for geriatric trauma centers

12,827–

Reliability adjustment: A necessity for trauma center ranking and benchmarking

12,865] vs.

Minority trauma patients tend to cluster at trauma centers with worse-than-expected mortality: Can this phenomenon help explain racial disparities in trauma outcomes?

12,304 [12,290–12,318]). Although these differences were statistically significant at p < 0.05, the absolute difference was very small. Further testing of these effect estimates using standardized differences revealed an insignificant difference of 0.5% for mortality, 0.4% for major complications, 0.2% for LOS, and 3.1% for hospital cost. CONCLUSION National estimates of outcomes for EGS conditions demonstrate comparable results between teaching and nonteaching hospitals. Concerns regarding quality of care and higher costs at teaching hospitals may be unfounded. Further research to test for differences by specific EGS conditions, operative management, and hospital costs are warranted.

Racial/ethnic disparities in emergency general surgery: explained by hospital-level characteristics?

IMPORTANCE Racial disparities in survival after trauma are well described for patients younger than 65 years. Similar information among older patients is lacking because existing trauma databases do not include important patient comorbidity information. OBJECTIVE To determine whether racial disparities in trauma survival persist in patients 65 years or older. DESIGN, SETTING, AND PARTICIPANTS Trauma patients were identified from the Nationwide Inpatient Sample (January 1, 2003, through December 30, 2010) using International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes. Injury severity was ascertained by applying the Trauma Mortality Prediction Model, and patient comorbidities were quantified using the Charlson Comorbidity Index. MAIN OUTCOMES AND MEASURES In-hospital mortality after trauma for blacks vs whites for younger (16-64 years of age) and older (≥65 years of age) patients was compared using 3 different statistical methods: univariable logistic regression, multivariable logistic regression with and without clustering for hospital effects, and coarsened exact matching. Model covariates included age, sex, insurance status, type and intent of injury, injury severity, head injury severity, and Charlson Comorbidity Index. RESULTS A total of 1,073,195 patients were included (502,167 patients 16-64 years of age and 571,028 patients ≥65 years of age). Most older patients were white (547,325 [95.8%]), female (406 158 [71.1%]), and insured (567,361 [99.4%]) and had Charlson Comorbidity Index scores of 1 or higher (323,741 [56.7%]). The unadjusted odds ratios (ORs) for death in blacks vs whites were 1.35 (95% CI, 1.28-1.42) for patients 16 to 64 years of age and 1.00 (95% CI, 0.93-1.08) for patients 65 years or older. After risk adjustment, racial disparities in survival persisted in the younger black group (OR, 1.21; 95% CI, 1.13-1.30) but were reversed in the older group (OR, 0.83; 95% CI, 0.76-0.90). This finding was consistent across all 3 statistical methods. CONCLUSIONS AND RELEVANCE Different racial disparities in survival after trauma exist between white and black patients depending on their age group. Although younger white patients have better outcomes after trauma than younger black patients, older black patients have better outcomes than older white patients. Exploration of this paradoxical finding may lead to a better understanding of the mechanisms that cause disparities in trauma outcomes.

Benchmarking trauma centers on mortality alone does not reflect quality of care: implications for pay-for-performance.

BACKGROUND The burden of injury among older patients continues to grow and accounts for a disproportionate number of trauma deaths. We wished to determine if older trauma patients have better outcomes at centers that manage a higher proportion of older trauma patients. METHODS The National Trauma Data Bank years 2007 to 2011 was used. All high-volume Level 1 and Level 2 trauma centers were included. Trauma centers were categorized by the proportion of older patients seen. Adult trauma patients were categorized as older (≥65 years) and younger adults (16–64 years). Coarsened exact matching was used to determine differences in mortality and length of stay between older and younger adults. Risk-adjusted mortality ratios by proportion of older trauma patients seen were analyzed using multivariate logistic regression models and observed-expected ratios. RESULTS A total of 1.9 million patients from 295 centers were included. Older patients accounted for one fourth of trauma visits. Matched analysis revealed that older trauma patients were 4.2 times (95% confidence interval, 3.99–4.50) more likely to die than younger patients. Older patients were 34% less likely to die if they presented at centers treating a high versus low proportion of older trauma (odds ratio, 0.66; 95% confidence interval, 0.54–0.81). These differences were independent of trauma center performance. CONCLUSION Geriatric trauma patients treated at centers that manage a higher proportion of older patients have improved outcomes. This evidence supports the potential advantage of treating older trauma patients at centers specializing in geriatric trauma. LEVEL OF EVIDENCE Prognostic and epidemiologic study, level III.

Explaining the Paradoxical Age-based Racial Disparities in Survival After Trauma: The Role of the Treating Facility

BACKGROUND Currently, trauma center quality benchmarking is based on risk adjusted observed-expected (O/E) mortality ratios. However, failure to account for number of patients has been recently shown to produce unreliable mortality estimates, especially for low-volume centers. This study explores the effect of reliability adjustment (RA), a statistical technique developed to eliminate bias introduced by low volume on risk-adjusted trauma center benchmarking. METHODS Analysis of the National Trauma Data Bank 2010 was performed. Patients 16 years or older with blunt or penetrating trauma and an Injury Severity Score (ISS) of 9 or greater were included. Based on the statistically accepted standards of the Trauma Quality Improvement Program methodology, risk-adjusted mortality rates were generated for each center and used to rank them accordingly. Hierarchical logistic regression modeling was then performed to adjust these rates for reliability using an empiric Bayes approach. The impact of RA was examined by (1) recalculating interfacility variations in adjusted mortality rates and (2) comparing adjusted hospital mortality quintile rankings before and after RA. RESULTS A total of 557 facilities (with 278,558 patients) were included. RA significantly reduced the variation in risk-adjusted mortality rates between centers from 14-fold (0.7–9.8%) to only 2-fold (4.4–9.6%) after RA. This reduction in variation was most profound for smaller centers. A total of 68 “best” hospitals and 18 “worst” hospitals based on current risk adjustment methods were reclassified after performing RA. CONCLUSION “Reliability adjustment” dramatically reduces variations in risk-adjusted mortality arising from statistical noise, especially for lower volume centers. Moreover, the absence of RA had a profound impact on hospital performance assessment, suggesting that nearly one of every six hospitals in National Trauma Data Bank would have been inappropriately placed among the very best or very worst quintile of rankings. RA should be considered while benchmarking trauma centers based on mortality.