Development and validation of a multivariable prediction model for early prediction of chronic postsurgical pain in adults: a prospective cohort study

Abstract

BACKGROUND: Early identification of patients at risk of developing chronic postsurgical pain (CPSP) is an essential step in reducing pain chronification in postsurgical patients. We aimed to develop and validate a prognostic model for the early prediction of CPSP including pain characteristics indicating altered pain processing within 2 weeks after surgery. METHODS: A prospective cohort study was conducted in adult patients undergoing orthopaedic, vascular, trauma, or general surgery between 2018 and 2019. Multivariable logistic regression models for CPSP were developed using data from the University Medical Centre (UMC) Utrecht and validated in data from the Erasmus UMC Rotterdam, The Netherlands. RESULTS: In the development (n=344) and the validation (n=150) cohorts, 28.8% and 21.3% of patients reported CPSP. The best performing model (area under the curve=0.82; 95% confidence interval [CI], 0.76-0.87) included preoperative treatment with opioids (odds ratio [OR]=4.04; 95% CI, 2.13-7.70), bone surgery (OR=2.01; 95% CI, 1.10-3.67), numerical rating scale pain score on postoperative day 14 (OR=1.57; 95% CI, 1.34-1.83), and the presence of painful cold within the painful area 2 weeks after surgery (OR=4.85; 95% CI, 1.85-12.68). Predictive performance was confirmed by external validation. CONCLUSIONS: As only four easily obtainable predictors are necessary for reliable CPSP prediction, the models are useful for the clinician to be alerted to further assess and treat individual patients at risk. Identification of the presence of painful cold within 2 weeks after surgery as a strong predictor supports altered pain processing as an important contributor to CPSP development.