In Diseases of the colon and rectum
BACKGROUND : Surgical site infection is a source of significant morbidity after colorectal surgery. Previous efforts to develop models that predict surgical site infection have had limited accuracy. Machine learning has shown promise in predicting post-operative outcomes by identifying non-linear patterns within large datasets.
OBJECTIVE : We sought to use machine learning to develop a more accurate predictive model for colorectal surgical site infections.
DESIGN : Patients who underwent colorectal surgery were identified in the American College of Surgeons National Quality Improvement Program database from years 2012-2019 and were split into training, validation, and test sets. Machine learning techniques included random forest, gradient boosting, and artificial neural network. A logistic regression model was also created. Model performance was assessed using area under the receiver operating characteristic curve.
SETTINGS : A national, multicenter dataset.
PATIENTS : Patients who underwent colorectal surgery.
MAIN OUTCOME MEASURES : The primary outcome (surgical site infection) included patients who experienced superficial, deep, or organ-space surgical site infections.
RESULTS : The dataset included 275,152 patients after application of exclusion criteria. 10.7% of patients experienced a surgical site infection. Artificial neural network showed the best performance with area under the receiver operating characteristic curve of 0.769 (95% CI 0.762 - 0.777), compared with 0.766 (95% CI 0.759 - 0.774) for gradient boosting, 0.764 (95% CI 0.756 - 0.772) for random forest, and 0.677 (95% CI 0.669 - 0.685) for logistic regression. For the artificial neural network model, the strongest predictors of surgical site infection were organ-space surgical site infection present at time of surgery, operative time, oral antibiotic bowel prep, and surgical approach.
LIMITATIONS : Local institutional validation was not performed.
CONCLUSIONS : Machine learning techniques predict colorectal surgical site infections with higher accuracy than logistic regression. These techniques may be used to identify patients at increased risk and to target preventative interventions for surgical site infection. See Video Abstract at http://links.lww.com/DCR/C88.
Chen Kevin A, Joisa Chinmaya U, Stem Jonathan, Guillem Jose G, Gomez Shawn M, Kapadia Muneera R
2022-Nov-30
