Failure modes in malrotated total knee replacement

Widening of tibial resection boundaries increases the rate of femoral component valgus and internal rotation in functionally aligned TKA

PURPOSE

The purpose of this study was to investigate the influence of increasing the tibial boundaries in functional alignment on femoral component orientation in total knee arthroplasty (TKA).

METHODS

A retrospective review of a database of robotic-assisted TKAs using a digital joint tensioning device was performed (BalanceBot®; Corin). A total of 692 TKAs with correctable deformity were included. Functional alignment with a tibia-first balancing technique was simulated by performing an anatomic tibial resection to recreate the native medial proximal tibial angle within certain boundaries (A, 87-90°; B, 86-90°; C, 84-92°), while accounting for wear. After balancing the knee, the resulting amount of femoral component outliers in the coronal and axial plane was calculated for each group and correlated to the coronal plane alignment of the knee (CPAK) classification.

RESULTS

The proportion of knees with high femoral component varus (>96°) or valgus (<87°) alignment increased from 24.5% (n = 170) in group A to 26.5% (n = 183) in group B and 34.2% (n = 237) in group C (p < 0.05). Similarly, more knees with high femoral component external rotation (>6°) or internal rotation (>3°) were identified in group C (33.4%, n = 231) than in group B (23.7%, n = 164) and A (18.4%, n = 127) (p < 0.05). There was a statistically significant (p < 0.01) overall increase in knees with both femoral component valgus <87° and internal rotation >3° from group A (4.0%, n = 28) to B (7.7%, n = 53) and C (15.8%, n = 109), with CPAK type I and II showing a 12.9- and 2.9-fold increase, respectively.

CONCLUSION

Extending the tibial boundaries when using functional alignment with a tibia-first balancing technique in TKA leads to a statistically significant higher percentage of knees with a valgus lateral distal femoral angle < 87° and >3° internal rotation of the femoral component, especially in CPAK type I and II.

LEVEL OF EVIDENCE

Level IV.

Prediction of knee biomechanics with different tibial component malrotations after total knee arthroplasty: conventional machine learning vs. deep learning

The precise alignment of tibiofemoral components in total knee arthroplasty is a crucial factor in enhancing the longevity and functionality of the knee. However, it is a substantial challenge to quickly predict the biomechanical response to malrotation of tibiofemoral components after total knee arthroplasty using musculoskeletal multibody dynamics models. The objective of the present study was to conduct a comparative analysis between a deep learning method and four conventional machine learning methods for predicting knee biomechanics with different tibial component malrotation during a walking gait after total knee arthroplasty. First, the knee contact forces and kinematics with different tibial component malrotation in the range of ±5° in the three directions of anterior/posterior slope, internal/external rotation, and varus/valgus rotation during a walking gait after total knee arthroplasty were calculated based on the developed musculoskeletal multibody dynamics model. Subsequently, deep learning and four conventional machine learning methods were developed using the above 343 sets of biomechanical data as the dataset. Finally, the results predicted by the deep learning method were compared to the results predicted by four conventional machine learning methods. The findings indicated that the deep learning method was more accurate than four conventional machine learning methods in predicting knee contact forces and kinematics with different tibial component malrotation during a walking gait after total knee arthroplasty. The deep learning method developed in this study enabled quickly determine the biomechanical response with different tibial component malrotation during a walking gait after total knee arthroplasty. The proposed method offered surgeons and surgical robots the ability to establish a calibration safety zone, which was essential for achieving precise alignment in both preoperative surgical planning and intraoperative robotic-assisted surgical navigation.