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.