Orthopedics

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Advances in Computer-Aided Technology for Total Knee Arthroplasty

Ahmed Siddiqi, DO; W. Mack Hardaker, MS; Krishna Kiran Eachempati, MBBS; Neil P. Sheth, MD

Abstract

Technology such as computer-assisted navigation systems, robotic-assisted systems, and patient-specific instrumentation has been increasingly explored during the past decade in an effort to optimize component alignment and improve clinical outcomes. Computer-assisted navigation accurately restores mechanical-axis alignment, but clinical outcome data are inconsistent. Computer-assisted navigation gap balancing has shown early promise in establishing mechanical-axis alignment with improved functional outcomes. Robotic-assisted systems more accurately restore component alignment when compared with computer-assisted navigation, but clinical outcomes have yet to be determined. Patient-specific instrumentation does not consistently improve alignment, accuracy, or patient outcomes. Studies demonstrating implant survivorship, cost-efficiency, and improved clinical outcomes and patient satisfaction are needed. [Orthopedics. 2017; 40(6):338–352.]

Abstract

Technology such as computer-assisted navigation systems, robotic-assisted systems, and patient-specific instrumentation has been increasingly explored during the past decade in an effort to optimize component alignment and improve clinical outcomes. Computer-assisted navigation accurately restores mechanical-axis alignment, but clinical outcome data are inconsistent. Computer-assisted navigation gap balancing has shown early promise in establishing mechanical-axis alignment with improved functional outcomes. Robotic-assisted systems more accurately restore component alignment when compared with computer-assisted navigation, but clinical outcomes have yet to be determined. Patient-specific instrumentation does not consistently improve alignment, accuracy, or patient outcomes. Studies demonstrating implant survivorship, cost-efficiency, and improved clinical outcomes and patient satisfaction are needed. [Orthopedics. 2017; 40(6):338–352.]

During the past 20 years, annual use of primary total knee arthroplasty (TKA) has increased by 161.5%, translating to more than 4 million patients with knee implants.1 Despite surgical advancements, multiple studies have shown that only 80% of patients are satisfied following TKA.2–9 Dissatisfaction is believed to be multifactorial and secondary to malposition, patient selection, and management of expectations.10 Studies have attempted to better evaluate shortcomings8,11–13 and have explored areas for improvement (eg, modified surgical approach)14,15 but have shown limited clinical difference.

Malalignment continues to result in increased implant failure rates and poorer clinical outcomes.16,17 Technology such as computer-assisted navigation systems, robotic-assisted systems, and patient-specific instrumentation has been increasingly explored during the past decade in an effort to decrease surgical variability and traditional instrumentation errors, thereby optimizing component alignment and improving clinical outcomes. It has been argued that the learning curve associated with adopting new technology will lead to increased complications and patient dissatisfaction. However, studies have shown that a beginner can reproduce the results of an expert from the outset, with operative times decreasing after only a handful of cases.18–21 This review explores the evolving computer-aided TKA technology and its associated advantages and disadvantages.

Computer-Assisted Navigation

Computer-assisted navigation allows for planning and assists in accurately executing TKA bony cuts without traditional instrumentation. Computer-assisted navigation TKA was first performed by Saragaglia and Picard in 1997 in Grenoble, France.22 A 10-year retrospective review evaluating the first 26 patients to undergo computer-assisted navigation TKA showed a 90% overall survival rate and an 85% patient satisfaction rate.23 As computer-assisted navigation evolves, a concomitant improvement in functional outcome is anticipated.

