The critical determinants of successful, durable total knee arthroplasty (TKA) are correct positioning of the implanted components, acceptable alignment of the knee with weight-bearing, and restoration of normal stability during functional activities without the need for excessive muscular activation. Although the ideal values of component position, joint alignment, and knee stability remain topics of intense debate, there remains a general consensus that the success of surgeons in achieving the target values of each of these parameters is highly variable. Some authors have speculated that this variability, and the disturbing incidence of outliers, contributes to the incidence of patient dissatisfaction with the outcome of TKA. This has led to the increasing adoption of computer-based technologies to augment or replace conventional manual instruments to provide confirmation of each patient’s component placement and knee function within the operative setting. The case for computer assistance in TKA has been strengthened by the emergence of joint registries that highlight the true incidence of revision of joint arthroplasties and the striking effect of the age of the patient on the durability of the procedure. Joint registry data also show increased longevity of cases performed using computer navigation compared with conventional instrumentation. This supports the adoption of computer assistance in younger and more active patients in whom the need for correct positioning, alignment, and stability appears to be even more critical.
The emergence of computer-assisted technologies in TKA has also created opportunities for active instrumentation to provide feedback to the surgeon based on intraoperative measurements derived from each patient’s knee. This information allows preoperative plans to be modified in response to the demands of each patient’s soft tissue envelope and bony anatomy. This approach to personalization of each TKA is most evident in cases performed with sensor-based “smart” tibial trials and robotic surgical systems that allow the surgeon to measure compartmental loading, predict ligamentous laxity, and perform intraoperative correction if necessary.
This chapter reviews computer-based technologies that are commercially available to measure component position, joint alignment, knee stability, and compartment pressures. In addition, the chapter will review advances in robotically assisted TKA in which navigation and machining of the bony surfaces are integrated to further improve the accuracy and reliability of component placement and alignment.
Surgical Navigation Systems in Total Knee Arthroplasty
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 ).
|NaviPro Knee||Kinamed Incorporated|
|AchieveCAS||Smith & Nephew|
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
Clinical Effectiveness (Complications and Survivorship)
The widespread use of navigation systems in knee arthroplasty has been slowed by the occurrence of specific complications associated with their use. These have been especially noted early in surgeons’ learning curves even though inconsistent data are quite prevalent. Particular intraoperative complications associated with navigated arthroplasty include an increased incidence of femoral notching, periprosthetic fracture of the femur and tibia around pin insertion sites, increased surgical times, and procedure blood loss together with increased transfusion rates.
Anterior femoral notching in TKA is an avoidable complication in which the anterior femoral cut leads to violation of the anterior femoral cortex. Femoral notching in most studies has no major long-term effects on knee arthroplasty, but some studies have noted an increased risk of future periprosthetic fracture around a TKA femoral component.
Factors that increase the risk of femoral notching include inappropriate femoral component sizing and rotation and inappropriate positioning on sagittal plane, leading to posterior displacement of the femoral component. In traditional navigation systems these variables are determined preoperatively and executed intraoperatively with expected reproducibility. Lee et al. retrospectively reviewed 148 TKAs in 130 patients and compared conventional with navigated arthroplasty. They noted an increased rate of anterior femoral notching in the navigated cohort (5.7% vs. 16.7%, P = .037). A similar difference in the incidence of anterior notching was reported by Kim et al. who, in a randomized control trial, observed notching in 11 TKAs (4%) performed with computer-assistance versus none in cases performed with conventional instrumentation ( P = .046). The increased risk of femoral notching in navigated arthroplasties suggested less intraoperative awareness by the surgeon during anterior femoral cut and preparation or a combination of factors not noted by the authors of the available studies.
Acceptable durations of operative procedures and utilization of operating rooms (turnover) are of utmost importance to hospital efficiency and the productivity of busy surgical practices. Another significant barrier to widespread adoption of navigation or robotic technology is the perceived loss of operating room (OR) efficiency because of increased operative times. This is more acute during the early part of the surgeon’s learning curve and tends to normalize with increased familiarity with the CAS system. Historically, increased surgical times have correlated with increased risk of infection, particularly when the length of the procedure exceeds 90 to 110 minutes. The increased time required for placement of arrays, presurgical registration, and intraoperative planning can also extend the length of the procedure. However, literature reports on the effect of CAS on operative duration and increased surgical times and complication rates are quite inconsistent. For example, Alcelik et al. showed that surgical navigation improved component positioning and did increase operative duration (mean difference of 32 minutes; 95% confidence interval [CI], 20.97 to 43.15; P < .00001), but it had no effect on the rate of complications.
