Can Technology Assistance be Cost Effective in TKA? A Simulation-Based Analysis of a Risk-prioritized, Practice-specific Framework

Abstract

BACKGROUND: Robotic, navigated, and patient-specific instrumentation (PSI) TKA procedures have been introduced to improve component placement precision and improve implant survivorship and other clinical outcomes. However, the best available evidence has shown that these technologies are ineffective in reducing revision rates in the general TKA patient population. Nonetheless, it seems plausible that these technologies could be an effective and cost-effective means of reducing revision risk in clinical populations that are at an elevated risk of revision because of patient-specific demographics (such as older age at index surgery, elevated BMI, and being a man). Since clinical trials on this topic would need to be very large, a simulation approach could provide insight on which clinical populations would be the most promising for analysis. QUESTIONS/PURPOSES: We conducted a simulation-based analysis and asked: (1) Given key demographic parameters characterizing a patient population, together with estimates of the precision achievable with selected forms of technology assistance in TKA, can we estimate the expected distributions of anticipated reductions in lifetime revision risk for that population and the associated improvements in quality-adjusted life years (QALYs) that would be expected to result? (2) Are there realistic practice characteristics (such as combinations of local patient demographics and capital and per-procedure costs) for which applying a per-patient risk-prioritized policy for using technology-assisted TKA could be considered cost-effective based on projected cost savings from reductions in revision rates? METHODS: We designed simulations of hypothetical practice-specific clinical scenarios, each characterized by patient volume, patient demographics, and technology-assisted surgical technique, using demographic information drawn from other studies to characterize two contrasting simulated clinical scenarios in which the distributions of factors describing patients undergoing TKA place one population at a comparatively elevated risk of revision (elevated-risk population) and the second at a comparatively reduced risk of revision (lower-risk population). We used results from previous systematic reviews and meta-analyses to estimate the implant precision in coronal plane alignment for patient-specific instrumentation, navigated, and robotic technology. We generated simulated TKA patient populations based on risk estimates from large clinical studies, structured reviews, and meta-analyses and calculated the patient-specific reduction in the revision risk and the change in QALYs attributable to the technology-assisted intervention in each of the two simulated clinical scenarios. We also incorporated a sensitivity analysis, incorporating variations in the effect size of deviations from overall coronal alignment on revision risk and difference in health state utilities acquired through a structured review process. We then simulated the outcomes of 25,000 operations per patient using the precisions associated with the conventional TKA technique, the three technology-assisted techniques, and a hypothetical technology-assisted intervention that could consistently deliver perfectly neutral overall coronal alignment, which is unachievable in practice. A risk-prioritized treatment policy was emulated by ordering the simulated patients from the highest to lowest predicted increase in QALYs, such that simulated patients who would see the greatest increase in the QALYs (and therefore the greatest reduction in lifetime revision risk) were the patients to receive technology-assisted TKA intervention in a practice. We used cost estimates acquired through a structured review process and calculated the net added costs of each of the three technology-assisted techniques as a function of the percent utilization (proportion of patients treated with technology assistance in a practice), factoring in fixed costs, per-procedure varia