Machine learning-based prediction of postoperative mortality in emergency colorectal surgery: A retrospective, multicenter cohort study using Tokushukai medical database

Background: Although prognostic factors associated with mortality in patients with emergency colorectal surgery have been identified, an accurate mortality risk assessment is still necessary to determine the range of therapeutic resources in accordance with the severity of patients. We established machine-learning models to predict in-hospital mortality for patients who had emergency colorectal surgery using clinical data at admission and attempted to identify prognostic factors associated with in-hospital mortality. Methods: This retrospective cohort study included adult patients undergoing emergency colorectal surgery in 42 hospitals between 2012 and 2020. We employed logistic regression and three supervised machine-learning models: random forests, gradient-boosting decision trees (GBDT), and multilayer perceptron (MLP). The area under the receiver operating characteristics curve (AUROC) was calculated for each model. The Shapley additive explanations (SHAP) values are also calculated to identify the significant variables in GBDT. Results: There were 8792 patients who underwent emergency colorectal surgery. As a result, the AUROC values of 0.742, 0.782, 0.814, and 0.768 were obtained for logistic regression, random forests, GBDT, and MLP. According to SHAP values, age, colorectal cancer, use of laparoscopy, and some laboratory variables, including serum lactate dehydrogenase serum albumin, and blood urea nitrogen, were significantly associated with in-hospital mortality. Conclusion: We successfully generated a machine-learning prediction model, including GBDT, with the best prediction performance and exploited the potential for use in evaluating in-hospital mortality risk for patients who undergo emergency colorectal surgery

A rare case of an infected tracheal diverticulum requiring emergency intervention: A case report

AbstractIntroductionRecent advancement in radiological imaging has revealed an increasing amount of asymptomatic abnormalities. Tracheal diverticula are relatively rare entities and are incidentally found on radiological imaging such as computed tomography. Here, we present a case of an infected tracheal diverticulum presenting as a paratracheal mass, which required emergency intervention.Case presentationA 65-year-old Japanese nonsmoker man presented with a fever, lower neck pain, and the aggravation of dyspnea for a week. An enhanced computed tomography scan demonstrated that the trachea was displaced by a paratracheal mass with a well-defined thin wall. His respiratory status was so urgent that emergency intubation and surgical drainage of the abscess were performed. A computed tomography scan performed 4days after admission demonstrated shrinking of the abscess, and he was extubated and discharged 7days after admission without any complications.ConclusionTo the best of our knowledge, this is the first report to confirm an infected tracheal diverticulum presenting as a paratracheal abscess, which required emergency intervention. Moreover, computed tomography plays an important role in the differentiation of paratracheal masses