Research Article
A Preoperative Risk Prediction Model for Complications After Radical Resection of Esophageal Cancer
Issue:
Volume 7, Issue 1, March 2026
Pages:
1-11
Received:
6 March 2026
Accepted:
18 March 2026
Published:
28 March 2026
DOI:
10.11648/j.ajnhs.20260701.11
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Abstract: Objective: To analyze preoperative risk factors for complications following radical esophageal cancer surgery, and to establish a preoperative risk prediction model for postoperative complications, thereby providing a reference for medical staff to formulate and implement reasonable intervention measures for patients. Methods: Clinical data from 485 patients who underwent surgery for esophageal cancer were retrospectively collected and randomly divided into a training set (340 cases) and a validation set (145 cases) at a ratio of 7: 3. Independent predictors were identified by univariate logistic regression, LASSO regression, and multivariate logistic regression analysis, and a nomogram model was constructed. The model's discriminative ability, calibration, and clinical utility were evaluated using ROC curves, calibration curves, DCA curves, and CIC curves. Results: The incidence of postoperative complications in this study was 36.9%. The proportions of various complication types were: pleural effusion 49.2%, aspiration pneumonia 7.3%, pulmonary infection 40.2%, pneumothorax 16.8%, atelectasis 2.8%, and respiratory failure 2.8%. Five predictors—age, BMI, underlying lung disease, NRS 2002 score, and tumor location—were included in the final model. The AUCs of the training set and validation set were 0.777 (95% CI: 0.725–0.829) and 0.702 (95% CI: 0.609–0.790), respectively, indicating good calibration and clinical practicability. Conclusion: The preoperative risk prediction model established in this study demonstrates good discrimination, calibration, and clinical effectiveness.
Abstract: Objective: To analyze preoperative risk factors for complications following radical esophageal cancer surgery, and to establish a preoperative risk prediction model for postoperative complications, thereby providing a reference for medical staff to formulate and implement reasonable intervention measures for patients. Methods: Clinical data from 48...
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Hongjuan Wu*
