Introduction: Determining the prevalence of chronic postsurgical pain (CPSP) after video-assisted thoracoscopic surgery (VATS) and identifying CPSP predictors should improve the prognosis of patients undergoing VATS. Although several studies have investigated predictors of CPSP after VATS, there were significant dissimilarities in the findings due to the confounding of predictors.

Chronic postsurgery pain (CPSP) refers to persistent or repeated pain around the incision after surgery. Different from acute postoperative pain, the persistence of CPSP seriously affects the quality of life of patients. CPSP has a considerable global impact due to large surgical volumes. Although the development of video-assisted thoracoscopy (VATS) has reduced the risk of CPSP, it still seriously affects patients’ quality of life. Clinical recognition of CPSP at an early stage is limited; therefore, we aimed to develop and validate a nomogram to identify the significant predictive factors associated with CPSP after VATS in patients with lung adenocarcinoma. We screened 137 patients with invasive adenocarcinoma of the lung from among 312 patients undergoing VATS. In this prospective study, patients were divided into the CPSP (n = 52) and non-CPSP (n = 85) groups according to the occurrence of CPSP. Relevant information was collected 1 day before surgery and 1–3 days after surgery, and the occurrence of CPSP was followed up by telephone at 3 months after surgery. Data on clinical characteristics and peripheral blood leukocyte miRNAs were used to establish a nomogram for predicting CPSP using least absolute shrinkage and selection operator (LASSO) regression methods. The area under curve (AUC) was used to determine the recognition ability of the nomograms. The model was subjected to correction and decision curve analyses. Four variables—body mass index (BMI), history of chronic pain, miR 550a-3p, and visual analog scale (VAS) score on postoperative day 2 (VAS2d)—were selected according to LASSO regression to build the nomogram. The nomogram demonstrated adequate calibration and discrimination in the prediction model, with an AUC of 0.767 (95% confidence interval: 0.679–0.856). The calibration plot showed the best fit between model predictions and practical observations, suggesting that the use of the proposed nomogram to predict CPSP is beneficial. A nomogram consisting of BMI, history of chronic pain, miR 550a-3p, and VAS2d predicted the risk of CPSP after VATS in patients with lung adenocarcinoma.