New Technology and Surgical Technique in TKA

Background

As the term “surgical navigation” suggests, the fundamental aim of computer-aided guidance technologies is to provide the operating surgeon with information defining the ideal or intended position and orientation of the prosthetic components with respect to the bony surfaces visible via the surgical incision. To make this possible, a predefined reference frame common to both the skeleton and implant is matched to the accessible features of the femur and tibia. This registration process can be performed using any anatomical or surface features digitized intraoperatively and any kinematic data inferred through the relative motion of the femur and the tibia. The simplest form of registration is performed using a calibrated probe to acquire the spatial coordinates of anatomical landmarks and bony surfaces required to define the reference frame within the surgical site. In theory this allows rapid registration with a minimum of data collection. However, most landmarks (e.g., the femoral epicondyles) are not singular points but are geometrical constructs (e.g., the center of a partial sphere), and so the accuracy and reproducibility of the registration process are dependent on the number of points collected and their relative spacing. For this reason, more sophisticated registration algorithms are “shaped-based,” meaning that patches of data acquired intraoperatively from landmarks and bony surfaces are matched to the corresponding areas of bony models by statistical shape matching or through reference to large atlases of similar bones with precisely defined surfaces and anatomical axes. Even though both of these methods may be performed without preoperative imaging (image-free), the most accurate registration routines are based on patient-specific models derived from CT and/or MRI (image-based navigation). When these models have been created through segmentation and reconstruction of the imaging data, reference axes can be calculated and registration undertaken by performing standard shape-matching procedures.

Computer-Assisted Surgical Systems

It has been previously demonstrated that two of the most important factors associated with the longevity of TKA are accurate component placement and restoration of the mechanical alignment of the lower extremity. Deviation of the mechanical axis by more than 3 degrees in the coronal plane has been correlated with accelerated component wear, loosening, instability, and poor implant survivorship. Although traditional intramedullary and extramedullary cutting guides are readily available and easy to use, they have been associated with deviations greater than 3 degrees from planned mechanical alignment placement in up to 30% of procedures. These errors are magnified in cases complicated by extraarticular deformity and unusual patient morphology. This experience has led to growing interest in computer-assisted surgery (CAS) over the past three decades to improve the accuracy and precision of bony resection, implant placement, and ligamentous balancing. The first case of TKA in which computer navigation was used for the entire surgical procedure was performed by Krackow et al. in August 1997. Currently, a wide variety of technologies exist to achieve these goals; these include patient-specific instrumentation, computer-assisted navigation systems, and, most currently, robotic systems.

CAS systems may be divided into three categories as defined by Picard et al. : active robotic systems that are fully automated in the performance of surgical tasks (e.g., TSolution One, THINK Surgical), semiactive robotic systems that do not perform surgical tasks but may limit placement of tools or provide haptic feedback (e.g., Mako, Stryker), and passive systems that simply display alignment information (e.g., OrthAlign).

CAS navigation systems offer preoperative dynamic assessment of deformity, alignment, and kinematics and, as such, are classified as passive computer-assisted devices. Historically, navigation systems have been image-based (i.e., using imaging data derived from fluoroscopy, CT, or other modalities) and image-free (i.e., based on preconstructed computer models of knee anatomy). Although image-based software remains prevalent in robotic-assisted systems, it quickly fell out of favor in CAS navigation because of its increased complexity and the associated financial burden of preoperative CT or MRI scans. Currently, imageless systems are the most widely studied and accepted for CAS navigation; they use kinematic or anatomical data that are collected intraoperatively to direct placement of cutting guides during implantation of off-the-shelf total knee implant designs ( Table 10.1 ).

Each navigation system has three basic components: a computer, a camera and trackers, and a direct line of sight between the arrays of trackers attached to the skeleton and a remotely positioned camera to measure the position and orientation of the femur and tibia to assess lower extremity movement, joint alignment, and instrument positioning in real time. Trackers are fixed to the patient’s femur and tibia often by pins, and a series of relevant anatomical landmarks are collected directly on the patient and processed through computer software to create a dynamic reference frame. The position of cutting guides and instruments are subsequently compared with this frame to enable the surgeon to customize the bone resection planes and final positioning of the implanted components.

