Developing and internally validating a prediction model for total knee replacement surgery in patients with osteoarthritis. Issue 3 (September 2022)

• Record Type: Journal Article
• Authors: Thuraisingam, Sharmala; Chondros, Patty; Manski-Nankervis, Jo-Anne; Spelman, Tim; Choong, Peter F.; Gunn, Jane; Dowsey, Michelle M.
• Is Part Of: Osteoarthritis and cartilage open. Volume 4:Issue 3(2022)
• Publication Date: 2022-09
• Subjects: Clinical prediction tool -- Decision support tool -- Competing risk model -- Total knee replacement -- Electronic health record -- Electronic medical record; Osteoarthritis -- Periodicals; Cartilage -- Periodicals; 616.7223005
• DOI: 10.1016/j.ocarto.2022.100281
• Languages: English
• ISSNs: 2665-9131

Abstract

Abstract: Objective: The objective of this study was to develop and internally validate a clinical algorithm for use in general practice that predicts the probability of total knee replacement (TKR) surgery within the next five years for patients with osteoarthritis. The purpose of the model is to encourage early uptake of first-line treatment strategies in patients likely to undergo TKR and to provide a cohort for the development and testing of novel interventions that prevent or delay the progression to TKR. Method: Electronic health records (EHRs) from 201, 462 patients with osteoarthritis aged 45 years and over from 483 general practices across Australia were linked with records from the Australian Orthopaedic Association National Joint Replacement Registry and the National Death Index. A Fine and Gray competing risk prediction model was developed using these data to predict the risk of TKR within the next five years. Results: During a follow-up time of 5 years, 15, 979 (7.9%) patients underwent TKR and 13, 873 (6.9%) died. Predictors included in the final algorithm were age, previous knee replacement, knee surgery (other than TKR), prescribing of osteoarthritis medication in the 12 months prior, comorbidity count and diagnosis of a mental health condition. Optimism corrected model discrimination was 0.67 (95% CI: 0.66 to 0.67) and model calibration acceptable. Conclusion: The model has the potential to reduce some of the economic burden associated with TKR in Australia. External validation and further optimisation of the algorithm will be carried out prior to implementation within Australian general practice EHR systems.