Computer-assisted navigation relies on infrared or electromagnetic signaling to guide instrument placement. Infrared optical systems require a clear line of sight, whereas electromagnetic systems (Stealthstation S7 Surgical Navigation System; Medtronic Inc, Louisville, Colorado) use a magnetic field generator to obtain extremity position in space.24 Optical systems can be active or passive. Active infrared systems (OrthoPilot; Aesculap AG & Co, Tuttlingen, Germany) use noncalibrated fixed bone markers that send light-emitting diode pulses to a central camera. Passive systems (ORTHOsoft; Zimmer, Inc, Warsaw, Indiana), which have predominantly replaced active systems, use reflecting spheres placed on the ends of screws or pins fixed to bone. These systems require routine calibration to ensure accuracy. Active trackers generally require additional cords, although some systems are now battery operated. Although passive trackers are cordless, efficient infrared identification is challenging because of the decreased visibility of the reflecting spheres after fluid contact.25 Overall, electromagnetic and infrared technology have been shown to be equivalent in mechanical-axis alignment restoration and accuracy.26

Other systems require preoperative 3-dimensional imaging through computed tomography, magnetic resonance imaging, or intraoperative fluoroscopy imaging. Imageless systems also exist. Computer-assisted navigation is registration dependent. Registration is accomplished by the collection of points on the bone to orient and scale a virtual image in 3-dimensional space. If trackers are not firmly secured to bone, registration may be inaccurate.27

Computed Tomography

Morphologic data can be obtained with preoperative computed tomography scans or magnetic resonance images. Although magnetic resonance imaging provides thorough soft tissue detail, its ability to identify bony structures is inferior to that of computed tomography. Intraoperative registration allows the system to calculate desired bony resection based on the computed tomography scan and helps the surgeon reproduce the simulated bony cuts. Currently, OrthoPilot and VectorVision (BrainLab, Heimstetten, Germany) are the two main computed tomography–based computer-assisted navigation systems (Figure 1). These systems are helpful in cases with extra-articular deformities such as malunion, neuropathic arthropathy, bone loss, and bone architectural abnormalities secondary to Paget's disease.28 Several studies have shown the accuracy of computed tomography computer-assisted navigation systems, especially for rotational alignment.29,30 However, increased radiation exposure and cost are concerns.

Radiographs of a patient with bilateral varus knee deformity (A). OrthoPilot (Aesculap AG & Co, Tuttlingen, Germany) computer-assisted navigation illustrating the patient's mechanical axis and planned tibial resection to restore neutral alignment (B). Postoperative long leg radiograph showing restoration of the mechanical axis (C).

Figure 1:

Radiographs of a patient with bilateral varus knee deformity (A). OrthoPilot (Aesculap AG & Co, Tuttlingen, Germany) computer-assisted navigation illustrating the patient's mechanical axis and planned tibial resection to restore neutral alignment (B). Postoperative long leg radiograph showing restoration of the mechanical axis (C).

Fluoroscopy

The Medtronic Viking System (Genesis II; Smith & Nephew, Memphis, Tennessee) uses intraoperative fluoroscopy for registration. Although this modality avoids the expense of computed tomography, it requires the maneuvering of large equipment and additional operating room personnel. Concerns exist regarding intraoperative radiation exposure and increased risk of periprosthetic joint infection. Panahi et al31 reported an association between operating room traffic and risk for periprosthetic joint infection. Whether fluoroscopy-based navigation increases the risk of periprosthetic joint infection remains to be determined. Overall, this technique has been shown to reproduce accurate component alignment, particularly in the coronal plane.32,33

Imageless

Several imageless computer-assisted navigation systems are available, including ORTHOsoft and iASSIST (Zimmer, Inc), OrthoMap (Stryker, Mahwah, New Jersey), AchieveCAS (Smith & Nephew), OrthoPilot, and VectorVision. Imageless systems depend on intraoperative software registration to define a virtual model based on a generic bone model. Traditional instrumentation can be used for bony resection through jig placement from the software's algorithm, avoiding expensive custom instruments. Although imageless systems avoid additional pre- and intraoperative imaging and radiation exposure and are able to improve the accuracy of axial alignment, they have longer operative times and questionable precision regarding component rotational positioning.34–37 Studies are needed comparing the technical and clinical results of image-based and imageless computer-assisted navigation systems.

