Journal of Pain Research (Jul 2023)
Protocol for Development and Validation of Multivariable Prediction Models for Chronic Postsurgical Pain Following Video-Assisted Thoracic Surgery
Abstract
Jing-Hui Hu,1,2,* Hai-Jing Shi,1,2,* Zhen-Yu Han,3,* Hong Liu,4 Fu-Hai Ji,1,2 Ke Peng1,2 1Departments of Anesthesiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, People’s Republic of China; 2Institute of Anesthesiology, Soochow University, Suzhou, Jiangsu, People’s Republic of China; 3Department of Thoracic Surgery, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, People’s Republic of China; 4Department of Anesthesiology and Pain Medicine, University of California Davis Health, Sacramento, CA, USA*These authors contributed equally to this workCorrespondence: Ke Peng, Department of Anesthesiology, First Affiliated Hospital of Soochow University, 188 Shizi St, Suzhou, Jiangsu, 215006, People’s Republic of China, Tel +86-159-6215-5989, Email [email protected]: Chronic postsurgical pain (CPSP) is a common complication after thoracic surgery and associated with long-term adverse outcomes. This study aims to develop two prediction models for CPSP after video-assisted thoracic surgery (VATS).Methods and Analysis: This single-center prospective cohort study will include a total of 500 adult patients undergoing VATS lung resection (n = 350 for development and n = 150 for external validation). Patients will be enrolled continuously at The First Affiliated Hospital of Soochow University in Suzhou, China. The cohort for external validation will be recruited in another time period. The outcome is CPSP, which is defined as pain with the numerical rating scale score of 1 or higher 3 months after VATS. Univariate and multivariable logistic regression analyses will be performed to develop two CPSP prediction models based on patients’ data of postoperative day 1 and day 14, respectively. For internal validation, we will use the bootstrapping validation technique. For external validation, the discrimination capability of the models will be assessed using the area under the receiver operating characteristic curve, and the calibration will be evaluated using the calibration curve and Hosmer–Lemeshow goodness-of-fit statistic. The results will be presented in model formulas and nomograms.Conclusion: Based on the development and validation of the prediction models, our results contribute to early prediction and treatment of CPSP after VATS.Trial Registration: Chinese Clinical Trial Register (ChiCTR2200066122).Keywords: chronic postsurgical pain, prediction, video-assisted thoracoscopic surgery
