Development and validation of a nomogram for predicting anastomotic leakage after esophagectomy: integrating baseline characteristics and early postoperative biomarkers.
- 1Department of Thoracic Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Abstract
Anastomotic leakage (AL) is a devastating complication following esophagectomy. Current predictive models relying solely on static preoperative factors fail to capture the evolving postoperative physiological stressors. This study aimed to develop and validate a multidimensional nomogram integrating baseline vulnerability with early dynamic biomarkers to predict AL. A retrospective cohort of 298 esophageal cancer patients who underwent esophagectomy between January 2020 and December 2025 was analyzed. Independent risk factors were identified using multivariate logistic regression. A predictive nomogram was constructed, evaluated using the area under the curve (AUC), internally validated via 1,000 bootstrap resamplings, and assessed for clinical utility using decision curve analysis (DCA). AL occurred in 38 patients (12.8%). Multivariate analysis identified five independent predictors: body mass index (BMI), chronic obstructive pulmonary disease (COPD), anastomosis site, postoperative day (POD) 2 serum lactate, and POD 3 neutrophil-to-lymphocyte ratio (NLR). The nomogram demonstrated excellent discrimination with an AUC of 0.873 and a bootstrap-corrected AUC of 0.859. Furthermore, DCA confirmed that the model provided superior clinical net benefit across a wide range of threshold probabilities. Notably, preexisting COPD and postoperative pulmonary complications (PPCs) drove AL incidence. The model accurately identified high-risk patients by POD 3, well before the median clinical AL diagnosis on POD 8. Integrating static baseline factors with dynamic physiological feedback yields a highly accurate AL prediction tool. However, this model is currently only internally validated. By stratifying risk as early as POD 3, this nomogram provides a critical 5-day window for targeted interventions, though its broader clinical implementation strictly requires confirmation in independent cohorts.
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BibTeX
@article{chong2026development,
title = {{Development and validation of a nomogram for predicting anastomotic leakage after esophagectomy: integrating baseline characteristics and early postoperative biomarkers.}},
author = {Zhongjian Chong and Le Zhou and Zijian Ma and Aiping Zhang},
journal = {Journal of thoracic disease},
year = {2026},
month = sep,
doi = {10.21037/jtd-2026-1985},
url = {https://europepmc.org/article/MED/42996925},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Development and validation of a nomogram for predicting anastomotic leakage after esophagectomy: integrating baseline characteristics and early postoperative biomarkers.
AU - Zhongjian Chong
AU - Le Zhou
AU - Zijian Ma
AU - Aiping Zhang
PY - 2026
DA - 2026/09/29
JO - Journal of thoracic disease
DO - 10.21037/jtd-2026-1985
UR - https://europepmc.org/article/MED/42996925
AB - Anastomotic leakage (AL) is a devastating complication following esophagectomy. Current predictive models relying solely on static preoperative factors fail to capture the evolving postoperative physiological stressors. This study aimed to develop and validate a multidimensional nomogram integrating baseline vulnerability with early dynamic biomarkers to predict AL. A retrospective cohort of 298 esophageal cancer patients who underwent esophagectomy between January 2020 and December 2025 was analyzed. Independent risk factors were identified using multivariate logistic regression. A predictive nomogram was constructed, evaluated using the area under the curve (AUC), internally validated via 1,000 bootstrap resamplings, and assessed for clinical utility using decision curve analysis (DCA). AL occurred in 38 patients (12.8%). Multivariate analysis identified five independent predictors: body mass index (BMI), chronic obstructive pulmonary disease (COPD), anastomosis site, postoperative day (POD) 2 serum lactate, and POD 3 neutrophil-to-lymphocyte ratio (NLR). The nomogram demonstrated excellent discrimination with an AUC of 0.873 and a bootstrap-corrected AUC of 0.859. Furthermore, DCA confirmed that the model provided superior clinical net benefit across a wide range of threshold probabilities. Notably, preexisting COPD and postoperative pulmonary complications (PPCs) drove AL incidence. The model accurately identified high-risk patients by POD 3, well before the median clinical AL diagnosis on POD 8. Integrating static baseline factors with dynamic physiological feedback yields a highly accurate AL prediction tool. However, this model is currently only internally validated. By stratifying risk as early as POD 3, this nomogram provides a critical 5-day window for targeted interventions, though its broader clinical implementation strictly requires confirmation in independent cohorts.
SN - 2072-1439
SN - 2077-6624
ER -