ArticleSurgical endoscopy2024
Machine learning-based preoperative analytics for the prediction of anastomotic leakage in colorectal surgery: a swiss pilot study.
Article in Surgical endoscopy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Machine learning model-guided selective use of temporary diverting ileostomy in rectal cancer surgery: a randomized controlled trial.Nature communications · 2026Trial
- Review
- Bringing AI to the OR: integrating a machine learning predictive model in the EHR - a pilot on user-friendliness.BMJ health & care informatics · 2026Article
- Multimodal biomarker panel for early prediction of anastomotic leak after colorectal surgery: from inflammation to ischemia.Frontiers in surgery · 2026Review
- Artificial intelligence in the surgical management for colorectal cancer: current applications and future directions.Frontiers in oncology · 2026Review
- Postoperative enteral nutrition timing and risk variation in elderly patients with perforated peptic ulcer.Frontiers in nutrition · 2026Article
- Artificial intelligence and computational prediction models for risk stratification, treatment response, and outcomes in colorectal cancer: a narrative review.Frontiers in oncology · 2026Review
- Mechanomedicine in digestive surgery: a theranostic framework integrating mechanical diagnostics and therapeutic modulation across the perioperative continuum.Theranostics · 2026Review
- Development of a Clinical Prediction Model for Anastomotic Leakage in Colorectal Surgery.JAMA network open · 2025Observational
- Ethical Pitfalls in AI-Based Predictive Models in Surgery.World journal of surgery · 2025Review
- Application and challenges of artificial intelligence in predicting perioperative complications of colorectal cancer.World journal of gastrointestinal surgery · 2025Article
- Diagnostic Value of Postsurgical Endoscopy.Visceral medicine · 2025Review
- Patient perspectives on AI-based decision support in surgery.BMJ surgery, interventions, & health technologies · 2025Article
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Authors and funding
17 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundAnastomotic leakage (AL), a severe complication following colorectal surgery, arises from defects at the anastomosis site. This study evaluates the feasibility of predicting AL using machine learning (ML) algorithms based on preoperative data.
methodsWe retrospectively analyzed data including 21 predictors from patients undergoing colorectal surgery with bowel anastomosis at four Swiss hospitals. Several ML algorithms were applied for binary classification into AL or non-AL groups, utilizing a five-fold cross-validation strategy with a 90% training and 10% validation split. Additionally, a holdout test set from an external hospital was employed to assess the models' robustness in external validation.
resultsAmong 1244 patients, 112 (9.0%) suffered from AL. The Random Forest model showed an AUC-ROC of 0.78 (SD: ± 0.01) on the internal test set, which significantly decreased to 0.60 (SD: ± 0.05) on the external holdout test set comprising 198 patients, including 7 (3.5%) with AL. Conversely, the Logistic Regression model demonstrated more consistent AUC-ROC values of 0.69 (SD: ± 0.01) on the internal set and 0.61 (SD: ± 0.05) on the external set. Accuracy measures for Random Forest were 0.82 (SD: ± 0.04) internally and 0.87 (SD: ± 0.08) externally, while Logistic Regression achieved accuracies of 0.81 (SD: ± 0.10) and 0.88 (SD: ± 0.15). F1 Scores for Random Forest moved from 0.58 (SD: ± 0.03) internally to 0.51 (SD: ± 0.03) externally, with Logistic Regression maintaining more stable scores of 0.53 (SD: ± 0.04) and 0.51 (SD: ± 0.02).
conclusionIn this pilot study, we evaluated ML-based prediction models for AL post-colorectal surgery and identified ten patient-related risk factors associated with AL. Highlighting the need for multicenter data, external validation, and larger sample sizes, our findings emphasize the potential of ML in enhancing surgical outcomes and inform future development of a web-based application for broader clinical use.
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