A Clinical nomogram for predicting chronic postsurgical pain in patients undergoing video-assisted thoracoscopic...

To develop and internally validate a clinical nomogram for predicting chronic postsurgical pain (CPSP) in patients undergoing video-assisted thoracoscopic surgery (VATS), with the aim of facilitating early identification of high-risk patients and personalized perioperative management.

This retrospective case-control study enrolled 500 patients who underwent VATS between January 2022 and June 2024. Based on the presence of pain at 3 months postoperatively, patients were categorized into CPSP (n = 92) and non-CPSP (n = 408) groups. Perioperative variables including demographics, surgical details, and daily pain scores (visual analog scale, VAS) assessed at rest, during coughing, and during shoulder abduction on postoperative days 1 through 6 were collected. Univariate analysis, LASSO regression, and multivariate logistic regression were used to identify independent predictors. A nomogram was constructed and validated in a split cohort (70% training, 30% validation) using discrimination (area under the curve, AUC), calibration (Hosmer-Lemeshow test), and decision curve analysis.

Multivariate analysis identified higher postoperative shoulder abduction pain (OR = 2.012, 95% CI: 1.601-2.528), scar length (OR = 1.285, 95% CI: 1.101-1.500), and preoperative anxiety (OR = 3.245, 95% CI: 1.502-7.012) as independent risk factors for CPSP, while the use of intercostal sutures was a protective factor (OR = 0.221, 95% CI: 0.088-0.557). The nomogram incorporating these four predictors demonstrated excellent discrimination (training set AUC = 0.852, 95% CI: 0.805-0.899; validation set AUC = 0.837, 95% CI: 0.758-0.916) and good calibration (P > 0.05). Decision curve analysis confirmed its clinical utility across a wide threshold probability range.

A practical nomogram integrating postoperative shoulder abduction pain, scar length, preoperative anxiety, and intercostal suture use effectively predicts CPSP risk after VATS. This tool may assist clinicians in risk stratification, optimization of analgesic strategies, and implementation of targeted interventions to potentially reduce the incidence of CPSP.