Development and validation of an early warning model for postoperative reflux in patients with esophageal cancer

To develop and validate a predictive model for assessing the risk of postoperative reflux in patients with esophageal cancer.

A total of 278 postoperative patients with esophageal cancer admitted between January and December 2022 were enrolled as the modeling cohort. An independent cohort of 120 patients admitted between January and April 2023 was used for external validation. Logistic regression analysis was performed to construct the risk prediction model. Model performance was evaluated in terms of discrimination, calibration, and goodness-of-fit. Discrimination was assessed using the area under the receiver operating characteristic curve (AUC), calibration was examined using calibration plots generated by 1000 bootstrap resamples, and the Hosmer-Lemeshow goodness-of-fit test was applied (P ​< ​0.05 considered significant).

The incidence of postoperative reflux in the modeling cohort was 36.3%. The AUC was 0.813 (95% CI: 0.762-0.864) in the modeling cohort and 0.820 (95% CI: 0.747-0.894) in the validation cohort, indicating good discriminative ability. The Hosmer-Lemeshow test showed P-values of 0.155 and 0.059 in the modeling and validation cohorts, respectively, indicating acceptable goodness-of-fit. Calibration curves demonstrated close agreement between predicted and observed probabilities in both cohorts, confirming good model calibration.

The developed early warning model demonstrates acceptable predictive performance for identifying postoperative reflux risk in patients with esophageal cancer. It may assist clinicians in early risk assessment and individualized intervention, although further external validation is warranted prior to clinical implementation.