Identification of patients experiencing poor outcomes following total knee arthroplasty (TKA) before the intervention could allow better case selection, patient preparation and, likely, improved outcomes. The objective was to develop a preliminary prediction rule (PR) to identify patients enrolled on surgical wait lists who are at the greatest risk of poor outcomes 6 months after TKA.
141 patients scheduled for TKA were recruited prospectively from the wait lists of 3 hospitals in Quebec City, Canada. Knee pain, stiffness and function were measured 6 months after TKA with the Western Ontario and McMaster Osteoarthritis Index (WOMAC) and participants in the lowest quintile for the WOMAC total score were considered to have a poor outcome. Several variables measured at enrolment on the wait lists (baseline) were considered potential predictors: demographic, socioeconomic, psychosocial, and clinical factors including pain, stiffness and functional status measured with the WOMAC. The prediction rule was built with recursive partitioning.
The best prediction was provided by 5 items of the baseline WOMAC. The rule had a sensitivity of 82.1% (95% CI: 66.7-95.8), a specificity of 71.7% (95% CI: 62.8-79.8), a positive predictive value of 41.8% (95% CI: 29.7-55.0), a negative predictive value of 94.2% (95% CI: 87.1-97.5) and positive and negative likelihood ratios of 2.9 (95% CI: 1.8-4.7) and 0.3 (95% CI: 0.1-0.6) respectively.
The developed PR is a promising tool to identify patients at risk of worse outcomes 6 months after TKA as it could help improve the management of these patients. Further validation of this rule is however warranted before clinical use.