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Robotic Arm-assisted UKA Improves Tibial Component Alignment: A Pilot Study

  • Jess H. LonnerEmail author
  • Thomas K. John
  • Michael A. Conditt
Symposium: Papers Presented at the Annual Meetings of the Knee Society

Abstract

The alignment of the components of unicompartmental knee arthroplasty (UKA) reportedly influences outcomes and durability. A novel robotic arm technology has been developed with the expectation that it could improve the accuracy of bone preparation in UKA. During the study period, we compared the postoperative radiographic alignment of the tibial component with the preoperatively planned position in 31 knees in 31 consecutive patients undergoing UKA using robotic arm-assisted bone preparation and in 27 consecutive patients who underwent unilateral UKA using conventional manual instrumentation to determine the error of bone preparation and variance with each technique. Radiographically, the root mean square error of the posterior tibial slope was 3.1° when using manual techniques compared with 1.9° when using robotic arm assistance for bone preparation. In addition, the variance using manual instruments was 2.6 times greater than the robotically guided procedures. In the coronal plane, the average error was 2.7° ± 2.1° more varus of the tibial component relative to the mechanical axis of the tibia using manual instruments compared with 0.2° ± 1.8° with robotic technology, and the varus/valgus root mean square error was 3.4° manually compared with 1.8° robotically. Further study will be necessary to determine whether a reduction in alignment errors of these magnitudes will ultimately influence implant function or survival.

Level of Evidence: Level III, therapeutic study. See Guidelines for Authors for a complete description of levels of evidence.

Keywords

Tibial Component Mechanical Axis Preoperative Plan Unicompartmental Knee Arthroplasty Computer Navigation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© The Association of Bone and Joint Surgeons® 2009

Authors and Affiliations

  • Jess H. Lonner
    • 1
    Email author
  • Thomas K. John
    • 2
  • Michael A. Conditt
    • 3
  1. 1.Booth Bartolozzi Balderston OrthopaedicsPennsylvania HospitalPhiladelphiaUSA
  2. 2.Department of Orthopaedic SurgeryAlbert Einstein Medical CenterPhiladelphiaUSA
  3. 3.Clinical Research, Mako Surgical CorpFt LauderdaleUSA

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