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.

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.

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.

Variation of blood transfusion in patients undergoing major noncardiac surgery.

OBJECTIVE: To examine the hospital variability in use of red blood cells (RBCs), fresh-frozen plasma (FFP), and platelet transfusions in patients undergoing major noncardiac surgery. BACKGROUND: Blood transfusion is commonly used in surgical procedures in the United States. Little is known about the hospital variability in perioperative transfusion rates for noncardiac surgery. METHODS: We used the University HealthSystem Consortium database (2006-2010) to examine hospital variability in use of allogeneic RBC, FFP, and platelet transfusions in patients undergoing major noncardiac surgery. We used regression-based techniques to quantify the variability in hospital transfusion practices and to study the association between hospital characteristics and the likelihood of transfusion. RESULTS: After adjusting for patient risk factors, hospital transfusion rates varied widely for patients undergoing total hip replacement (THR), colectomy, and pancreaticoduodenectomy. Compared with patients undergoing THR in average-transfusion hospitals, patients treated in high-transfusion hospitals have a greater than twofold higher odds of being transfused with RBCs [adjusted odds ratio (AOR) = 2.41; 95% confidence interval (CI), 1.89-3.09], FFP (AOR = 2.81; 95% CI, 2.02-3.91), and platelets (AOR = 2.52; 95% CI, 1.95-3.25), whereas patients in low-transfusion hospitals have an approximately 50% lower odds of receiving RBCs (AOR = 0.45; 95% CI, 0.35-0.57), FFP (AOR = 0.37; 95% CI, 0.27-0.51), and platelets (AOR = 0.42; 95% CI, 0.29-0.62). Similar results were obtained for colectomy and pancreaticoduodenectomy. CONCLUSIONS: There was dramatic hospital variability in perioperative transfusion rates among patients undergoing major noncardiac surgery at academic medical centers. In light of the potential complications of transfusion therapy, reducing this variability in hospital transfusion practices may result in improved surgical outcomes.

The Surgical Mortality Probability Model: derivation and validation of a simple risk prediction rule for noncardiac surgery.

OBJECTIVE: To develop a 30-day mortality risk index for noncardiac surgery that can be used to communicate risk information to patients and guide clinical management at the "point-of-care," and that can be used by surgeons and hospitals to internally audit their quality of care. BACKGROUND: Clinicians rely on the Revised Cardiac Risk Index to quantify the risk of cardiac complications in patients undergoing noncardiac surgery. Because mortality from noncardiac causes accounts for many perioperative deaths, there is also a need for a simple bedside risk index to predict 30-day all-cause mortality after noncardiac surgery. METHODS: Retrospective cohort study of 298,772 patients undergoing noncardiac surgery during 2005 to 2007 using the American College of Surgeons National Surgical Quality Improvement Program database. RESULTS: The 9-point S-MPM (Surgical Mortality Probability Model) 30-day mortality risk index was derived empirically and includes three risk factors: ASA (American Society of Anesthesiologists) physical status, emergency status, and surgery risk class. Patients with ASA physical status I, II, III, IV or V were assigned either 0, 2, 4, 5, or 6 points, respectively; intermediate- or high-risk procedures were assigned 1 or 2 points, respectively; and emergency procedures were assigned 1 point. Patients with risk scores less than 5 had a predicted risk of mortality less than 0.50%, whereas patients with a risk score of 5 to 6 had a risk of mortality between 1.5% and 4.0%. Patients with a risk score greater than 6 had risk of mortality more than 10%. S-MPM exhibited excellent discrimination (C statistic, 0.897) and acceptable calibration (Hosmer-Lemeshow statistic 13.0, P = 0.023) in the validation data set. CONCLUSIONS: Thirty-day mortality after noncardiac surgery can be accurately predicted using a simple and accurate risk score based on information readily available at the bedside. This risk index may play a useful role in facilitating shared decision making, developing and implementing risk-reduction strategies, and guiding quality improvement efforts.

The association between nurse staffing and hospital outcomes in injured patients.

