Machine learning prediction model for postoperative ileus following colorectal surgery

• Published in: ANZ journal of surgery Vol. 94; no. 7-8; pp. 1292 - 1298
• Main Authors: Traeger, Luke, Bedrikovetski, Sergei, Hanna, Jessica E., Moore, James W., Sammour, Tarik
• Format: Journal Article
• Language: English
• Published: Melbourne John Wiley & Sons Australia, Ltd 01.07.2024; Blackwell Publishing Ltd
• Subjects: Aged; colorectal; Colorectal surgery; Colorectal Surgery - adverse effects; Decision trees; Female; Humans; ileus; Ileus - diagnosis; Ileus - epidemiology; Ileus - etiology; Incidence; Learning algorithms; Logistic Models; Machine Learning; Male; Middle Aged; Morbidity; Multilayer perceptrons; Multivariate analysis; Postoperative Complications - diagnosis; Postoperative Complications - epidemiology; Postoperative Complications - etiology; prediction; Prediction models; Radial basis function; Regression analysis; Retrospective Studies; Risk Factors; ROC Curve; Sarcopenia; Surgery; prediction; ileus; machine learning; colorectal
• ISSN: 1445-1433; 1445-2197; 1445-2197
• DOI: 10.1111/ans.19020

Background Postoperative ileus (POI) continues to be a major cause of morbidity following colorectal surgery. Despite best efforts, the incidence of POI in colorectal surgery remains high (~30%). This study aimed to investigate machine learning techniques to identify risk factors for POI in colorectal surgery patients, to help guide further preventative strategies. Methods A TRIPOD‐guideline‐compliant retrospective study was conducted for major colorectal surgery patients at a single tertial care centre (2018–2022). The primary outcome was the occurrence of POI, defined as not achieving GI‐2 (outcome measure of time to first stool and tolerance of oral diet) by day four. Multivariate logistic regression, decision trees, radial basis function and multilayer perceptron (MLP) models were trained using a random allocation of patients to training/testing data sets (80/20). The area under the receiver operating characteristic (AUROC) curves were used to evaluate model performance. Results Of 504 colorectal surgery patients, 183 (36%) experienced POI. Multivariate logistic regression, decision trees, radial basis function and MLP models returned an AUROC of 0.722, 0.706, 0.712 and 0.800, respectively. The MLP model had the highest sensitivity and specificity values. In addition to well‐known risk factors for POI, such as postoperative hypokalaemia, surgical approach, and opioid use, the MLP model identified sarcopenia (ranked 4/30) as a potentially modifiable risk factor for POI. Conclusion MLP outperformed other models in predicting POI. Machine learning can provide valuable insights into the importance and ranking of specific predictive variables for POI. Further research into the predictive value of preoperative sarcopenia for POI is required. Postoperative ileus poses a considerable challenge to the well‐being of colorectal surgical patients. In this article, we delve into the application of advanced machine‐learning techniques to develop a more effective prediction model for postoperative ileus compared to traditional statistical methods.