A geometric ratio to predict the flexion gap in total knee arthroplasty

Abstract

Measured resection is a common technique for obtaining symmetric flexion and extension gaps in posterior‐stabilized (PS) total knee arthroplasty (TKA). A known limitation of measured resection, however, is its reliance on osseous landmarks to guide bone resection and component alignment while ignoring the geometry of the surrounding soft tissues such as the medial collateral ligament (MCL), a possible reason for knee instability. To address this clinical concern, we introduce a new geometric proportion, the MCL ratio, which incorporates features of condylar geometry and MCL anterior fibers. The goal of this study was to determine whether the MCL ratio can predict the flexion gaps and to determine whether a range of MCL ratio corresponds to balanced gaps. Six computational knee models each implanted with PS TKA were utilized. Medial and lateral gaps were measured in response to varus and valgus loads at extension and flexion. The MCL ratio was related to the measured gaps for each knee. We found that the MCL ratio was associated with the flexion gaps and had a stronger association with the medial gap (β = −7.2 ± 3.05, P < .001) than with the lateral gap (β = 3.9 ± 7.26, P = .04). In addition, an MCL ratio ranging between 1.1 and 1.25 corresponded to balanced flexion gaps in the six knee models. Future studies will focus on defining MCL ratio targets after accounting for variations in ligament properties in TKA patients. Our results suggest that the MCL ratio could help guide femoral bone resections in measured resection TKA, but further clinical validation is required.

Simulation of preoperative flexion contracture in a computational model of total knee arthroplasty: Development and evaluation

“…Computational models derived from six cadaveric knees were virtually implanted with a PS TKA (Optetrak Logic ® , Exactech, Gainesville, FL, USA) following our previously published framework ( Elmasry et al, 2019 , 2020 ; Kia et al, 2017 , 2018 ). The model development pipeline included: segmenting and reconstructing the entire femoral and tibial bony geometries in 3D from computed tomography (CT) scans; creating bone-fixed and knee coordinate systems following the description of Grood and Suntay (1983) ; simulating bony cuts and implant positioning using measured resection and posterior referencing ( Elmasry et al, 2019 ); incorporating ligament models that included the insertion locations and mean stiffnesses of the collateral and capsular structures ( Kia et al, 2018 ); and adding a compliant contact formulation ( Kia et al, 2017 ) ( Supplementary material 1 and Fig.…”

“…1 ). Ligament stiffnesses and insertion sites were defined using mean values from the literature and data from our previous studies ( Elmasry et al, 2020 ; Kia et al, 2016 ). The slack lengths ( l 0 ) of the MCL, POL, LCL, and FFL were calibrated to produce forces equal to those measured in cadaveric robotic experiments conducted in our laboratory.…”

“…These clinical and cadaveric studies, however, lack the ability to precisely define important variables including the degree of flexion contracture, the amount of additional bone resection, or the level of ligament release while controlling for potential confounding factors such as variability in bony cuts, implant placement, and ligament properties. In contrast, computational models enable systematic comparison among surgical techniques while controlling for the effects of these confounding factors ( Elmasry et al, 2020 ; Erdemir et al, 2019 ; Kia et al, 2018 ). A computational model incorporating ligament properties associated with preoperative flexion contracture would enable the study of different correction procedures to restore knee extension.…”