ArticleJournal of medical Internet research2025
Machine Learning-Based Prediction of Early Complications Following Surgery for Intestinal Obstruction: Multicenter Retrospective Study.
Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Predictive models for intestinal obstruction: from clinical scores to artificial intelligence.Frontiers in surgery · 2026Review
- Enhancing privacy-preserving deployable large language models for perioperative complication detection: a targeted strategy with LoRA fine-tuning.NPJ digital medicine · 2025Article
- Development and validation of an interpretable shap-based machine learning model for predicting postoperative complications in laryngeal cancer.BMC surgery · 2025Article
- Global, regional, and national burden of disease analysis on paralytic ileus and intestinal obstruction in adults aged 65 and over from 1990 to 2021, with projections for 2030: a Global Burden of Disease Study 2021 analysis.BMC gastroenterology · 2025Article
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9 authors.
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Abstract
backgroundEarly complications increase in-hospital stay and mortality after intestinal obstruction surgery. It is important to identify the risk of postoperative early complications for patients with intestinal obstruction at a sufficiently early stage, which would allow preemptive individualized enhanced therapy to be conducted to improve the prognosis of patients with intestinal obstruction. A risk predictive model based on machine learning is helpful for early diagnosis and timely intervention.
objectiveThis study aimed to construct an online risk calculator for early postoperative complications in patients after intestinal obstruction surgery based on machine learning algorithms.
methodsA total of 396 patients undergoing intestinal obstruction surgery from April 2013 to April 2021 at an independent medical center were enrolled as the training cohort. Overall, 7 machine learning methods were used to establish prediction models, with their performance appraised via the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, and F
resultsThe incidence of postoperative complications in the training cohort was 47.44% (176/371), while the incidences in 4 external validation cohorts were 34% (17/50), 56.06% (37/66), 52.08% (25/48), and 48.17% (79/164), respectively. Postoperative complications were associated with 8-item features: Physiological Severity Score for the Enumeration of Mortality and Morbidity (POSSUM physiological score), the amount of colloid infusion, shock index before anesthesia induction, ASA (American Society of Anesthesiologists) classification, the percentage of neutrophils, shock index at the end of surgery, age, and total protein. The random forest model showed the best overall performance, with an AUROC of 0.788 (95% CI 0.709-0.869), accuracy of 0.756, sensitivity of 0.695, specificity of 0.810, and F
conclusionsWe have developed and validated a generalizable random forest model to predict postoperative early complications in patients undergoing intestinal obstruction surgery, enabling clinicians to screen high-risk patients and implement early individualized interventions. An online risk calculator for early postoperative complications was developed to make the random forest model accessible to clinicians around the world.
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