Increased femoral component flexion and no difference in slope in robotic vs computer assisted total knee arthroplasty when targeting mechanical alignment.
Antonio KlasanVictoria Anelli-MontiThomas NeriSven PutnisMaximilian ZacherlChristian KammerlanderPatrick SadoghiPublished in: The journal of knee surgery (2023)
Robotic assisted surgery (RAS) in total knee arthroplasty (TKA) is becoming popular due to better precision, when compared to other instrumentation. Although RAS has been validated in comparison with computer assisted surgery (CAS), data from clinical settings comparing these two techniques is lacking. This is especially the case for sagittal alignment. Whereas pure mechanical alignment (MA) aims for 0-3° of flexion of the femoral component and 3° of posterior slope for the tibial component, adjusted MA (aMA) mostly used with RAS allows for flexing of the femoral component for downsizing and increase of slope for an increase of the flexion gap. In the present study, we compared sagittal alignment after TKA using RAS with aMA and computer assisted surgery (CAS) targeting MA, that has been the standard in the center for more than 10 years. We analyzed a prospectively collected database of patients undergoing TKA in a single center. Femoral component flexion and tibial slope were compared for both techniques. In 140 patients, 68 CAS and 72 RAS, we found no difference in tibial slope (p=0.661), 1° median femoral component flexion p=0.023, and no difference in outliers (Femur p=0.276, Tibia p=0.289). Robotic assisted surgery slightly increases femoral component flexion, but has no influence on tibial slope, when compared to computer assisted surgery in total knee arthroplasty. If mechanical alignment is the target, robotic assisted surgery provides no benefit over computer assisted surgery for achieving the targeted sagittal alignment.
Keyphrases
- total knee arthroplasty
- minimally invasive
- coronary artery bypass
- total hip
- surgical site infection
- patients undergoing
- crispr cas
- end stage renal disease
- ejection fraction
- percutaneous coronary intervention
- chronic kidney disease
- machine learning
- newly diagnosed
- wild type
- acute coronary syndrome
- prognostic factors
- coronary artery disease
- drug delivery