UC Davis

BACKGROUND: Surgical wound classification has been used in risk-adjustment models. However, it can be subjective and could potentially improperly bias hospital quality comparisons. The objective is to examine the effect of wound classification on hospital performance risk-adjustment models. STUDY DESIGN: Retrospective review of the 2011 American College of Surgeons NSQIP database was conducted for the following wound classification categories: clean, clean-contaminated, contaminated, and dirty-infected. To assess the influence of wound classification on risk adjustment, 2 models were developed for all outcomes: 1 including and 1 excluding wound classification. For each model, hospital postoperative complications were estimated using hierarchical multivariable regression methods. Absolute changes in hospital rank, correlations of odds ratios, and outlier status agreement between models were examined. RESULTS: Of the 442,149 cases performed in 315 hospitals: 53.6% were classified as clean; 34.2% as clean-contaminated; 6.7% as contaminated; and 5.5% as dirty-infected. The surgical site infection rate was highest in dirty-infected (8.5%) and lowest in clean (1.8%) cases. For overall surgical site infection, the absolute change in risk-adjusted hospital performance rank between models, including vs excluding wound classification, was minimal (mean 4.5 of 315 positions). The correlations between odds ratios of the 2 performance models were nearly perfect (R = 0.9976, p < 0.0001), and outlier status agreement was excellent (κ = 0.95ss08, p < 0.0001). Similar findings were observed in models of subgroups of surgical site infections and other postoperative outcomes. CONCLUSIONS: In circumstances where alternate information is available for risk adjustment, there appear to be minimal differences in performance models that include vs exclude wound classification. Therefore, the American College of Surgeons NSQIP is critically evaluating the continued use of wound classification in hospital performance risk-adjustment models.