Prediction of anastomotic leakage after esophagectomy for esophageal cancer: a nomogram study integrating systemic inflammation indices and clinical factors

Abstract

Background Esophageal cancer remains one of the leading causes of cancer-related mortality worldwide. Anastomotic leakage (AL) following esophagectomy is a major postoperative complication that significantly impacts patient outcomes, including mortality, morbidity, prolonged hospital stays, and increased healthcare costs. Despite advances in surgical techniques and adjuvant therapies, predicting the risk of AL remains a challenge. Objective This study aims to develop and validate a predictive model for assessing the risk of AL in esophageal cancer patients undergoing esophagectomy, based on comprehensive clinical and laboratory variables. Methods This retrospective cohort study included 650 esophageal cancer patients who underwent esophagectomy between January 2015 and May 2025, divided into a training set (n = 455) and a validation set (n = 195) at 7:3 ratio. Baseline demographic, clinicopathological, and laboratory data were collected, with AL as the primary outcome, defined according to the Esophagectomy Complications Consensus Group (ECCG). Univariable and multivariable logistic regression, restricted cubic splines (RCS), and nomogram development to identify predictors, with model performance assessed using receiver operating characteristic (ROC) curve, calibration plots, and decision curve analysis (DCA). Results Seven significant predictors of AL were identified in the training set: age, neoadjuvant radiotherapy, C-reactive protein-albumin-lymphocyte (CALLY) index, hypertension, neutrophil-to-lymphocyte ratio (NLR), neutrophil-to-monocyte ratio (NMR), and platelet-to-lymphocyte ratio (PLR). A nomogram model was developed, showing good discrimination (AUC = 0.813) and calibration in the training set. The validation cohort demonstrated moderate predictive accuracy (AUC = 0.763), with consistent net benefits observed across different risk thresholds in DCA. Conclusions In conclusion, this study established a potentially useful predictive model for AL risk, which may facilitate individualized risk stratification, guide perioperative decision-making, and ultimately contribute to reducing AL incidence and improving postoperative recovery.