Advantages and Disadvantages

Computer-assisted navigation systems primarily improve component positioning and rotational accuracy and precision. Compared with conventional techniques, computer-assisted navigation has been shown to reduce coronal or sagittal malalignment and to help minimize anatomic mechanical-axis alignment deviation.22,29,38,39 Mason et al40 reported a meta-analysis of 29 studies showing mechanical-axis malalignment of greater than 3° in 9% of computer-assisted navigation TKA vs 31.8% of conventional TKA.

Although several studies have shown a reduction in outliers in both the coronal plane and the sagittal plane, other studies have questioned its clinical significance.41–44 Ishida et al45 and Choong et al46 reported significantly better range of motion, knee scores, and quality of life in the computer-assisted navigation group vs a conventional group at both 1-year and 5-year follow-up. However, other case-control studies showed no statistically significant difference between computer-assisted navigation TKA and conventional TKA at 2 and 5 years.47–49 There is currently no consensus regarding the clinical relevance of improved anatomic alignment, component longevity, and patient satisfaction. However, an Australian registry of 44,573 computer-assisted navigation TKA cases showed that computer-assisted navigation reduced the revision rate from loosening among patients younger than 65 years.49

The concerns associated with computer-assisted navigation include duration of surgery, potential fracture risk with pins, and cost-effectiveness.43,50–57 Computer-assisted navigation increases surgical time by 17 to 20 minutes (20% to 23%), resulting in a theoretical increased risk of periprosthetic joint infection.51–54

Robotic-Assisted Systems

Robotic-assisted systems have evolved to better control surgical variables58 by mitigating execution errors causing component malalignment from imprecise bone cuts. Koulalis et al59 and Clark and Schmidt60 have shown superior results with robotic-assisted systems vs computer-assisted navigation systems, with robotic-assisted systems having shorter bone resection time, shorter operative duration, less coronal or sagittal deviation, and increased accuracy of mechanical-axis malalignment restoration.

The first surgical robotic system was developed in 1985 for neurosurgical biopsies and was shown to have increased precision.61,62 The first orthopedic robotic-assisted system, the ROBODOC system (THINK Surgical Inc, Fremont, California), was developed in 1986 and was used for total hip arthroplasty (THA) in 1992.34 During the past 2 decades, enthusiasm for robotic-assisted systems has increased.63,64

Robotic-assisted systems can be passive, active, or semi-active.65 Passive systems (PRAXIM; Orthopedic Synergy Inc, OMNIlife Science, Raynham, Massachusetts) do not actively perform any part of the surgical procedure but do provide precise information regarding instrument positioning and bony cuts. Active robots (TSolution One; THINK Surgical Inc) perform surgical tasks without surgeon intervention, although the surgeon is still responsible for the operative plan. Semi-active “haptic” systems (MAKO-Stryker, Fort Lauderdale, Florida) restrict motion based on preset boundaries.66 Haptic feedback, which is provided through audible beeps, vibration, or visual alerts, signals appropriate resection achievement; it also controls burr or saw speed and depth.66 The main robotic-assisted systems currently used are summarized in Table 1.

Main Robotic-Assisted Systems in the United States and Europe

Table 1:

Main Robotic-Assisted Systems in the United States and Europe

MAKO Robotic-Arm System

The MAKO Robotic-Arm System (MAKO-Stryker) was introduced in 2005 and is currently available for unicompartmental knee arthroplasty (UKA), TKA, and THA (Figure 2A). This system requires preoperative computed tomography for virtual implant positioning. Once bony resection and implant positioning parameters have been determined preoperatively, the system provides haptic feedback to prevent surgical steps outside the plan.67,68

MAKO Robotic-Arm System (Courtesy of MAKO-Stryker, Fort Lauderdale, Florida) (A). Navio Surgical System handheld robotic burr with computer software setup and virtual cut confirmation before final bone preparation (Courtesy of Smith & Nephew, Memphis, Tennessee) (B, C). New generation TSolution One TCAT with TPLAN software that uses 3-dimensional images generated from preoperative computed tomography data (Courtesy of THINK Surgical Inc, Fremont, California) (D).

