The changing paradigm in the treatment of rectal SCC holds great promise for improved outcomes in this rare disease.

Background: Colorectal surgery carries a significant mortality risk, with reported rates of 1-6% for elective surgery and up to 22% in the emergency setting. Both clinicians and patients will benefit from being able to predict the likelihood of death before surgery. Recently, we have described and validated two risk stratification models for colorectal surgery, the Barwon Health 2012 and Association Française de Chirurgie models. However, these models are not suitable for assessment at patient's bedside. The purpose of this study is to develop a simplified preoperative model capable of predicting mortality following colorectal surgery. Methods:The new model is termed Colorectal preOperative Surgical Score (CrOSS). The development and internal validation of CrOSS was performed using a prospectively maintained colorectal database. External validation was performed using retrospective data. Univariate and multivariate analyses were performed in model development. Calibration and discrimination were used for model validation. Results: There were 474 and 389 consecutive colorectal surgeries at Geelong Hospital and Western Hospital. Overall mortality rates were 5.16% and 1.03%, respectively. Significant predictors for mortality were as follows: age ≥70, urgent operation, albumin ≤30 g/L and congestive heart failure (receiver operating characteristic (ROC) = 0.870, calibration P-value = 0.937). The predicted risk of mortality was stratified according to the risk profile of 0.39-66.51%. When validated externally, CrOSS predicted mortality accurately (ROC = 0.847, calibration P-value = 0.199). Conclusions: A robust and simple preoperative model has been created to riskstratify patients for colorectal surgery. This was successfully validated at another tertiary hospital.

We have developed a model that can accurately predict mortality after major colorectal surgery by using only data that are available preoperatively. After recalibration, the model was successfully validated in a second hospital.