Most CAS navigation systems require attachment of tracker pins to the femur and tibia. At the case conclusion, tracking pin removal can introduce the possible risk of periprosthetic fracture because of the stress riser effect on the bone, though the reported incidence is very low. A similar complication has been documented on medial unicompartmental arthroplasty, with extraarticular jig pins leading to stress fracture of the medial tibial plateau. Management of these complications can be quite challenging, and in a patient expecting rapid recovery and with high activity expectations they can certainly prove devastating. Periprosthetic mid femur and tibia fractures have also been documented and can potentially occur around navigation tracker pin sites. By placing the trackers intraarticularly, a second incision is not needed, and muscle violation by the pin track and the stress riser can also be decreased. Smaller pins can potentially decrease the stress procedure on the bone, but they can lead to instability of the pins and compromise of the navigation workflow. Most reports of these tracker-induced fractures are in small case series and case reports in which the treatment consisted of protected weight-bearing and closed management.
As CAS TKA does not require violation of the femoral canal, decreased surgical blood loss is an expected benefit, though the published evidence is mixed. Whereas some studies show a trend toward less intraoperative blood loss and less postoperative drainage in patients undergoing navigated TKA, others show no change in the rate of transfusion. In a registry study with 10,034 patients the group of 2008 patients treated with navigated TKA and the group of 8026 patients treated with conventional TKA showed no difference in surgical time but a statistically significant difference in perioperative transfusion rates (15.5% conventional vs. 9.7% navigated, P < .001).
Long-term revision rates between conventional and navigated TKA are certainly inconsistent in the literature. One study based on data collected by the Australian Orthopedic Association National Joint Replacement Registry examined the survivorship of navigated TKA in patients younger than 65 years of age and reported a cumulative revision rate of 7.8% (95% CI, 7.5 to 8.2) at 9 years postoperatively for conventional TKA compared with 6.3% (95% CI, 5.5 to 7.3) for navigated TKA. In a retrospective study of 1121 consecutive primary TKA Schurr et al. noted that all case revision rates averaged 4.7% for conventional TKA and 2.3% for navigated procedures ( P = .012), with a dramatic reduction in the rate of aseptic revision (1.9% vs. 0.1%, P = .024). However, other studies report very minimal or no difference in the durability of conventional and navigated TKA.
Patient Outcomes (Pain, Function, and Satisfaction)
Long-term functional success after TKA has been defined in terms of correction of preoperative deformity, optimal radiographic implant positioning, and return to pain-free, age-related function. The widespread adoption of patient-reported outcome measures (PROMs) in total joint arthroplasty now enables surgeons to measure the pain, function, and quality of life experienced by their patients in an efficient manner. Moreover, PROMs data provide a basis for the implementation of patient-centered care and evidence-based decision making. PROMs also have the potential to serve as a measure of surgeon quality and could dictate future reimbursement algorithms (quality of service).
A patient’s perception of the success of the procedure and their satisfaction with the outcome is tied to pain perception and return to function in the immediate postoperative period and afterward. This section will evaluate whether computer-assisted navigation has shown progress in positively or negatively affecting immediate postoperative pain and implant- or surgery-related complications and long-term patient-reported outcomes after TKA.
Despite the great promise of CAS systems in improving the accuracy and reproducibility of TKA, showing any difference in functional outcome, PROMs, or pain between conventional and navigated TKA. At 15-year follow-up, in a randomized trial of patients with same-day bilateral knee arthroplasties, Kim et al. reported on 296 patients and showed no difference in functional scores, postoperative arc of motion, and revision rate between conventional and navigated knees. No changes in radiographic alignment parameters were shown between the groups. Similar conclusions were reported in two studies with functional outcome measures as their endpoint; Harvie et al. and Spencer et al. were unable to show differences in functional outcomes even though statistically significant differences in overall alignment were noted at 5 years. This lack of clinical outcome difference in most likely secondary to the long-standing success rate of TKA.