Figure 2:

MAKO Robotic-Arm System (Courtesy of MAKO-Stryker, Fort Lauderdale, Florida) (A). Navio Surgical System handheld robotic burr with computer software setup and virtual cut confirmation before final bone preparation (Courtesy of Smith & Nephew, Memphis, Tennessee) (B, C). New generation TSolution One TCAT with TPLAN software that uses 3-dimensional images generated from preoperative computed tomography data (Courtesy of THINK Surgical Inc, Fremont, California) (D).

In 2010, the first MAKO UKA study, involving 10 patients, showed no short-term complications.69 The difference between the planned and the intraoperative femoral angles was 1.0°, and postoperative mechanical-axis alignment was within 1.6°.69 A study involving 32 patients undergoing MAKO UKA showed reduced tibial component alignment variability in both coronal and sagittal planes.70 MAKO moves independently and allows the bone to move during the procedure, tracking the movement with a traditional optical navigation system. This reduces the likelihood of femur or tibia fracture, infection, or other iatrogenic injury related to the robot's size and movement.69

Jones et al71 reported that, compared with traditional UKA, patients who undergo MAKO UKA have improved clinical outcomes with decreased postoperative pain, improved knee function at 3 months, decreased rehospitalization, and a higher satisfaction rate at 2.5 years. However, on comparing 30 cases of MAKO UKA vs conventional UKA, Hansen et al72 found no short-term clinical or radiographic differences that could justify the routine use of the robotic-assisted system.

MAKO has predominantly been used in conjunction with UKA and THA in more than 50,000 procedures.73 The device was approved by the Food and Drug Administration for TKA in 2015. Studies are needed regarding its long-term efficacy.

Navio Surgical System

The Navio Surgical System (Smith & Nephew) received Food and Drug Administration approval for UKA and patellofemoral arthroplasty in 2012 and for TKA in 201674 (Figures 2B–C). To assist with bone resurfacing, Navio integrates a robotic handheld burring tool with computed tomography and intraoperative imageless registration. It uses robotic visual navigation to provide a 3-dimensional image, eliminating the need for traditional cutting jigs and guides.75 Similar to MAKO, preset boundaries prevent excessive resection. Navio robots alter the burr speed and retract the burr tip to prevent errors; however, a potential lag time between burr tip retraction and speed change can be problematic.66

The Navio imageless system eliminates preoperative imaging cost and associated radiation exposure. Ponzio and Lonner76 switched from a computed tomography–based robotic-assisted system to an imageless system and concluded that preoperative computed tomography exposure has a radiation effective dose of 4.8 mSv, approximately equal to 48 chest radiographs; 25% of the patients had additional computed tomography scans amounting to more than 100 mSv in total per patient. The Food and Drug Administration has warned that a computed tomography effective dose of 10 mSv may increase the risk of malignancy, so initiatives should be implemented to diminish radiation exposure.77

Simons and Riches78 and Gregori et al79 reported Navio's ability to restore precise mechanical alignment with improved clinical outcomes and no significant learning curve. Of the first 57 patients undergoing Navio UKA, 52 (91%) showed a postoperative mechanical-axis alignment within 1° of the intraoperative Navio plan. Oxford Knee Scores and postoperative range of motion were both significantly improved at the 6-week follow-up.79 Short- and long-term data for Navio TKA and its outcomes vs those with conventional TKA have not yet been reported.

