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With up to 40% of patients having patellofemoral joint osteoarthritis (PFJ OA), the two arthroplasty options are to replace solely the patellofemoral joint via patellofemoral arthroplasty (PFA), or the entire knee via total knee arthroplasty (TKA). The aim of this study was to assess postoperative success of second-generation PFAs compared to TKAs for patients treated for PFJ OA using patient-reported outcome measures (PROMs) and domains deemed important by patients following a patient and public involvement meeting. MEDLINE, EMBASE via OVID, CINAHL, and EBSCO were searched from inception to January 2022. Any study addressing surgical treatment of primary patellofemoral joint OA using second generation PFA and TKA in patients aged above 18 years with follow-up data of 30 days were included. Studies relating to OA secondary to trauma were excluded. ROB-2 and ROBINS-I bias tools were used.Aims
Methods
Patient dissatisfaction is not uncommon following primary total knee arthroplasty. One proposed method to alleviate this is by improving knee kinematics. Therefore, we aimed to answer the following research question: are there significant differences in knee kinematics based on the design of the tibial insert (cruciate-retaining (CR), ultra-congruent (UC), or medial congruent (MC))? Overall, 15 cadaveric knee joints were examined with a CR implant with three different tibial inserts (CR, UC, and MC) using an established knee joint simulator. The effects on coronal alignment, medial and lateral femoral roll back, femorotibial rotation, bony rotations (femur, tibia, and patella), and patellofemoral length ratios were determined.Aims
Methods
The use of technology to assess balance and alignment during total knee surgery can provide an overload of numerical data to the surgeon. Meanwhile, this quantification holds the potential to clarify and guide the surgeon through the surgical decision process when selecting the appropriate bone recut or soft tissue adjustment when balancing a total knee. Therefore, this paper evaluates the potential of deploying supervised machine learning (ML) models to select a surgical correction based on patient-specific intra-operative assessments. Based on a clinical series of 479 primary total knees and 1,305 associated surgical decisions, various ML models were developed. These models identified the indicated surgical decision based on available, intra-operative alignment, and tibiofemoral load data.Aims
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