Development and validation of a nomogram to predict anastomotic leakage after esophagectomy for esophageal carcinoma

BACKGROUND: This study aimed to identify variables associated with anastomotic leakage after esophagectomy and established a tool for anastomotic leakage prediction. METHODS: Twenty-six preoperative and postoperative variables were retrospectively collected from esophageal cancer patients who were treated with radical esophagectomy from January 2018 to June 2020 in the Affiliated Hospital of Qingdao University. SPSS Version 23.0 and Empower Stats software were used for establishing a nomogram after screening relevant variables by univariate and multivariate Logistic regression analyses. The established nomogram was identified by depicting the receiver operating characteristic (ROC) curves and calibration curve, which was verified by 1,000 bootstrap resamples method. RESULTS: A total of 604 eligible esophageal cancer patients were included, of which 51 (8.4%) patients had anastomotic leakage. Multivariate Logistic regression analysis showed that smoking, anastomotic location, anastomotic technique, prognostic nutritional index (PNI) and ASA score were independent risks of anastomotic leakage. The area under curve (AUC) of ROC in the established nomogram was 0.764 (95% CI, 0.69–0.83). The internal validation confirmed that the nomogram had a great discrimination ability (AUC =0.766). Depicted calibration curve demonstrated a well-fitted prediction and observation probability. In addition, the decision curve analysis concluded that the newly established nomogram is significant for clinical decision-making. CONCLUSIONS: This nomogram provided the individual prediction of anastomotic leakage for esophageal cancer patients after surgery, which might benefit treatment results for patients and clinicians, as well as pre-and postoperative intervention strategy-making.