OMNIBotics

The OMNIBotics Robotic System (OMNI, Raynham, Massachusetts), previously known as PRAXIM Robotic-Assisted Navigation, uses iBlock automated active cutting-guide technology, which was approved by the Food and Drug Administration for TKA in 2010.80 Similar to Navio, OMNIBotics uses imageless anatomic knee mapping and real-time information to establish kinematic alignment. This system uses reflective marker probes that are visualized by an infrared camera to create a virtual navigation image.81 The iBlock jig is a bone-mounted motorized femoral guide that decreases systematic error by eliminating manual cutting jig orientation and placement. This system lacks haptic feedback and is a closed platform, limiting its use to a specific implant system (Apex Knee; OMNI).

Koulalis et al59 performed an analysis examining the efficacy of the iBlock technique vs computer-assisted navigation involving 12 cadavers. iBlock had a shorter bone preparation time and a significantly lower average deviation in bone cuts in all planes.59 Clark and Schmidt60 compared the results for 52 patients undergoing OMNIBotics TKA with those for 29 patients undergoing computer-assisted navigation TKA. Surgical times were 9 minutes shorter and mechanical alignment was 0.5° closer compared with computer-assisted navigation. Despite early positive results, in vivo studies and clinical outcome evaluation are required.

ROBODOC

ROBODOC, the first active robot used in arthroplasty, was introduced in Germany in 1994. The most popular European system for TKA, THA, and revision THA, ROBODOC has performed more than 28,000 arthroplasties across the United States, Europe, Japan, Korea, and India.82,83 In 2008, it became the first Food and Drug Administration–approved robotic system in orthopedic surgery. In 2014, THINK Surgical Inc acquired Curexo Technology and released the next generation robot, the TSolution One Surgical System, which received Food and Drug Administration approval for THA.84 Its TKA application is currently unavailable in the United States.

TSolution One uses preoperative planning computer software, TPLAN, to incorporate preoperative computed tomography data and is equipped with a 5-axis robotic arm equipped with a milling device (Figure 2D). Whereas ROBODOC required preoperative fiducial placement for registration, TSolution One is fiducial free and uses a digitizer to collect points and locate the exact position of the patient's anatomy to mill joint surfaces for component placement.84 Although TSolution One allows the surgeon to oversee the milling process and stop the robot, no intraoperative adjustments are allowed while the preoperative plan is executed.85

Disadvantages of this system include increased time required pre- and intraoperatively compared with conventional TKA. Due to reports of inconsistent results, use of the ROBODOC system has decreased,86,87 although TSolution One is actively used for TKA throughout Europe and Asia.

CASPAR and Acrobot

The Computer-Assisted Surgical Planning and Robotics (CASPAR) System (URS Ortho GmbH & Co KG, Mecklenburg-Vorpommern, Germany), another German active image-based robotic system, was introduced in 2000 for THA and TKA. The company merged with Smith & Nephew in 2001.66 After initial reported success, CASPAR TKA showed increasing inferior results and higher complication rates.66,88,89 As a result, CASPAR is no longer available.

The Acrobot system for UKA and TKA was acquired by Stanmore Implants Worldwide in 2010 and renamed the Stanmore Sculptor System (SSS; Acrobot, London, England). This was the first system to introduce haptic response, and it paved the way for more advanced systems such as MAKO and Navio. In a prospective randomized study, Cobb et al90 showed that, compared with conventional methods, Acrobot UKA had superior implant accuracy. However, the SSS was withdrawn from robotics when MAKO-Surgical acquired the technology in 2013.34

Advantages and Disadvantages

Robotic-assisted systems have been effective for improving mechanical-axis alignment in the hopes of decreasing the risk of aseptic loosening and increasing component longevity. Although larger survivorship studies are needed to compare robotic-assisted systems, computer-assisted navigation, and conventional TKA, early results show promise.

The main disadvantage of robotic-assisted systems is the high initial capital expenditure. Each Navio system costs $400,000 to $450,000, while MAKO requires $1 million of upfront capital investment.91,92 The cost-effectiveness of robotic-assisted systems remains a topic of debate because long-term follow-up and longterm outcomes have yet to be determined.

