Chinese Journal of Tissue Engineering Research ›› 2023, Vol. 27 ›› Issue (4): 558-564.doi: 10.12307/2022.963

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Retrospective analysis of the influencing factors of chronic pain after total knee arthroplasty

Wan Guoli, Shi Chenhui, Wang Weishan, Li Ang, Shi Xunda, Cai Yi   

  1. Orthopedics Center, First Affiliated Hospital of Shihezi University School of Medicine, Shihezi 832008, Xinjiang Uygur Autonomous Region, China
  • Received:2021-11-20 Accepted:2022-01-13 Online:2023-02-08 Published:2022-06-22
  • Contact: Shi Chenhui, Chief physician, Professor, Doctoral supervisor, Orthopedics Center, First Affiliated Hospital of Shihezi University School of Medicine, Shihezi 832008, Xinjiang Uygur Autonomous Region, China
  • About author:Wan Guoli, Master candidate, Orthopedics Center, First Affiliated Hospital of Shihezi University School of Medicine, Shihezi 832008, Xinjiang Uygur Autonomous Region, China
  • Supported by:
    National Natural Science Foundation of China, No. 81660374 (to SCH); National Natural Science Foundation of China, No. 81760404 (to WWS)

Abstract: BACKGROUND: The influencing factors of chronic pain after knee arthroplasty are a hot spot in clinical research. However, there are few reports on how to achieve individualized prediction of the risk of chronic pain after knee arthroplasty at home and abroad. 
OBJECTIVE: To explore the influencing factors of chronic pain after knee arthroplasty by constructing and validating an individualized prediction model of chronic pain risk after knee arthroplasty using nomogram. 
METHODS: Totally 212 patients who underwent knee arthroplasty in the First Affiliated Hospital of Shihezi University from January 2018 to October 2020 were enrolled in this study. The data of the patients were collected and followed up. Through Logistics regression analysis, the independent risk factors of chronic pain after knee arthroplasty were selected to construct a predictive model. The C-index, ROC curve, calibration chart and decision curve analysis were used to evaluate the identification, calibration and clinical usefulness of the predictive model. The bootstrap verification was used to evaluate internal verification. 
RESULTS AND CONCLUSION: (1) Predictors contained in the prediction nomogram included sleep, hip-knee-ankle angle, preoperative pain visual analogue scale score, pain visual analogue scale score at discharge, and time of tourniquet. The constructed model had a good recognition ability. (2) The ROC curve showed that the model predicted the influencing factors of chronic pain after knee arthroplasty. The area under the curve was 0.833, and the C-index calculated by the R software was 0.837 (95% CI: 0.824-0.849). The high C-index value of 0.810 4 could still be reached in the interval verification, with good calibration and good ability to predict. (3) It is concluded that sleep, hip-knee-ankle angle, preoperative pain visual analogue scale score, pain visual analogue scale score at discharge, and time of tourniquet are independent risk factors for chronic pain after knee arthroplasty. A nomogram model is constructed to predict the influencing factors of chronic pain after knee arthroplasty. Good discrimination and accuracy can provide scientific guidance for individualized clinical prevention and treatment of chronic pain after knee arthroplasty. 

Key words: chronic pain after knee arthroplasty, knee osteoarthritis, total knee arthroplasty, pain sensitization, postoperative acute pain

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