BACKGROUND: The enormous fiscal pressures facing trauma centers may lead trauma centers to reduce nurse staffing and to make increased use of less expensive and less skilled personnel. The impact of nurse staffing and skill mix on trauma outcomes has not been previously reported. The goal of this study was to examine whether nurse staffing levels and nursing skill mix are associated with trauma patient outcomes. METHODS: We used data from the Healthcare Cost and Utilization Project Nationwide Inpatient Sample to perform a cross-sectional study of 70,142 patients admitted to 77 Level I and Level II centers. Logistic regression models were used to examine the association between nurse staffing measures and (1) mortality, (2) healthcare associated infections (HAI), and (3) failure-to-rescue. We controlled for patient risk factors (age, gender, injury severity, mechanism of injury, comorbidities) and hospital structural characteristics (trauma center status - Level I versus Level II, hospital size, ownership, teaching status, technology level, and geographic region). RESULTS: A 1% increase in the ratio of licensed practical nurse (LPN) to total nursing time was associated with a 4% increase in the odds of mortality (adj OR 1.04; 95% CI: 1.02-1.06; p = 0.001) and a 6% increase in the odds of sepsis (adj OR 1.06: 1.03-1.10; p < 0.001). Hospitals in the highest quartile of LPN staffing had 3 excess deaths (95% CI: 1.2, 5.1) and 5 more episodes of sepsis (95% CI: 2.3, 7.6) per 1000 patients compared to hospitals in the lower quartile of LPN staffing. CONCLUSIONS: Higher hospital LPN staffing levels are independently associated with slightly higher rates of mortality and sepsis in trauma patients admitted to Level I or Level II trauma centers.

Association between intraoperative blood transfusion and mortality and morbidity in patients undergoing noncardiac surgery.

BACKGROUND: The impact of intraoperative erythrocyte transfusion on outcomes of anemic patients undergoing noncardiac surgery has not been well characterized. The objective of this study was to examine the association between blood transfusion and mortality and morbidity in patients with severe anemia (hematocrit less than 30%) who are exposed to one or two units of erythrocytes intraoperatively. METHODS: This was a retrospective analysis of the association of blood transfusion and 30-day mortality and 30-day morbidity in 10,100 patients undergoing general, vascular, or orthopedic surgery. We estimated separate multivariate logistic regression models for 30-day mortality and for 30-day complications. RESULTS: Intraoperative blood transfusion was associated with an increased risk of death (odds ratio [OR], 1.29; 95% CI, 1.03-1.62). Patients receiving an intraoperative transfusion were more likely to have pulmonary, septic, wound, or thromboembolic complications, compared with patients not receiving an intraoperative transfusion. Compared with patients who were not transfused, patients receiving one or two units of erythrocytes were more likely to have pulmonary complications (OR, 1.76; 95% CI, 1.48-2.09), sepsis (OR, 1.43; 95% CI, 1.21-1.68), thromboembolic complications (OR, 1.77; 95% CI, 1.32-2.38), and wound complications (OR, 1.87; 95% CI, 1.47-2.37). CONCLUSIONS: Intraoperative blood transfusion is associated with a higher risk of mortality and morbidity in surgical patients with severe anemia. It is unknown whether this association is due to the adverse effects of blood transfusion or is, instead, the result of increased blood loss in the patients receiving blood.

Resource commitment to improve outcomes and increase value at a level I trauma center.

BACKGROUND: Optimal care of trauma patients requires cost-effective organization and commitment of trauma center resources. We examined the impact of creating a dedicated trauma care unit (TCU) and adding advanced practice nurses on the quality and cost of care at an adult Level I trauma center. METHODS: Patient demographic and injury data, length of stay, complications, outcomes, and total direct cost of care were evaluated for four 1-year intervals in the recent history of our trauma center: Year A, a trauma team of in-house trauma surgeons and resident physicians; Year B, the addition of nurse practitioners to the trauma team 5 days/week; Year C, the creation of a dedicated TCU for all non intensive care unit trauma patients; and Year D, the addition of a permanent clinical nurse specialist and an increase in nurse practitioner coverage to 7 days/week. For each year, value was determined by calculating the median cost of a survivor and the median cost of a survivor with no complications. Significance was attributed to p<0.05. RESULTS: Patient volume increased from 1,927 in year A to 2,546 by year D. Over the period of study, there was an increase in blunt trauma (87.1-89.9%; p<0.05), median Injury Severity Score (5-6; p<0.05), and patients aged ≥65 years (11.4-19.8%; p<0.05). However, risk-adjusted mortality was unchanged. There was a decrease in patients with a complication (20.8-14.9%; p < 0.05), median intensive care unit length of stay (39.5-23.4 hours; p < 0.05), and median cost of care ($4,306-$3,698; p<0.05). Value increased: both the median costs of a survivor and of a survivor with no complications decreased from $4,259 to $3,658 (p<0.05) and from $3,898 to $3,317 (p<0.05), respectively. The median cost of a survivor with severe injury (Injury Severity Score ≥15) decreased from $17,651 to $12,285 (p<0.05). CONCLUSION: The addition of a dedicated TCU and advanced practice nurses improved the quality and reduced the cost of care, resulting in increased value at an adult Level I trauma center.