Soft Tissue Balancing Technology

Instability, a common mode of TKA failure, is often attributed to inadequate soft tissue balancing. Poor soft tissue balancing has been shown to increase the risk of polyethylene wear, loosening, and residual pain.93–97 Traditional instruments that assist in soft tissue balancing rely on static measurements.98 Intraoperative sensors such as the VERASENSE Knee System (OrthoSensor, Inc, Dania Beach, Florida) and the eLIBRA Dynamic Knee Balancing System (Synvasive Technology, Zimmer-Biomet, Warsaw, Indiana) are recent developments allowing for dynamic in vivo evaluation of joint loading and correlation of intercompartmental pressures throughout range of motion.99 Real-time feedback aids subsequent adjustments through either further bony resection or soft tissue release or balancing.100

Nodzo et al101 reported on 54 patients undergoing sensor TKA with improved functional and Knee Society Scores at a mean follow-up of 4.6 months. Gustke et al102,103 reviewed 176 patients who underwent sensor TKA and noted 87% as balanced and 13% as left unbalanced. The balanced group had greater improvement in Knee Society Scores and Western Ontario and McMaster Universities Arthritis Index scores at 6 months and 1 year postoperatively. The primary limitation of sensor technology is the detection of measured pressures in a non–weight-bearing state, which is not reflective of forces that occur with weight-bearing activities.104

Computer navigation has also been used to assess the gaps created by conventional TKA instruments.105 Pang et al106 showed decreased laxity, improved mechanical-axis alignment, and better functional and Oxford Knee Scores for computer-assisted navigation soft tissue– balanced TKA vs conventional TKA at 2 years. Stiehl and Heck105 further showed that computer navigation was precise to 1 mm for gap measurements. However, potential errors during computer-assisted navigation registration from bony landmark variability can yield an imbalanced knee.107 Early results show that computer-assisted navigation gap balancing may be the missing link in improving clinical outcomes for computer navigation, but further comparative trials with longer-term follow-up are needed. Additionally, because multiple studies have shown the ability of robotic-assisted systems to control surgical variables and implant positioning,59,60,69,79,108 future technology could explore incorporating robotic-assisted systems with soft tissue balancing.

Patient-Specific Instrumentation

Patient-specific instrumentation aims to remove intraoperative variability and decrease operative time by shifting decision-making to the preoperative setting. Preoperative advanced imaging is used to create custom cutting jigs based on the patient's native bony anatomy. Most magnetic resonance imaging–based guides are designed to fit on the articular cartilage. In a meta-analysis, An et al109 showed that magnetic resonance imaging–based patient-specific instrumentation guides had fewer outliers compared with computed tomography with no significant difference in component placement.

Patient-specific instrumentation software analyzes images to create a virtual model used to calculate implant size. Disposable custom guides based on osseous anatomy require intraoperative curettage of the articular surface until subchondral bone is reached. The custom jigs are low profile and do not require intramedullary instrumentation. The main patient-specific instrumentation systems on the market are summarized in Table 2.

Main Patient-Specific Instrumentation Systems Currently on the Market

Table 2:

Main Patient-Specific Instrumentation Systems Currently on the Market

Voleti et al110 published a meta-analysis evaluating 9 studies with 529 cases of magnetic resonance imaging–based patient-specific instrumentation TKA vs 428 cases of conventional TKA. Although patient-specific instrumentation showed improved femorotibial coronal alignment, conventional TKA had a better coronal hip–knee–ankle angle. There were no differences regarding outliers, operative time, and blood loss. Patient-specific instrumentation added an extra $628 per case, which did not include the cost of the preoperative magnetic resonance imaging.110 Jiang et al111 and Thienpont et al112 reported meta-analyses of 18 and 16 studies, respectively, concluding that, compared with conventional TKA, patient-specific instrumentation does not improve alignment accuracy. Additionally, Huijbregts et al113 reviewed 21 studies in a meta-analysis of patient-specific instrumentation TKA and determined that there was no statistically significant improvement in clinical outcomes at 6 months and 1 year postoperatively.

