TKR

TKR provides patient-specific predictions of 1-year knee pain and physical function to inform decisions between total knee replacement (TKR) and nonsurgical treatments for advanced knee osteoarthritis and to identify candidates for randomized clinical trials (RCTs).

Key Features:

• Mathematical Equipoise: Assesses the balance between predicted outcomes of total knee replacement (TKR) and nonsurgical treatments for individual patients to identify when no clear treatment advantage exists.
• Predictive Modeling: Utilizes multivariable linear regression models to predict 1-year outcomes in knee pain and physical function for both TKR and nonsurgical options.
• Patient-Specific Variables: The knee pain model uses four variables with r^2 = 0.32 and the physical function model uses six variables with r^2 = 0.34.
• Evidence Database: Derives predictions from a database constructed using non-RCT sources of knee osteoarthritis outcomes and incorporating patient and clinician input on critical treatment outcomes.
• Outcome Overlap Visualization: Graphically illustrates the degree of overlap in pain and functional outcome distributions between TKR and nonsurgical treatments for individual patients.
• RCT Enrollment Support: Applies mathematical equipoise and model predictions to identify candidates for randomized clinical trials (RCTs).

Scientific Applications:

• Shared Decision-Making: Supports clinician–patient decision-making by providing evidence-based, patient-specific outcome predictions for TKR versus nonsurgical treatment.
• Personalized Treatment Planning: Enables tailoring of care plans based on predicted 1-year pain and functional outcomes for individual patients with knee osteoarthritis.
• Trial Recruitment: Identifies patients with clinical equipoise to support targeted recruitment into randomized clinical trials (RCTs).

Methodology:

Multivariable linear regression models predict 1-year knee pain and physical function for TKR and nonsurgical options using a database assembled from non-RCT knee osteoarthritis outcome sources with patient and clinician input; mathematical equipoise assesses balance between predicted outcomes; graphical visualizations display overlap in pain and function outcomes.