Construction and validation of chronic pain prediction model after total knee arthroplasty
Abstract
Objective: To explore the risk factors of chronic pain after total knee arthroplasty (TKA) and to establish and verify a prediction model.
Methods: As a retrospective observational study, medical records of 239 patients who underwent TKA in Affiliated Hospital of Jiangnan University from January 2020 to December 2022 were reviewed. Fifty four patients suffered from chronic pain after TKA surgery. Univariate and multivariate logistic regression were used to analyze factors associated with the occurrence of chronic pain after TKA. A nomogram prediction model was established based on the identified independent risk factors, and its predictive effectiveness was evaluated.
Results: Gender, postoperative 24-hourss numerical rating scale (NRS) and postoperative three months Hospital for Special Surgery Knee-Rating (HSS) scores were independent risk factors for chronic pain after TKA (p<0.05). The area of the receiver operating characteristic (ROC) of the nomogram model based on these factors was 0.904 (95% confidence interval [CI): 0.861-0.947), which indicates a good predictive value for the postoperative chronic pain. When the optimal cut off value was selected, the sensitivity and specificity of the model were 92.6% and 74.1%, respectively, indicating that the predictive model is effective.
Conclusions: Gender, postoperative 24-hours NRS and postoperative three months HSS score are independent risk factors for chronic pain after TKA. The nomogram prediction model based on these factors is effective and can provide auxiliary reference for patients with chronic pain after TKA.
doi: https://doi.org/10.12669/pjms.40.4.8979
How to cite this: Qian J, Wang X. Construction and validation of chronic pain prediction model after total knee arthroplasty. Pak J Med Sci. 2024;40(4):657-662. doi: https://doi.org/10.12669/pjms.40.4.8979
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