Machine Learning-based Model for Predicting Postoperative Complications among Patients with Colonic Perforation: A Retrospective study

Objectives: Surgery for colonic perforation has high morbidity and mortality rates. Predicting complications preoperatively would help improve short-term outcomes; however, no predictive risk stratification model exists to date. Therefore, the current study aimed to determine risk factors for complications after colonic perforation surgery and use machine learning to construct a predictive model. Methods: This retrospective study included 51 patients who underwent emergency surgery for colorectal perforation. We investigated the connection between overall complications and several preoperative indicators, such as lactate and the Glasgow Prognostic Score. Moreover, we used the classification and regression tree (CART), a machine-learning method, to establish an optimal prediction model for complications. Results: Overall complications occurred in 32 patients (62.7%). Multivariate logistic regression analysis identified high lactate levels [odds ratio (OR), 1.86; 95% confidence interval (CI), 1.07-3.22; p = 0.027] and hypoalbuminemia (OR, 2.56; 95% CI, 1.06-6.25; p = 0.036) as predictors of overall complications. According to the CART analysis, the albumin level was the most important parameter, followed by the lactate level. This prediction model had an area under the curve (AUC) of 0.830. Conclusions: Our results determined that both preoperative albumin and lactate levels were valuable predictors of postoperative complications among patients who underwent colonic perforation surgery. The CART analysis determined optimal cutoff levels with high AUC values to predict complications, making both indicators clinically easier to use for decision making.