Advantages and Disadvantages

Patient-specific instrumentation is thought to facilitate more accurate and reliably placed implants with fewer outliers, which may improve clinical outcomes, satisfaction, and longevity without the excess cost of computer-assisted navigation and robotic-assisted systems. Advocates further propose that decreased surgical duration leads to decreased tourniquet and anesthesia time, potentially decreasing postoperative complications. Custom disposable cutting jigs reduce the number of surgical trays and steps required for implantation. However, Hamilton et al114 performed a prospective randomized study and determined that surgical time was significantly shorter for 26 patients who underwent traditional TKA compared with 26 patients who underwent patient-specific instrumentation TKA with no difference in postoperative alignment or component position. Additionally, Stronach et al,115 in reporting on 60 patients who underwent patient-specific instrumentation TKA, discovered that patient-specific instrumentation did not reduce surgical time. Further, because patient-specific instrumentation predicted implanted component size in only 23% of femurs and 47% of tibias, several intraoperative changes were necessary.

Hospitals may support patient-specific instrumentation because there is less tray use and expensive navigation equipment is avoided; however, custom jigs and preoperative advanced imaging increase overall cost. Sassoon et al116 concluded, from a systematic review, that patient-specific instrumentation TKA did not reliably show improvement in postoperative limb or component alignment vs conventional instruments. Although fewer trays are needed, patient-specific instrumentation has not been shown to improve surgical efficiency, cost-efficiency, postoperative pain, or functional outcomes.116 Similar to computer-assisted navigation and robotic-assisted systems, long-term data are inconclusive.

Cost Analysis

The Centers for Medicare & Medicaid Services and the Bundled Payments for Care Improvement initiative link payments for the multiple services beneficiaries receive during an episode of care.117 The aims of these models are higher-quality care while lowering Medicare costs. Given the increasingly cost-conscious health care environment, it is important to consider thresholds for cost-savings associated with these technologies. A cost analysis by Novak et al56 showed that a purchased computer-assisted navigation system adds $1500 per operation. Furthermore, navigated TKA can be considered cost-effective with an incremental cost-effectiveness ratio of $45,554 per quality-adjusted life-year, which is below the $50,000 per quality-adjusted life-year that is a threshold by the Center for Evaluation of Value and Risk in Health for cost-effectiveness.118–120

Dong and Buxton121 applied a Markov model and concluded that computer-assisted navigation TKA was a long-term cost-saving technology. However, the conclusions from these analyses were based on the assumption that precise implant alignment decreases the risk of revision TKA. Gothesen et al88 reviewed the Norwegian Arthroplasty Registry and suggested that for computer navigation to be below health care's threshold value for cost-added quality-adjusted life-year, the rate of revision surgery needs to be reduced to between 0.8% and 13%. However, the Norwegian Arthroplasty Registry results are based on Norwegian market prices and health services.88

The 2013 Medicare Provider Analysis and Review data provided by the Centers for Medicare & Medicaid Services indicated that most hospitals lose revenue at an average of more than $8000 per revision surgery.122,123 Leone et al123 suggested that intraoperative sensors are cost-effective because they minimize hospital financial losses by decreasing the total revision burden. Additionally, sensors are fairly inexpensive ($459 per case) and do not add appreciable time to surgical work-flow.99,124–127

Conclusion

Computer-assisted navigation, robotic-assisted, and patient-specific instrumentation TKA systems are not yet universally accepted. Cost–benefit ratio remains critical in today's cost-conscious health care environment. Current studies regarding radiographic and clinical outcomes are summarized in Table 3 and Table 4, respectively. Future research should focus on high-quality studies to determine implant survivorship, patient satisfaction, and postoperative clinical function. To generate desired long-term data, it would be judicious for surgeons who use this technology to participate in registries such as the American Joint Replacement Registry that include patient-reported outcomes and satisfaction. As technology continues to rapidly evolve, the ultimate goal of patient satisfaction will remain paramount in achieving favorable clinical outcomes.

