• Victor W Fazio, Paris P Tekkis, Feza Remzi, and Ian C Lavery.
• Department of Colorectal Surgery, Cleveland Clinic Foundation, Cleveland, Ohio 44195, USA. ptekkis@blueyonder.co.uk
• Dis. Colon Rectum. 2004 Dec 1;47(12):2015-24.

IntroductionPredictive models play a pivotal role in the provision of risk-adjusted, operative mortality rates. The purpose of the study was to describe the development of a dedicated prognostic index for quantifying operative risk in colorectal cancer surgery.MethodsData were collected from 5,034 consecutive patients undergoing major surgery in a single center from October 1976 to July 2002. Primary end point was 30-day operative mortality. A multilevel Bayesian logistic regression model was developed to adjust for case-mix and accommodate the variability of outcomes between surgeons. The model was internally validated (split-sample) and tested using measures of discrimination, calibration, and subgroup analysis.ResultsThe patients' median age was 66 (range, 18-98) years. Operative mortality was 2.3 percent with no significant variability between surgeons or through time. Multivariate analysis identified the following independent risk factors: age (odds ratio = 1.5 per 10-year increase), American Society of Anesthesiologists grade (odds ratio for ASA II, III, IV-V vs. I = 2.6, 4.3, 6.8), TNM staging (odds ratio for Stage IV vs. I-III = 2.6), mode of surgery (odds ratio for urgent vs. nonurgent = 2.1) no-cancer resection vs. cancer resection (odds ratio = 4.5), and hematocrit level. The model offered adequate discrimination (area under receiver operator characteristic curve = 0.801) and excellent agreement between observed and model-predicted outcomes over ten major colorectal procedures (P = 0.191).ConclusionsThe colorectal cancer model provided an accurate means of estimating risk for individual patients in the preoperative setting. It has important implications in everyday practice, because it may be used as an adjunct in the process of informed consent and for monitoring surgical performance through time.

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