Several tracking methods have been proposed based on electromagnetic, ultrasound, and stereoscopic technologies, but infrared optoelectronic tracking systems are the most common due to their reliability and ease of use. Validation studies of optoelectronic tracking systems have demonstrated high reliability and accuracy, with errors as low as 0.25 mm for translation and 1 degree for rotation, although measurement accuracy is correlated with the spatial location of the camera. Active infrared light-emitting diode (LED) tracking uses an array of four to six diodes that are alternatively illuminated in a pattern detected by a charge-coupled device (CCD) camera ( Fig. 10.1 ). Although accurate, active infrared technology relies on reusable electronic LED emitters within the surgical field that are reported to increase tracker weight, which leads to errors because of motion at the bony fixation point. Additional costs are also incurred due to battery requirements within each tracker. Alternatively, passive infrared tracking uses reflective spheres or discs that are affixed to a metal frame in a unique shape and registered by an infrared camera ( Fig. 10.2 ). Although this method greatly lessens tracker weight, major costs are associated with the disposable reflective spheres/discs, and contamination of their reflective coating with tissue or bodily fluid markedly affects their accuracy.

Accuracy of CAS Systems in Terms of Limb and Component Alignment

Although there is continued debate regarding the optimal limb and component alignment in TKA, numerous studies have demonstrated that CAS navigation systems are more accurate, precise, and reproducible compared with conventional manual instrumentation using intramedullary or extramedullary guides. Navigation systems have been shown to significantly improve postoperative mechanical limb alignment compared with traditional mechanical instrumentation. In a metaanalysis performed by Mason et al., deviations in knee alignment greater than 3 degrees were reported in 9% of navigated TKAs versus 32% with conventional instrumentation. Similar improvements were also seen in the alignment of the femoral and tibial components themselves ( Fig. 10.3 ). Coronal malalignment ≥3 degrees was seen in 5% of navigated femoral components and 4% of navigated tibial components versus 16% and 11%, respectively, in conventional TKA. Sagittal femoral and tibial component alignment demonstrated greater discrepancies from target positioning, with both methods of component placement. In this case, malalignment ≥3 degrees occurred in 26% femoral and 18% of tibial components with conventional instrumentation compared with 8% and 12%, respectively, using CAS navigation.

The effect of CAS navigation on rotational alignment has been less consistent. This is tied to the dependence of navigation systems on intraoperative identification of reference axes defining neutral rotation of the femur and the tibia. In separate studies analyzing postoperative CT images of TKA Chauhan et al. and Stöckl et al. demonstrated significant improvement in component rotation and decreased femoral/tibial rotational mismatch in cases performed with CAS navigation compared with conventional instrumentation. However, cadaveric studies reported by Siston et al. showed high variability in the rotational alignment of both the femoral and tibial components, regardless of whether navigation was used. Because the registration process is dependent on the precise location of bony landmarks, variations in spatial location arising from visual and tactile cues, as commonly seen with the medial epicondylar sulcus, may have a profound effect on final surgical accuracy.

Even though CAS navigation is commonly used in some countries such as Australia, this technology has not received widespread global adoption in most of the world because of a variety of economic and ergonomic concerns. As with any new technology, the financial costs associated with CAS navigation were a primary deterrent to its initial adoption. Typically, these included the capital cost of the navigation system itself, the per-case cost of disposables (e.g., retroreflective arrarys), and the recurring cost of software and maintenance. This does not take into account indirect costs attributable to the increased duration of operative procedures, increased surgical instrument preparation and sterilization, and the need for additional intraoperative surgical assistance. An analysis performed by Novak et al. in 2007 estimated that CAS navigation led to an increase in upfront costs of approximately $1500 per case. However, they concluded that CAS navigation also had the potential to be a cost-effective or even cost-saving addition to TKA if improved component and limb alignment led to increased survivorship and reduced revision case load.

