A predictive tool for early identification of moderate-to-severe pain following open colorectal surgery in older adults: a retrospective cohort study.

Abstract

Background: Moderate-to-severe pain is a common but often under-recognized complication after open colorectal surgery in older adults, leading to delayed recovery and extended hospitalization. Early identification of high-risk patients is essential for timely pain management. The objective of this study was to develop and internally validate a predictive model, presented as a nomogram, for estimating the risk of moderate-to-severe postoperative pain within 24 h among elderly patients undergoing open colorectal surgery.

Methods: We conducted a retrospective cohort study of 300 patients aged ≥ 60 years who underwent elective open colorectal surgery. Postoperative pain within 24 h was assessed using the Numerical Rating Scale (NRS); NRS ≥ 4 was defined as moderate-to-severe pain. Preoperative psychosocial, cognitive, inflammatory, and perioperative factors were evaluated. Multivariable logistic regression with stepwise AIC selection identified independent predictors. Model performance was assessed using ROC curves, calibration plots, the Hosmer-Lemeshow test, and decision curve analysis (DCA). A nomogram was developed for clinical use.

Results: Of the 300 patients, 120 (40.0%) experienced moderate-to-severe pain. These patients were older and had higher preoperative NRS and CRP levels, along with worse psychosocial and cognitive scores (P < 0.01). Seven variables independently predicted pain severity: GAD-7, PHQ-9, MMSE, MOS-SSS, CRP, operative duration, and undergoing a Miles procedure (P < 0.05). The model showed good discrimination (AUC = 0.79 in training; 0.77 in validation) and calibration. DCA demonstrated net clinical benefit across a range of thresholds.

Conclusion: We developed and validated a nomogram incorporating psychosocial, inflammatory, and procedural factors to predict moderate-to-severe postoperative pain. This tool may enable early risk stratification and guide individualized analgesic strategies in elderly patients.