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.

The association between cost and quality in trauma: is greater spending associated with higher-quality care?

OBJECTIVE: To examine the association between trauma center quality and costs. BACKGROUND: Current efforts to reduce health care costs and improve health care quality require a better understanding of the relationship between cost and quality. METHODS: Using data from the Healthcare Cost and Utilization Projects Nationwide Inpatient Sample, we performed a retrospective observational study of 67,124 trauma patients admitted to 73 trauma centers. Generalized linear models were used to explore the association between hospital cost and in-hospital mortality, controlling for hospital and patient factors as follows: injury diagnoses, age, gender, mechanism of injury, comorbidities, teaching status, hospital ownership, geographic region, and hospital wages. RESULTS: Patients treated in hospitals with low risk-adjusted mortality rates had significantly lower costs than those treated in average-quality hospitals. The relative cost of patients treated in high-quality hospitals was 0.78 (95% confidence interval: 0.64, 0.95) compared with average-quality hospitals. The cost of treating patients in average- and high-mortality trauma centers was similar. CONCLUSION: In this study based on the Healthcare Cost and Utilization Project Nationwide Inpatient Sample, the care of injured patients is less expensive in hospitals with lower risk-adjusted mortality rates. Hospitals with low risk-adjusted mortality rates have adjusted mortality rates that are 34% lower while spending nearly 22% less compared with average-quality hospitals.

The effect of preexisting conditions on hospital quality measurement for injured patients.

OBJECTIVE: To determine whether adjusting for comorbidities significantly affects hospital quality measurement compared with adjusting for injury severity alone. BACKGROUND: Pre-existing conditions have a significant impact on mortality after injury. The impact of including comorbidities on hospital quality measurement is not well understood. METHODS: Retrospective cohort study using the Healthcare Cost and Utilization Project Nationwide Inpatient Sample (2005-2006). The Trauma Mortality Probability Model (TMPM-ICD9) was re-estimated with and without the addition of the comorbidity measures in the Agency for Health Research and Quality comorbidity algorithm. Hospital quality was measured using an adjusted odds ratio (OR) obtained using hierarchical logistic regression modeling. The OR quantifies the likelihood that trauma patients treated at a specific hospital are more or less likely to die compared with patients treated at an average hospital. Hospitals with an adjusted OR significantly greater than, or less than 1 were classified as low-quality or high-quality outliers, respectively. Pairwise comparison of hospital quality based on TMPM-ICD9 with and without comorbidity information were performed using the intraclass correlation coefficient, the Spearman correlation coefficient, the Bland-Altman Plot, and the kappa statistic. RESULTS: There was nearly perfect agreement between hospital ranking based on TMPM-ICD9 and TMPM-ICD9 with comorbidities. The intraclass correlation coefficient was 0.943 (95% CI, 0.931-0.951), the Spearman correlation coefficient was 0.953 (95% CI, 0.944-0.960), and the kappa statistic was 0.863 (95% CI, 0.792-0.934). The odds of a patient dying in the worst 5% hospitals was 1.73 (95% CI, 1.61-1.86), whereas the odds of a patient dying in the best 5% of the hospitals was 0.37 (95% CI, 0.31-0.44). CONCLUSION: In this large study of 148,280 trauma patients in 511 hospitals, we found no evidence that adding comorbidites to the risk-adjustment model used to benchmark hospital performance changes hospital ranking. In addition, there appears to be significant variability in mortality outcomes between the best and worst performing hospitals. This difference in outcomes across hospitals may represent a significant opportunity to improve health outcomes for injured patients.

How well do hospital mortality rates reported in the New York State CABG report card predict subsequent hospital performance?

BACKGROUND: The use of mortality report cards as the basis for hospital choice assumes that a hospital's current performance is predicted by its past performance. OBJECTIVE: To assess the accuracy of hospital risk-adjusted mortality rates reported in the New York State (NYS) coronary artery bypass graft (CABG) report card for predicting subsequent hospital mortality. METHODS: We performed a retrospective study based on hospital mortality measures for CABG surgery (n = 37 hospitals) in NYS, which are publicly reported by the NYS Department of Health. Feasible generalized least squares was used to examine the association between a hospital's past quality ranking (high-quality, intermediate-quality, low-quality) and its subsequent performance, as measured using the ratio of the observed-to-expected mortality rate (O-to-E ratio). RESULTS: Hospitals identified as low-mortality hospitals using 2-year-old data had subsequent O-to-E ratios that were 16.8% lower (95% confidence interval, 8.9-24.8; P < 0.001) than average-mortality hospitals, whereas hospitals identified as high-mortality hospitals had subsequent O-to-E ratios that were 31.8% higher (95% confidence interval, 3.69-59.9; P < 0.05) compared with average-mortality hospitals. Hospitals identified as high-mortality hospitals using 3-year-old data were indistinguishable from average-mortality hospitals. CONCLUSION: Hospital ranking based on 2-year-old data is a strong predictor of future performance. Report cards based on 3-year-old data may not be useful for identifying low-performance hospitals. We recommend that the CABG report cards in NYS should be based on 2-year-old data, as opposed to the current practice of basing them on either 2- or 3-year-old data.

