Development and validation of a predictive model for acute postoperative pain after thoracoscopic lobectomy in patients with NSCLC: a multicenter retrospective study

作者：Jin Huang, Ping‐Lan Wang, Jiazhou Xiao, Ye Lin, Cheng‐xiong You, Z. T. Sun, Chao Chen, Yan-Ming Shen, Yun-Fan Luo, Jie Chen, Shaojun Xu, Shuchen Chen · 发表于：International Journal of Surgery · 年份：2025 · DOI：10.1097/js9.0000000000003925 · 被引用次数：1 · 研究领域：Lung Cancer Diagnosis and Treatment、Gastric Cancer Management and Outcomes、Pleural and Pulmonary Diseases

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, ...