Using Two Predictive Models to Capture Two Types of Poor Outcomes in Knee Arthroplasty: A Multisite Longitudinal Cohort Study
Supported by the Pfizer Global Medical Grant Awards for Advancing Chronic Pain Research (grant 69863469).
Additional supplementary information cited in this article can be found online in the Supporting Information section (http://onlinelibrary.wiley.com/doi/10.1002/art.42819).
Author disclosures and graphical abstract are available at https://onlinelibrary.wiley.com/doi/10.1002/art.42819.
Abstract
Objective
Poor outcome after knee arthroplasty (KA), a common major surgery worldwide, reportedly occurs in approximately 20% of patients. These patients demonstrate minimal improvement, at least moderate knee pain, and difficulty performing many routine daily activities. The purposes of our study were to comprehensively determine poor outcome risk after KA and to identify predictors of poor outcome.
Methods
Data from 565 participants with KA in the Osteoarthritis Initiative and the Multicenter Osteoarthritis studies were used. Previously validated latent class analyses (LCAs) of good versus poor outcome trajectories of Western Ontario and McMaster Universities Arthritis Index (WOMAC) Pain and Disability were generated to describe minimal improvement and poor final outcome. The modified Escobar RAND appropriateness system was used to generate classifications of appropriate, inconclusive, and rarely appropriate. Multivariable prediction models included LCA-based good versus poor outcome, modified Escobar classifications, and evidence-driven preoperative prognostic variables.
Results
Modified Escobar appropriateness classifications were nonsignificant predictors of WOMAC Pain good versus poor outcomes, indicating the methods provide independent outcome estimates. For WOMAC Pain and WOMAC Disability, approximately 34% and 45% of participants, respectively, had a high probability of either minimal improvement via “rarely appropriate” classifications or poor outcome via LCA. In multivariable prediction models, greater contralateral knee pain consistently predicted poor outcome (eg, odds ratio 1.21, 95% confidence interval 1.10–1.33).
Conclusion
Appropriateness criteria and LCA estimates provided combined poor outcome estimates that were approximately double the commonly reported poor outcome of 20%. Rates of poor outcome could be reduced if clinicians screened patients using appropriateness criteria and LCA predictors before surgery to optimize outcome.
