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

PURPOSE: Early 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. METHODS: We 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). RESULTS: AL 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%. CONCLUSIONS: ML-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.