Ranking Trauma Center Quality

Abstract

To explore whether trauma center quality metrics based on historical data can reliably predict future trauma center performance. 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. 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. 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. 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.