ArticleAnnals of medicine2025
Evaluation of model performance in predicting sepsis after intestinal obstruction surgery: a multicenter retrospective study.
Article in Annals of medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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10 authors.
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Abstract
purposeIntestinal obstruction surgery is a high-risk procedure associated with postoperative sepsis. In this multicenter retrospective study, we aimed to employ machine-learning methods to predict sepsis after intestinal obstruction surgery and visualize its driving mechanism to assist clinical decision-making.
methodsThe study included patients who underwent surgery for intestinal obstruction at three medical centers between 2013 and 2021, as well as patients with intestinal obstruction from the publicly available perioperative dataset INSPIRE. Various algorithms, including logistic regression, support vector classification (SVC), the Gaussian Bayesian classifier, gradient boosting machine, random forest, adaptive boosting, and multilayer perceptron, were tested. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), and the SHapley Additive exPlanation (SHAP) values were utilized for model interpretation.
resultsPostoperative sepsis was observed in 60 patients (16.17%), 10 patients (20%), and 10 patients (15.15%) in three medical centers, as well as 7 patients (12.73%) in INSPIRE dataset. In the internal validation set, the SVC algorithm demonstrated favorable performance, with an AUC value of 0.876 (95%CI 0.799-0.939). The model was well-calibrated, with a Brier score of 0.108 and a non-significant Hosmer-Lemeshow test (
conclusionWe have developed a highly interoperable sepsis risk calculator based on the SVC model to assist postoperative clinical decision-making in patients with intestinal obstruction.
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