Postlaparoscopic appendectomy acute pain: identifying risk factors and building a clinical prediction model

Background: Laparoscopic appendectomy is the standard treatment for acute appendicitis; however, postoperative acute pain remains a significant challenge. This study aimed to identify risk factors and develop an externally validated nomogram to predict moderate-to-severe acute pain following the procedure. Methods: = 124). Postoperative pain intensity was quantified using the peak numeric rating scale (NRS) score recorded within the first 24 h (assessed at 1, 3, 7, 9, 12, and 24 h). Patients were categorized into mild (NRS ≤ 3) and moderate-to-severe (NRS > 3) pain groups. Potential risk factors were identified via univariate analysis, and multivariable binary logistic regression was performed to determine independent predictors after assessing multicollinearity using the variance inflation factor. A nomogram-based predictive model was then developed and rigorously evaluated using the area under the curve (AUC), calibration plots, and decision curve analysis (DCA) in both cohorts. Results: = 0.008, OR = 1.797; 95% CI 1.168-2.763). The developed nomogram exhibited robust discriminative performance, with an AUC of 0.762 (95% CI 0.716-0.808) in the training set and 0.785 in the external validation set. Calibration curves for both cohorts demonstrated optimal agreement between predicted and observed outcomes. In the validation cohort, DCA confirmed significant clinical net benefits across threshold ranges of 10%-14% and 16%-95%. Conclusion: Surgical approach, preoperative total delay, and admission body temperature were identified as independent predictors of acute pain following laparoscopic appendectomy. Compared with TPLA, the SPLA approach was associated with a significantly lower risk of moderate-to-severe acute pain. The externally validated nomogram provides a reliable clinical tool with high discriminative power and practical applicability, facilitating the identification of high-risk patients and supporting the optimization of individualized perioperative pain management strategies.