ObjectivesTo develop and validate a simple clinical prediction model, based on easily collected preoperative information, to identify patients at high risk of pain and functional disability 6 months after total knee arthroplasty (TKA).MethodsThis was a multi-centre cohort study of patients from 9 centres across the UK, who were undergoing a primary TKA for osteoarthritis. Information on socio-demographic, psychosocial, clinical, and quality of life measures were collected at recruitment. The primary outcome measure for this analysis was Oxford Knee Score, measured 6 months postoperatively by postal questionnaire. Multivariable logistic regression was used to develop the model. Model performance (discrimination and calibration) and internal validity was assessed, and a simple clinical risk score developed.Results721 participants (mean age 68.3 years; 53% female) provided data for the current analysis and 14% had a poor outcome at 6 months. Key predictors were poor clinical status, widespread body pain, high expectation of postoperative pain, and lack of active coping. The developed model based on these variables demonstrated good discrimination. At the optimal cut-off, the final model had a sensitivity of 83%, specificity of 61%, and positive likelihood ratio of 2.11. Excellent agreement was found between observed and predicted outcomes, and there was no evidence of overfitting in the model.ConclusionWe have developed and validated a clinical prediction model that can be used to identify patients at high risk of a poor outcome after TKA. This clinical risk score may be an aid to shared decision-making between patient and clinician.
- knee pain
- total knee arthroplasty
- prediction modelling
- clinical risk score
- model calibration
- model discrimination
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- School of Medicine, Medical Sciences & Nutrition, Applied Health Sciences - Senior Research Fellow
- School of Medicine, Medical Sciences & Nutrition, Centre for Health Data Science
- School of Medicine, Medical Sciences & Nutrition, Data Safe Haven
- School of Medicine, Medical Sciences & Nutrition, Medical Statistics
Person: Academic, Academic Related - Research