Multivariate integrated predictive model for 1-year mortality risk prognosis in patients undergoing esophageal cancer resection: establishment and validation

OBJECTIVE: To develop and validate a multidimensional prognostic model for predicting postoperative 1-year all-cause mortality in esophageal cancer patients by integrating clinical, hematological, tumor-related, and perioperative factors (including the Naples Prognostic Score, NPS), and to provide evidence for clinical risk stratification and individualized interventions. METHODS: A retrospective analysis was conducted on 625 patients (≥ 18 years) who underwent esophageal cancer resection (January 2018–January 2023) at the Cancer Hospital Affiliated to Xinjiang Medical University. Ultimately, 521 eligible patients were enrolled and split into a training cohort (n = 364, 70%) and validation cohort (n = 157, 30%). LASSO regression was used for feature selection, and Cox univariate/multivariate analyses identified independent risk factors for 1-year mortality. Kaplan-Meier analysis compared survival differences by NPS and Clavien-Dindo classification. RESULTS: The 1-year all-cause mortality rate was 20.0% (104/521). Independent predictors included anemia (assessed by red blood cell count), white blood cell count, NPS, Clavien-Dindo classification, and TNM stage (all P < 0.05). The model had an AUC of 0.82 (95% CI: 0.76–0.87) in the training cohort and 0.76 (95% CI: 0.66–0.86) in the validation cohort. Calibration curves and DCA confirmed good calibration and clinical utility. Kaplan-Meier analysis showed significant survival differences between NPS < 3 vs. ≥3 and Clavien-Dindo < 3 vs. ≥3 (both P < 0.0001). CONCLUSION: This multidimensional model effectively predicts postoperative 1-year mortality in esophageal cancer patients. With NPS as a core component, it provides a practical tool for perioperative risk stratification and individualized management to identify high-risk patients and reduce mortality.