Technology-assisted revision Total Knee Arthroplasty: current state, surgical technique and future perspectives
Background: Revision total knee arthroplasty (revTKA) presents significant technical challenges, including substantial bone loss, compromised anatomical landmarks, and ligamentous insufficiency. Traditional manual techniques often rely on intramedullary canal geometry for component alignment, which may result in suboptimal positioning when metaphyseal and diaphyseal centers are misaligned.
Objective: This article reviews the clinical rationale for technology-assisted revTKA, evaluates evidence-based outcomes regarding radiographic accuracy and clinical efficacy, and describes a standardized surgical technique utilizing an image-free robotic platform.
Key Points: Systematic reviews indicate that technology-assisted revTKA significantly reduces radiographic outliers in hip-knee-ankle alignment and component positioning compared to conventional methods. A fundamental paradigm shift is occurring from canal-dictated reconstruction to joint line-based restoration. This approach prioritizes anatomical joint line height and position, often utilizing short cemented stems to bypass diaphyseal constraints. The described robotic workflow employs image-free smart mapping, real-time gap balancing, and precision milling. Key technical steps include defining a "Homebase" reference before implant removal and utilizing specialized digital registration for intraoperative bone defect assessment. While radiographic precision is enhanced, current literature demonstrates comparable complication rates and patient-reported outcomes between robotic and manual techniques. Operative time remains higher for technology-assisted procedures, though the penalty decreases with advanced platforms and surgeon experience.
Conclusion: Technology-assisted platforms provide superior technical precision and facilitate anatomical joint line restoration in complex revision scenarios. Although long-term clinical superiority remains to be established, these systems offer a data-driven framework for addressing bone loss and soft tissue imbalance in revTKA.
