Imageless, robotic-assisted total knee arthroplasty combined with a robotic tensioning system can help predict and achieve accurate postoperative ligament balance
| Autor: | Jeffrey Lawrence, Christopher Plaskos, Jeffrey DeClaire, John Keggi, Amber Randall, Sami Shalhoub |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
musculoskeletal diseases
Kinematics Robotic assisted Total knee arthroplasty Robotic-assisted Knee Joint 03 medical and health sciences 0302 clinical medicine lcsh:Orthopedic surgery Medicine Orthopedics and Sports Medicine 030212 general & internal medicine Balance (ability) Original Research Orthodontics 030222 orthopedics business.industry Soft-tissue balance musculoskeletal system lcsh:RD701-811 medicine.anatomical_structure Gaps Ligament Surgery Implant business Range of motion Prediction human activities |
| Zdroj: | Arthroplasty Today, Vol 5, Iss 3, Pp 334-340 (2019) Arthroplasty Today |
| ISSN: | 2352-3441 |
| Popis: | Background: Achieving balanced gaps is a key surgical goal in total knee arthroplasty, yet most methods rely on subjective surgeon feel and experience to assess and achieve knee balance intraoperatively. Our objective was to evaluate the ability to quantitatively plan and achieve a balanced knee throughout the range of motion using robotic-assisted instrumentation in a tibia-first, gap-balancing technique. Methods: A robotic-assisted, gap-balancing technique was used in 121 consecutive knees. After resection of the proximal tibia, a computer-controlled tensioning device was inserted into the knee joint and the pre-femoral-resection knee gaps were acquired dynamically throughout flexion under controlled load. Predicted gap profiles were used to plan the femoral implant by adjusting the implant alignment and position within certain boundaries to achieve a balanced knee throughout the range of flexion. Femoral cuts were then made according to this plan using a miniature robotic-assisted cutting guide. The tensioning device used to measure the pre-femoral-resection gaps was then reinserted into the joint to quantify the final gap balance under known tension. The final gap profiles were then compared with the predictive gap plans. Results: The overall root mean square error between the predicted and achieved gaps was 1.3 mm and 1.5 mm for the medial and lateral sides, respectively. Use of robotic assistance resulted in over 90% of knees having mediolateral balance within 2 mm across the flexion range. Gaps at 0° flexion were 2 mm smaller than the gaps at 90°. This difference decreased to less than 1 mm when comparing the tibiofemoral gaps at 10°, 45°, and 90°. Conclusions: Imageless, robotic-assisted total knee arthroplasty accurately predicts postoperative gaps before femoral resections. This allows surgeons to virtually plan femoral implant alignment and optimize gap balance throughout the range of motion. The accurate prediction of gaps throughout the arc of motion combined with precise, robotically assisted femoral resection produces accurate postoperative ligament balance consistently. Keywords: Soft-tissue balance, Total knee arthroplasty, Robotic-assisted, Prediction, Kinematics, Gaps |
| Databáze: | OpenAIRE |
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