Abstract
ObjectiveTo analyze the risk factors of anastomotic leakage after esophagectomy, establish a nomogram prediction model to better predict anastomotic leakage risk, and validate the model. MethodsIn total, 480 patients who underwent radical esophagectomy at our hospital between July 2022 and April 2024 were included in this study. Patients were randomly divided into training and validation groups in a ratio of 8:2. Independent risk factors for postoperative anastomotic leakage were screened using univariate and multivariate logistic regression analyses. These independent risk factors were then used as variables to construct a nomogram, the accuracy of which was verified by receiver operating characteristic (ROC) curves, calibration curve, and decision curve analysis (DCA). ResultsUnivariate and multivariate logistic regression analyses showed age, smoking index, diabetes, anastomotic location, and prognostic nutrition index (PNI) were significant risk factors for anastomotic leakage after esophagectomy (all P < 0.05). Based on the results, we constructed a risk prediction model for postoperative anastomotic leakage in esophageal cancer. The areas under ROC curves for the training and validation groups were 0.932 and 0.898, respectively. The calibration curve indicated that the model's predicted probability was consistent with the actual probability of anastomotic leakage after esophagectomy. The DCA curve demonstrated that the model had high efficiency in clinical decision-making. ConclusionsThe prediction model for anastomotic leakage after esophagectomy can identify high-risk patients, allowing for the formulation of individualized treatment plans to reduce its incidence. This can improve the prognosis of patients with esophageal cancer and offer a new strategy for perioperative treatments.
Published Version
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