A theoretical bayesian decision tree model for prioritizing RA-TKA under constrained resources robotic-assisted versus conventional total knee arthroplasty.

Robotic-assisted total knee arthroplasty (RA-TKA) has demonstrated superior alignment accuracy and reduced variability compared to conventional techniques. However, the clinical indications for RA-TKA remain poorly standardized, often driven by surgeon preference or institutional availability rather than patient-specific complexity. There is a need for an objective model to guide case selection and promote rational use of robotic systems. We developed a theoretical Bayesian decision tree model based on literature-derived conditional probabilities. The model integrates clinical variables (age, body mass index [BMI], coronal alignment, deformity severity, ASA classification) and system-level factors (robotic access) to estimate the posterior probability of benefit from RA-TKA. Simulated clinical scenarios and a Monte Carlo simulation with 10,000 virtual patients were used to evaluate model behavior and sensitivity. Coronal deformity ≥ 10° and BMI > 35 were the most influential variables, while robotic access acted as a binary gatekeeper. In simulated scenarios, posterior RA-TKA recommendation probabilities ranged from 14.6% to 89.2%, depending on complexity and access. The Monte Carlo simulation yielded a mean recommendation probability of 53.7% (SD 21.2%), with strong discriminatory performance. Sensitivity analysis confirmed the robustness of the model across input variations. This Bayesian model provides a transparent, interpretable framework for RA-TKA indication. It supports evidence-based, individualized decision-making and offers a platform for standardizing the use of robotic technology. Future validation with institutional and multicenter datasets may allow for integration into clinical workflows and development of guideline-driven algorithms for robotic arthroplasty.