This study aimed to identify independent risk factors for early postoperative pain following cesarean section and to develop a reliable nomogram prediction model to facilitate clinical risk assessment and individualized pain management. We retrospectively collected clinical data from women who underwent cesarean section at our hospital between January 2022 and December 2023. Early postoperative pain was assessed within 24 hours after surgery using the Numerical Rating Scale (NRS), and the highest NRS score (resting or activity-related) during the first 24 hours was used as the primary outcome measure. Moderate-to-severe pain was defined as NRS > 3 or the need for additional analgesia. Patients were categorized into pain and no-pain groups based on the presence of moderate-to-severe pain. Univariate logistic regression was performed to screen potential risk factors, followed by multivariate logistic regression to identify independent predictors. A nomogram prediction model was then constructed based on the final multivariate results. Model performance was evaluated by receiver operating characteristic (ROC) analysis and the area under the curve (AUC). Calibration was assessed using calibration curves, and clinical utility was examined through decision curve analysis. Internal validation was conducted using bootstrap resampling. A total of 768 patients were included, of whom 587 (76.43%) experienced early postoperative pain. Multivariate logistic regression identified 4 independent predictors: diabetes mellitus (OR = 1.763, 95% CI: 1.072–3.874), history of previous surgery (OR = 2.825, 95% CI: 1.152–4.678), absence of intraoperative NSAID use (OR = 3.164, 95% CI: 1.198–9.476), and intraoperative blood transfusion (OR = 2.124, 95% CI: 1.182–7.635). The nomogram demonstrated good discriminative ability, with an AUC of 0.804 in the training cohort and 0.770 in the validation cohort, and showed favorable calibration and clinical utility. Early postoperative pain after cesarean section is influenced by multiple perioperative factors. The nomogram developed in this study provides an effective tool for individualized risk prediction, enabling clinicians to identify high-risk patients and implement targeted preventive strategies. This model may contribute to optimizing postoperative pain management and enhancing maternal recovery.
Xu et al. (Fri,) studied this question.