ArticleSurgical endoscopy2026
Early prediction of anastomotic leakage within 24 h after minimally invasive colorectal cancer surgery using postoperative inflammatory markers: development and temporal validation of a machine learning model.
Article in Surgical endoscopy, 2026. 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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Abstract
purposeEarly identification of anastomotic leakage (AL) is critical for safe discharge within enhanced recovery pathways. This study developed and prospectively validated a machine learning (ML) model to predict AL using 24-h postoperative inflammatory biomarkers.
methodsWe analyzed 1,961 patients undergoing elective minimally invasive colorectal resection (2012-2025). Five ML architectures were developed using a 70/15/15 split. The Regularized Logistic Regression (RLB) model was selected and locked with a pre-specified threshold (0.1258). Global variable importance and directionality were assessed via SHAP analysis. Prospective temporal validation was performed on 250 consecutive patients (February 2024 - December 2025).
resultsAL incidence was 9.8% in the development cohort. The RLB model achieved high discrimination (AUCPR 0.859; AUC-ROC 0.819). Postoperative C-reactive protein (CRP) and the Systemic Inflammation Response Index (SIRI) at 24 h were the strongest predictors. During temporal validation, despite a 70% relative reduction in AL incidence (2.8%), the model maintained a robust negative predictive value (NPV) of 97.9% (95% CI 95.1-99.1% and an AUC of 0.73 (95% CI 0.54-0.92). Calibration was near-optimal (slope 0.987, intercept 0.505). Decision curve analysis demonstrated superior net clinical benefit across risk thresholds of 5-20%.
conclusionsML-based integration of early inflammatory biomarkers provides a reliable "safety filter" for postoperative surveillance. The high NPV supports objective decision-making for early discharge, even in changing clinical environments with decreasing complication rates.
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