Robotic Systems in TKA
Robotic surgical systems can be divided into three categories based on the degree of control provided to the operating surgeon: active and autonomous, semiactive, and passive. In active systems the robot autonomously executes the preplanned surgical procedure without physical guidance from the surgeon, who assumes the role of an observer monitoring the progress of the robotic device. Semiactive systems provide intraoperative feedback to the surgeon during the operation, usually in the form of auditory (beeping), tactile (vibration), and visual information, as a means of assisting the surgeon in positioning the components more accurately while avoiding overresection of bone stock. In passive systems the robotic system provides guidance and positions instruments (e.g., cutting blocks) while all or part of the procedure is carried out under the surgeon’s direct control.
Robotic systems are also differentiated by the source of data used to create the individualized surgical plan. These are classified as either image-based or imageless. In image-based systems a patient-specific three-dimensional (3D) computer model of the patient’s bony anatomy is created preoperatively through reconstruction of data derived from CT or MRI ( Fig. 10.6 ). During preoperative planning the surgeon uses these models to determine bone resection depth, component size, implant alignment, and so on (see Fig. 10.6 ). To enable replication of these resections during surgery, the computer models and the patient’s joint surfaces must be spatially matched (i.e., registered) before the robot can execute the preoperative plan. In imageless systems these steps are performed intraoperatively by predicting the surface coordinates of the femur and tibia using accessible areas of the bony surfaces and various anatomical landmarks. This information is used as a basis for predicting the patient’s bony morphology by scaling preexisting detailed anatomical models that have been collected preoperatively.
Systems Approved for TKA
Many robotic TKA platforms have been developed and used in clinical settings worldwide ( Table 10.2 ). This has occurred in conjunction with a dramatic increase in peer-reviewed publications related to robotic technology in TKA. In the United States the Food and Drug Administration (FDA) has given approval for the use of a number of new robotic systems for TKA with varying features and functions. Some of the common contemporary robotic systems used for TKA include (1) Mako (Stryker, Mahwah, NJ), (2) OMNIBotics (Corin Group, Cirencester, UK), (3) NAVIO (Smith & Nephew, London, UK), (4) ROSA (Zimmer Biomet, Warsaw, IN), (5) VELYS (Johnson & Johnson/DePuy Synthes, Warsaw, IN), and (6) TSolution One (ROBODOC) (THINK Surgical, Inc., Fremont, CA).
|Robotic System||Resection Type||Preop Imaging||Control||FDA Approval|
|Mako||Semiactive||Pre-op CT||Haptic feedback||Aug. 2015|
|OMNIBotics||Passive||Imageless||Manual (robotically positioned cutting guide)||Sept. 2017|
|NAVIO||Semiactive||Imageless||Robotic-assisted nonhaptic||Jun. 2017|
|ROSA||Semiactive||Pre-op X-ray or imageless||Manual (robotically positioned cutting guide)||Jan. 2019|
|ROBODOC/TSolution One||Active||Pre-op CT||Autonomous control||Oct. 2019|
|Velys||Semiactive||Imageless||Robotic-assisted nonhaptic||Jan. 2021|
|CORI||Semiactive||Imageless||Robotic-assisted nonhaptic||Nov. 2021|
|CASPAR||Active||Pre-op CT||Autonomous control||NA|
The next section will review the individual features of the robotic platforms approved for clinical use in TKA.
Mako Robotic Arm System
The Mako robotic arm system (originally named Rio) is an image-based semiactive system widely used in clinical practice for robotic-assisted unicompartmental knee arthroplasty (UKA) and total hip arthroplasty ( Fig. 10.7 ). Mako TKA system was granted 510(k) market clearance by the FDA in 2015. As an image-based robotic system, a preoperative patient-specific model derived from a CT scan is used to plan component sizing and implant positioning. Intraoperatively, the patient’s limb is secured within a mobile leg holder boot, and the preoperative models and the planned implant components are registered to the patient’s anatomy. The patient’s knee is then moved through the arc of flexion from 0 to 90 degrees. During this maneuver, shims are placed within the medial and lateral compartments to determine the correct adjustment of the native joint space required for correct alignment and joint balancing. Once the desired 3D plan has been created, the surgeon can make the corresponding cuts with a conventional surgical saw that is mounted on the robotic arm. Haptic feedback generated by the system helps the surgeon to control the force and direction of saw blade within the confines of the predefined resection zone. An additional safety feature automatically stops the device when there is rapid or jerking movement to prevent bone and soft tissue injury. The system allows the surgeon to execute the preoperative plan with precision, but if this plan is flawed, the system cannot compensate for it.