Development and validation of a nomogram for predicting 30-day major complications in elderly patients undergoing major abdominal surgery.

Abstract

Objective: To investigate the risk factors related to postoperative major complications in elderly patients undergoing major abdominal surgery, and to construct and validate a nomogram risk prediction model.

Methods: This study analyzed data from 380 elderly patients who underwent major abdominal surgery at the Affiliated Cancer Hospital of Xinjiang Medical University between April and November 2023. The cohort was randomly divided into training and validation sets. Variable selection was performed using Lasso regression, followed by univariate and multivariate logistic regression to identify predictors of major postoperative complications. A nomogram-based risk prediction model was subsequently developed and its predictive performance rigorously evaluated.

Results: This study analyzed clinical data from 370 elderly patients undergoing major abdominal surgery, of whom 104 (28.1%) developed major complications. Patients were randomly divided into training (n = 259) and validation (n = 111) cohorts in a 7:3 ratio. Using Lasso regression followed by univariate and multivariate logistic regression, gender, ASA classification, CFS, and CCI were identified as significant predictors of major postoperative complications (p < 0.05). The predictive model demonstrated strong performance, with AUCs of 0.884 (95%CI: 0.840-0.929) in the training cohort and 0.855 (95%CI: 0.784-0.927) in the validation cohort.

Conclusion: This model is helpful for the clinical prediction of major postoperative complications in elderly patients undergoing major abdominal surgery and assists clinicians in choosing individualized treatment plans to reduce the incidence of serious complications and improve the quality of postoperative recovery of patients.