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Article Abstract

Background: It is clinically important to anticipate the likelihood of pain catastrophizing in patients who undergo total knee arthroplasty (TKA). Persistent pain and diminished physical function following TKA are independently associated with preoperative pain catastrophizing. The purpose of this study was to develop and validate a nomogram model to predict pain catastrophizing in patients who have severe osteoarthritis undergoing primary TKA.

Methods: Data were collected from patients who have severe osteoarthritis undergoing primary TKA at four tertiary general hospitals in Changsha, China, from September to December 2023. The study cohort was randomly divided into a training group and a validation group in the proportion of 70 to 30%. Least absolute shrinkage and selection operator regression analysis was utilized to select the optimal predictive variables for the model. A nomogram model was created using independent risk factors that were identified through multivariate regression analysis. Their performance was assessed using the concordance index and calibration curves, and their clinical utility was analyzed using decision curve analysis.

Results: A total of 416 patients were included, 291 in the training group and 125 in the validation group. There were 115 (27.6%) who had pain catastrophizing. The predictors contained in the nomogram were pain intensity during activity, anxiety and depression, body mass index, social support, and household. The area under the curve of the nomogram was 0.976 (95% confidence interval = 0.96 to 0.99) for the training group and 0.917 (95% confidence interval = 0.88 to 0.96) for the validation group. The calibration curves confirmed the nomogram's accuracy, and decision curve analysis showed its strong predictive performance.

Conclusions: The comprehensive nomogram generated in this study was a valid and easy-to-use tool for assessing the risk of pain catastrophizing in preoperative TKA patients, and helped healthcare professionals to screen the high-risk population.

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http://dx.doi.org/10.1016/j.arth.2024.09.011DOI Listing

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