Regardless of financial barriers to adoption, ergonomic challenges have also been created by the bulk of the consoles, the line-of-sight limitation, and the addition of extra instruments. Furthermore, widespread acceptance was also deterred by concerns regarding the length of the CAS learning curve, coupled with the results of early studies reporting an increased incidence of complications such as fractures and infection associated with tracker pin sites. Even though later evidence did not confirm the initial concerns regarding increased complication rates, skepticism was supported by other studies reporting limited advantages of CAS navigation compared with conventional instrumentation. Contrary to individual reports within the literature, a metaanalysis authored by Bauwens et al. in 2007 demonstrated that CAS navigation did not significantly improve mechanical axis alignment, despite an average increase in operative time of 23%. This echoes the conclusions of numerous publications that have reported increases of 10 to 20 minutes in the duration of primary TKA procedures on average, furthering concerns for increased complication and infection risk and financial impact.

To overcome some of the limitations associated with large-console CAS navigation systems, newer handheld accelerometer and gyroscope-based navigation systems have gained popularity. These imageless portable devices are supplied in a disposable, single-use format, and they use dynamic motion sensors to reference anatomical landmarks. Built-in displays provide digital feedback without the need for additional large, capital-intensive equipment within the operating room. Furthermore, these systems avoid any line-of-sight issues and capital equipment and set-up costs, and they provide similar operative times and instrumentation to conventional methods. However, these devices have a number of drawbacks, including significant cost and the inability to assess femoral and tibial component rotation.

Accelerometer-based Navigation Systems

Two accelerometer-based navigation (ABN) systems that are currently commercially available are the iASSIST (Zimmer Biomet, Warsaw, IN) and the OrthAlign (OrthAlign, Inc., Aliso Viejo, CA). Both systems determine the center of rotation of the hip and the mechanical axis of the lower extremity to establish appropriate resection planes for the distal femur and proximal tibia. The iASSIST system uses small, disposable accelerometer-equipped “pods” and a local wireless network to assess limb alignment and direct cutting guides for optimal component positioning ( Fig. 10.4 ).

In a prospective randomized controlled trial by Kinney et al. iASSIST ABN demonstrated significant improvement over conventional instrumentation, with 4% of patients having a postoperative limb alignment of >3 degrees from the neutral mechanical axis compared with 36% with conventional instruments. Furthermore, studies have demonstrated no significant difference in the accuracy and precision of the iASSIST ABN compared with large-console CAS systems, with no increase in operative time compared with conventional methods. Although no initial capital equipment costs are incurred, a cost-based analysis for iASSIST ABN performed by Goh et al. in 2016 found that an added cost of ABN was approximately $1000 per operation compared with the previously reported $1500 per case cost calculated by Novak et al. for CAS navigation.

An alternative ABN system, the OrthAlign, provides dynamic measurement of the alignment of the distal femoral and tibial cutting blocks using a reference sensor and femoral and tibial jigs. The results are displayed on a display console within the single-use handheld device ( Fig. 10.5 ).

This device has the added benefit of using open-platform software that allows it to be used with any design of knee prosthesis. A number of studies have demonstrated excellent accuracy and precision with the OrthAlign system without the need for additional surgical time, unlike large-console–based CAS navigation systems. In a retrospective analysis of cases performed with the OrthAlign ABN Nam et al. reported 98% of tibial resections within 90 ± 2 degrees for coronal alignment and 96% within 3 ± 2 degrees for sagittal alignment. In addition, 96% of cases were within 90 ± 2 degrees for distal femoral alignment and 94% were within 0 ± 3 degrees for overall mechanical axis. In a subsequent study comparing OrthAlign with a large-console imageless CAS system (AchieveCAS; Smith & Nephew, Memphis, TN), the OrthAlign ABN was found to be as accurate as, if not more so than, a large-console CAS system in regard to overall lower extremity, femoral, and tibial component alignment, particularly in valgus knees.

Clinical Performance of Surgical Navigation Systems in TKA