Risk Adjustment as Basis for Rational Benchmarking: The Example of Colon Carcinoma

This is the authors' abstract. We don't add key points for this paper.

BACKGROUND: The results of resection of colorectal carcinoma can vary greatly from one hospital to another. However, this does not necessarily reflect differences in the quality of treatment. The purpose of this study was to compare various tools for the risk-adjusted assessment of treatment results after resection of colorectal carcinoma within the context of hospital benchmarking. METHODS: On the basis of a data pool provided by a multicentric observation study of patients with colon cancer, the postoperative in-hospital mortality rates at two high-volume hospitals ("A" and "B") were compared. After univariate comparison, risk-adjusted comparison of postoperative mortality was performed by logistic regression analysis (LReA), propensity-score analysis (PScA), and the CR-POSSUM score. Postoperative complications were compared by LReA and PScA. RESULTS: Although postoperative mortality differed significantly (P = 0.041) in univariate comparison of hospitals A and B (2.9% vs. 6.4%), no significant difference was found by LReA or PScA. Similarly, the observed mortality at these did not differ significantly from the mortality estimated by the CR-POSSUM score (hospital A, 2.9%/4.9%, P = 0.298; hospital B, 6.4%/6.5%, P = 1.000). Significant differences were seen in risk-adjusted comparison of most postoperative complications (by both LReA and PScA), but there were no differences in the rates of relaparotomy or anastomotic leakage that required surgery. CONCLUSIONS: For the hard outcome variable "postoperative mortality," none of the three risk adjustment procedures showed any difference between the hospitals. The CR-POSSUM score can be regarded as the most practicable tool for risk-adjusted comparison of the outcome of colon-carcinoma resection in clinical benchmarking.

Risk adjustment eliminates apparent mortality differences; supports routine adjusted benchmarking in observational surgical data but leaves optimal methods open.