Development and validation of a predictive model for early postoperative pain after cesarean section

Area of Science:

• Obstetrics and Gynecology
• Pain Management
• Medical Informatics

Background:

• Early postoperative pain is a significant concern following cesarean section, impacting maternal recovery.
• Effective pain management strategies require accurate identification of patients at high risk for severe pain.

Purpose of the Study:

• To identify independent risk factors for early postoperative pain after cesarean section.
• To develop and validate a predictive nomogram model for early postoperative pain risk assessment.
• To facilitate individualized pain management strategies and improve clinical outcomes.

Main Methods:

• Retrospective analysis of clinical data from 768 women undergoing cesarean section.
• Logistic regression analysis to identify independent predictors of pain (Numerical Rating Scale > 3 or need for analgesia).
• Construction and validation of a nomogram model using ROC analysis, calibration curves, and decision curve analysis.

Main Results:

• Over 76% of patients experienced early postoperative pain.
• Independent predictors identified: diabetes mellitus, history of previous surgery, absence of intraoperative NSAID use, and intraoperative blood transfusion.
• The nomogram showed good discriminative ability (AUC training: 0.804, AUC validation: 0.770) and favorable calibration.

Conclusions:

• Early postoperative pain after cesarean section is multifactorial, influenced by perioperative elements.
• The developed nomogram is an effective tool for predicting individual pain risk.
• This model supports targeted preventive strategies and optimized pain management, potentially enhancing maternal recovery.