Development and Temporal Validation of a Discharge-Based Administrative Data Risk Model for 30-Day Unplanned Readmission After Endometrial Cancer Staging Surgery

Background: Postoperative complications after staging surgery for endometrial cancer often occur after discharge, and 30-day unplanned readmission is an important post-discharge outcome. Existing models lack same-day usability at discharge and sufficient validation, limiting discharge-time risk stratification and transitional care planning. This study aimed to develop and validate a discharge-time risk prediction model based on administrative data for 30-day unplanned hospital readmission among patients undergoing staging surgery for endometrial cancer. Methods: This retrospective cohort study was conducted in accordance with the TRIPOD reporting guidelines. We constructed a nationally representative adult cohort using the Healthcare Cost and Utilization Project Nationwide Readmissions Database (HCUP-NRD) from 2016 to 2022, including index hospitalizations discharged between January and November to ensure complete 30-day follow-up. Variables available at discharge (demographics, payer/income, presentation, inpatient course, discharge disposition, hospital characteristics, Elixhauser index) were included; age and length of stay were modeled with restricted cubic splines. Complex survey-weighted multivariable logistic regression was used for model development with 1000 bootstrap internal validations. Temporal validation within the NRD was performed using 2020–2021 and 2022 data. Decision curve analysis, subgroup, and sensitivity analyses were conducted. Results: A total of 89,627 hospitalizations were included; development and validation cohorts had balanced baselines. Internal validation showed moderate discrimination (AUC 0.719) and good calibration (Brier 0.045; intercept 0.014; slope 0.955). Discharge to home health or institutional care, non-elective or emergency admission, and a higher Elixhauser readmission index (a measure of comorbidity burden) were associated with increased risk. Age and length of stay showed nonlinear associations. Temporal validation yielded a similar performance (AUC 0.708–0.713; Brier 0.043–0.046), and decision curves indicated positive net clinical benefit. Conclusions: The model demonstrated stable discrimination and maintained good calibration after temporal validation and recalibration. It represents a candidate discharge-time decision-support tool, based on administrative data, for identifying patients at higher risk of 30-day unplanned readmission, and may support nurse-led transitional care planning. External validation and local recalibration are required before application in other healthcare settings.