Measuring quality for public reporting of health provider quality: making it meaningful to patients.

Public quality reports of hospitals, health plans, and physicians are being used to promote efficiency and quality in the health care system. Shrinkage estimators have been proposed as superior measures of quality to be used in these reports because they offer more conservative and stable quality ranking of providers than traditional, nonshrinkage estimators. Adopting the perspective of a patient faced with choosing a local provider on the basis of publicly provided information, we examine the advantages and disadvantages of shrinkage and nonshrinkage estimators and contrast the information made available by them. We demonstrate that 2 properties of shrinkage estimators make them less useful than nonshrinkage estimators for patients making choices in their area of residence.

The Survival Measurement and Reporting Trial for Trauma (SMARTT): background and study design.

BACKGROUND: This report describes a project funded by the Agency for Healthcare Research and Quality to evaluate the impact of providing hospitals with nonpublic report cards on trauma outcomes. The Survival Measurement and Reporting Trial for Trauma explores the feasibility of using the National Trauma Data Bank as a platform for measuring and improving trauma outcomes. METHODS: We identified a cohort of 125 hospitals in the National Trauma Data Bank with annual hospital volumes of 250 or more trauma cases meeting specific minimum criteria for data quality. The performance of hospitals in this cohort was evaluated using hierarchical logistic regression model. The effect of each hospital on trauma mortality was captured by a shrinkage coefficient, which is exponentiated to yield an adjusted odds ratio. This adjusted odds ratio represents the likelihood that a trauma patient treated at a specific hospital is more or less likely to die compared with a patient treated at an "average" hospital. RESULTS: The initial hospital cohort includes 125 hospitals and 157,045 patients admitted in 2006. Most hospitals are either level I (36%) or level II (34%) trauma centers. Patients admitted to the worst-performing hospitals were at least 50% more likely to die than patients admitted to the average hospital, after adjusting for injury severity. CONCLUSION: The initial findings of this trial suggest that there is significant variability in trauma mortality across centers caring for injured patients after adjusting for differences in patient casemix. This variation in risk-adjusted mortality presents an opportunity for improvement. The Survival Measurement and Reporting Trial for Trauma study is designed to test the hypothesis that nonpublic report cards can lead to improved population mortality for injured patients. The results of this study may have substantial implications in the future design and implementation of a national effort to report and improve trauma outcomes in the United States.

Impact of statistical approaches for handling missing data on trauma center quality.

OBJECTIVE: To determine whether imputed data can be used to produce unbiased hospital quality measures. BACKGROUND: Different methods for handling missing data may influence which hospitals are designated as quality outliers. METHODS: Monte-Carlo simulation study based on 63,020 patients with no missing data in 68 hospitals using the National Trauma Data Bank (NTDB, version 6.1). Patients were assigned missing data for the motor component of the Glasgow coma scale (GCS) conditional on their observed clinical risk factors. Multiple imputation was then used to "fill in" the missing data. Hospital risk-adjusted quality measures (observed-to-expected mortality ratio) based either on (1) imputed data, (2) excluding patients with missing data (complete case analysis), or (3) excluding the predictor with missing data were compared with hospital quality based on the true data (no missing data). Pair-wise comparisons of hospital quality were performed using the intraclass correlation coefficient (ICC) and the kappa statistic. RESULTS: With 10% of the data missing, the level of agreement between multiple imputation and the true data (ICC = 0.99 and kappa = 0.87) was better compared with the level of agreement between complete case analysis and the true data (ICC = 0.93 and kappa = 0.62). Excluding the predictor (motor GCS) with missing data from the risk adjustment model resulted in the least amount of agreement with quality assessment based on the true data (ICC = 0.88 and kappa = 0.46). CONCLUSION: Multiple imputation can be used to impute missing data and yields hospital quality measures that are nearly identical to those based on the true data. Simply excluding patients with missing data or excluding risk factors with missing data from hospital quality assessment yields substantially inferior quality measures.