Abstract
Obtaining accurate bone cuts based on mechanical axes and ligament balancing, are necessary for a successful total knee procedure. The OrthoSensor Tibial Trial displays on a GUI the magnitude and location of the lateral and medial contact forces at surgery. The goal of this study was to develop algorithms to inform the surgeon which bone cuts or soft tissue releases were necessary to achieve balancing, from an initial unbalanced state.
A rig was designed for lower body specimens mounted on a standard operating table. SURGICAL TESTS were then defined: Sag Test, leg supported at the foot; Dynamic Heel Push test, flexing to 120 degrees with the foot sliding along a rail; Varus-Valgus test; AP Drawer test; Internal-External Rotation test. The bone cuts were made using a Navigation system, matching the Triathlon PCL retaining knee. To determine the initial thickness of the tibial trial, the Sag Test was performed to reach 0 deg flexion. The Heel Push Test was then performed to check the AP position of the lateral and medial contacts, from which the rotational position of the tibial tray was determined. Pins were used to reproduce this position during the experiments.
SURGICAL VARIABLES were then defined, which would influence the balancing: LCL Stiffness, MCL Stiffness, Distal Femoral Cut Level, Tibial Sagittal Slope, Tibial Varus or Valgus, and AP Femoral Component Length. Balancing was defined as equal lateral and medial forces due to soft tissue tensions throughout the flexion range, equal varus and valgus stiffnesses, and no contacts closer than 10 mm to component edges. All of the above tests were then performed sequentially, and the changes in the contact force readings were considered as a signature of that Surgical Variable.
Testing was carried out on 10 full leg specimens. The Sag Test was the basic test for determining the thickness of the tibial insert. The Heel Push Test was then implemented from which force data throughout flexion was determined; followed by the Varus-Valgus Test. In a surgical case, this data will be used in a decision tree to identify which Surgical Variable required correction. In the experiments, by obtaining the above data for each SURGICAL VARIABLE in turn, we were able to determine a SIGNATURE for each SURGICAL VARIABLE. It was found that there was considerable variation in the force magnitudes between knees. However the SIGNATURES were sufficient to point to the specific SURGICAL VARIABLE requiring correction. In some knees, although there was a dominant SURGICAL VARIABLE, even after correcting for that, there was still an imbalanced state, requiring a second correction.
This research provided the fundamental principles and data for:
- 1.
Defining tests to be carried out at surgery, to obtain force data to determine the SURGICAL VARIABLE to correct.
- 2.
Defining the algorithm based on Closest Approach, for building up a database of data for predictive purposes.
- 3.
How to use the Sag Test and the Varus-Valgus test as primary indicators.
- 4.
How to use the AP Drawer test and the Internal-External Rotation test as fine tune indicators.
