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Updated: Jan 11, 2026
An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
Published on: January 27, 2010
Development and validation of a predictive model for acute postoperative pain after thoracoscopic lobectomy in
Jin Huang1,2, Ping-Lan Wang3, Jia-Zhou Xiao4
1Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, China.
Background:
Acute postoperative pain (APP) management following radical resection of non-small cell lung cancer (NSCLC) constitutes a core component of enhanced recovery after surgery protocols. The development of precise APP risk prediction models holds significant clinical importance, as these models enable early pain detection and tiered interventions, effectively mitigate postoperative stress responses, reduce the incidence of pulmonary complications, and ultimately accelerate the postoperative recovery trajectory.
Methods:
A training cohort of 1256 patients with NSCLC undergoing thoracoscopic lobectomy between June 2021 and December 2022 was included in this study, and an external validation cohort of 321 patients undergoing the same procedure was established during the same period. A nomogram for predicting APP after thoracic surgery was constructed based on a binary logistic regression model. The predictive power of the model was evaluated using subject operating characteristic curves receiver operating characteristic curves (area under the curve, AUC), calibration plots, and decision curve analysis (DCA).
Results:
The incidence of APP in the development and validation cohorts was 20.5% (257/1256) and 21.5% (69/321), respectively. In both cohorts, APP was significantly associated with postoperative chronic pain and pulmonary infections ( P < 0.05). In the modeling group, preoperative use of analgesics, smoking history, age, history of thoracic surgery, cancer history, and anxiety status were identified as independent risk factors for APP. Therefore, we developed a nomogram, which showed an AUC of 0.760 (95% CI: 0.726-0.794) and 0.734 (95% CI: 0.668-0.801) in the training and validation cohorts, respectively. The calibration plot demonstrated a high consistency between the predicted and observed outcomes. DCA indicated that the nomogram provided significant clinical net benefit. Furthermore, we developed a simplified scoring system to predict APP in patients, which demonstrated good predictive ability.
Conclusion:
The developed nomogram serves as a practical tool for predicting the risk of APP following thoracoscopic lobectomy for NSCLC. This model can support personalized management and preventive strategies, enhancing APP control.
