Development and internal validation of a clinical risk tool to predict chronic postsurgical pain in adults: a prospective multicentre cohort study

Abstract

Abstract Chronic postsurgical pain (CPSP) is a highly prevalent condition. To improve CPSP management, we aimed to develop and internally validate generalizable point-of-care risk tools for preoperative and postoperative prediction of CPSP 3 months after surgery. A multicentre, prospective, cohort study in adult patients undergoing elective surgery was conducted between May 2021 and May 2023. Prediction models were developed for the primary outcome according to the International Association for the Study of Pain criteria and a secondary threshold-based CPSP outcome. Models were developed with multivariable logistic regression and backward stepwise selection. Internal validation was conducted using bootstrap resampling, and optimism was corrected by shrinkage of predictor weights. Model performance was assessed by discrimination and calibration. Clinical utility was assessed by decision curve analysis. The final cohort included 960 patients, 16.3% experienced CPSP according to the primary outcome and 33.6% according to the secondary outcome. The primary CPSP model included age and presence of other preoperative pain. Predictors in the threshold-based models associated with an increased risk of CPSP included younger age, female sex, preoperative pain in the surgical area, other preoperative pain, orthopedic surgery, minimally invasive surgery, expected surgery duration, and acute postsurgical pain intensity. Optimism-corrected area-under-the-receiver-operating curves for preoperative and postoperative threshold-based models were 0.748 and 0.747, respectively. These models demonstrated good calibration and clinical utility. The primary CPSP model demonstrated fair predictive performance including 2 significant predictors. Derivation of a generalizable risk tool with point-of-care predictors was possible for the threshold-based CPSP models but requires independent validation.