Aims. A comprehensive classification for coronal lower limb alignment with predictive capabilities for knee balance would be beneficial in total knee arthroplasty (TKA). This paper describes the Coronal Plane Alignment of the Knee (CPAK) classification and examines its utility in preoperative soft tissue balance prediction, comparing kinematic alignment (KA) to mechanical alignment (MA). Methods. A radiological analysis of 500 healthy and 500 osteoarthritic (OA) knees was used to assess the applicability of the
Aims. The Coronal Plane Alignment of the Knee (CPAK) classification has been developed to predict individual variations in inherent knee alignment. The impact of preoperative and postoperative
The aim of this study was to investigate the distribution of phenotypes in Asian patients with end-stage osteoarthritis (OA) and assess whether the phenotype affected the clinical outcome and survival of mechanically aligned total knee arthroplasty (TKA). We also compared the survival of the group in which the phenotype unintentionally remained unchanged with those in which it was corrected to neutral. The study involved 945 TKAs, which were performed in 641 patients with primary OA, between January 2000 and January 2009. These were classified into 12 phenotypes based on the combined assessment of four categories of the arithmetic hip-knee-ankle angle and three categories of actual joint line obliquity. The rates of survival were analyzed using Kaplan-Meier methods and the log-rank test. The Hospital for Special Surgery score and survival of each phenotype were compared with those of the reference phenotype with neutral alignment and a parallel joint line. We also compared long-term survival between the unchanged phenotype group and the corrected to neutral alignment-parallel joint line group in patients with Type IV-b (mild to moderate varus alignment-parallel joint line) phenotype.Aims
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The surgical target for optimal implant positioning in robotic-assisted total knee arthroplasty remains the subject of ongoing discussion. One of the proposed targets is to recreate the knee’s functional behaviour as per its pre-diseased state. The aim of this study was to optimize implant positioning, starting from mechanical alignment (MA), toward restoring the pre-diseased status, including ligament strain and kinematic patterns, in a patient population. We used an active appearance model-based approach to segment the preoperative CT of 21 osteoarthritic patients, which identified the osteophyte-free surfaces and estimated cartilage from the segmented bones; these geometries were used to construct patient-specific musculoskeletal models of the pre-diseased knee. Subsequently, implantations were simulated using the MA method, and a previously developed optimization technique was employed to find the optimal implant position that minimized the root mean square deviation between pre-diseased and postoperative ligament strains and kinematics.Aims
Methods