Predicting patellar kinematics and contact forces after TKA: a simulation study on quadriceps malalignment

Patellofemoral complications remain a frequent cause of dissatisfaction following total knee arthroplasty (TKA), despite continuous advances in implant design and surgical techniques. Abnormal patellar tracking may lead to increased contact stresses, instability, and anterior knee pain, particularly in patients with preoperative quadriceps malalignment. Subject-specific assessment tools are therefore needed to improve surgical planning and postoperative outcomes.

A subject-specific multibody dynamics (MBD) framework was developed to simulate patellofemoral mechanics following TKA. A single representative patient with pronounced quadriceps malalignment was selected for this proof-of-concept study to enable detailed subject-specific modeling and experimental validation under controlled conditions. The effects of femoral and tibial component positioning, including internal/external rotation and varus/valgus alignment, were systematically evaluated. Numerical predictions were validated using a sensorized 3D-printed knee rig.

Quadriceps malalignment was the primary determinant of patellar instability, leading to increased lateralization (bisect offset index up to 0.90, 25% higher than the reference condition), elevated peak contact forces (11% increase), and near-dislocation during knee extension. Valgus alignment increased peak contact forces by up to 3.6% (femur) and 2.9% (tibia) and further exacerbated lateral patellar shift, whereas internal rotation increased peak contact forces by ~ 2.2% and worsened tracking. In contrast, varus alignment and external rotation produced moderate reductions in contact forces (up to - 2.5%) and partial improvement in patellar alignment. The average total computation time was 48 s, and the simulated kinematics and contact forces showed strong agreement with the experimental measurements.

The proposed computational framework enables rapid, subject-specific evaluation of patellofemoral mechanics and implant positioning, incorporating individual anatomical and alignment characteristics. Although demonstrated here in a proof-of-concept setting, its computational efficiency and predictive capability suggest potential for future use in intraoperative assessment and last-minute surgical optimization in TKA.