The Journal of Arthroplasty, Volume 31, Issue 12, 2660 - 2667.e1

A New Prediction Model for Patient Satisfaction After Total Knee Arthroplasty

Van Onsem, Stefaan et al.


Total knee arthroplasty (TKA) is a proven and cost-effective treatment for osteoarthritis. Despite the good to excellent long-term results, some patients remain dissatisfied. Our study aimed at establishing a predictive model to aid patient selection and decision-making in TKA.


Using data from our prospective arthroplasty outcome database, 113 patients were included. Preoperatively and postoperatively, the patients completed 107 questions in 5 questionnaires: Knee Injury and Osteoarthritis Outcome Score, Oxford Knee Score, Pain Catastrophizing Scale, Euroqol questionnaire, and Knee Scoring System. First, outcome parameters were compared between the satisfied and dissatisfied group. Second, we developed a new prediction tool using regression analysis. Each outcome score was analyzed with simple regression. Subsequently, the predictive weight of individual questions was evaluated applying multiple linear regression. Finally, 10 questions were retained to construct a new prediction tool.


Overall satisfaction rate in this study was found to be 88%. We identified a significant difference between the satisfied and dissatisfied group when looking at the preoperative questionnaires. Dissatisfied patients had more preoperative symptoms (such as stiffness), less pain, and a lower quality of life. They were more likely to ruminate and had a lower preoperative Knee Scoring System satisfaction score. The developed prediction tool consists of 10 simple but robust questions. Sensitivity was 97% with a positive-predictive value of 93%.


Based upon preoperative parameters, we were able to partially predict satisfaction and dissatisfaction after TKA. After further validation, this new prediction tool for patient satisfaction following TKA may allow surgeons and patients to evaluate the risks and benefits of surgery on an individual basis and help in patient selection.

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