Prediction of symptomatic anastomotic leak after rectal cancer surgery: A machine learning approach

INTRODUCTION: Anastomotic leakage (AL) remains the most dreaded and unpredictable major complication after low anterior resection for mid-low rectal cancer. The aim of this study is to identify patients with high risk for AL based on the machine learning method. METHODS: Patients with mid-low rectal cancer undergoing low anterior resection were enrolled from West China Hospital between January 2008 and October 2019 and were split by time into training cohort and validation cohort. The least absolute shrinkage and selection operator (LASSO) method and stepwise method were applied for variable selection and predictive model building in the training cohort. The area under the receiver operating characteristic curve (AUC) and calibration curves were used to evaluate the performance of the models. RESULTS: The rate of AL was 5.8% (38/652) and 7.2% (15/208) in the training cohort and validation cohort, respectively. The LASSO-logistic model selected almost the same variables (hypertension, operating time, cT4, tumor location, intraoperative blood loss) compared to the stepwise logistic model except for tumor size (the LASSO-logistic model) and American Society of Anesthesiologists score (the stepwise logistic model). The predictive performance of the LASSO-logistics model was better than the stepwise-logistics model (AUC: 0.790 vs. 0.759). Calibration curves showed mean absolute error of 0.006 and 0.013 for the LASSO-logistics model and stepwise-logistics model, respectively. CONCLUSION: Our study developed a feasible predictive model with a machine-learning algorithm to classify patients with a high risk of AL, which would assist surgical decision-making and reduce unnecessary stoma diversion. The involved machine learning algorithms provide clinicians with an innovative alternative to enhance clinical management.