Advanced Technology for Unicompartmental or Total Knee Arthroplasty With Radiographic OutcomesAdvanced Technology for Unicompartmental or Total Knee Arthroplasty With Radiographic Outcomes

Table 3:

Advanced Technology for Unicompartmental or Total Knee Arthroplasty With Radiographic Outcomes

Advanced Technology for Unicompartmental or Total Knee Arthroplasty With Clinical OutcomesAdvanced Technology for Unicompartmental or Total Knee Arthroplasty With Clinical Outcomes

Table 4:

Advanced Technology for Unicompartmental or Total Knee Arthroplasty With Clinical Outcomes

References

  1. Cram P, Lu X, Kates SL, Singh JA, Li Y, Wolf BR. Total knee arthroplasty volume, utilization, and outcomes among Medicare beneficiaries, 1991–2010. JAMA. 2012; 308(12):1227–1236. doi:10.1001/2012.jama.11153 [CrossRef]
  2. Neogi T. The epidemiology and impact of pain in osteoarthritis. Osteoarthritis Cartilage. 2013; 21(9):1145–1153. doi:10.1016/j.joca.2013.03.018 [CrossRef]
  3. Anderson JG, Wixson RL, Tsai D, Stulberg SD, Chang RW. Functional outcome and patient satisfaction in total knee patients over the age of 75. J Arthroplasty. 1996; 11(7):831–840. doi:10.1016/S0883-5403(96)80183-5 [CrossRef]
  4. Chesworth BM, Mahomed NN, Bourne RB, Davis AM. Willingness to go through surgery again validated the WOMAC clinically important difference from THR/TKR surgery. J Clin Epidemiol. 2008; 61(9):907–918. doi:10.1016/j.jclinepi.2007.10.014 [CrossRef]
  5. Dunbar MJ, Robertsson O, Ryd L, Lidgren L. Appropriate questionnaires for knee arthroplasty: results of a survey of 3600 patients from the Swedish Knee Arthroplasty Registry. J Bone Joint Surg Br. 2001; 83(3):339–344. doi:10.1302/0301-620X.83B3.11134 [CrossRef]
  6. Hawker G, Wright J, Coyte P, et al. Health-related quality of life after knee replacement. J Bone Joint Surg Am. 1998; 80(2):163–173. doi:10.2106/00004623-199802000-00003 [CrossRef]
  7. Noble PC, Conditt MA, Cook KF, Mathis KB. The John Insall Award: patient expectations affect satisfaction with total knee arthroplasty. Clin Orthop Relat Res. 2006; 452:35–43. doi:10.1097/01.blo.0000238825.63648.1e [CrossRef]
  8. Bourne RB, Chesworth BM, Davis AM, Mahomed NN, Charron KD. Patient satisfaction after total knee arthroplasty: who is satisfied and who is not?Clin Orthop Relat Res. 2010; 468(1):57–63. doi:10.1007/s11999-009-1119-9 [CrossRef]
  9. Schroer WC, Berend KR, Lombardi AV, et al. Why are total knees failing today? Etiology of total knee revision in 2010 and 2011. J Arthroplasty. 2013; 28(suppl 8):116–119. doi:10.1016/j.arth.2013.04.056 [CrossRef]
  10. Arden NK, Kiran A, Judge A, et al. What is a good patient reported outcome after total hip replacement?Osteoarthritis Cartilage. 2011; 19(2):155–162. doi:10.1016/j.joca.2010.10.004 [CrossRef]
  11. Choi Y, Ra HJ. Patient satisfaction after total knee arthroplasty. Knee Surg Relat Res. 2016; 28(1):1–15. doi:10.5792/ksrr.2016.28.1.1 [CrossRef]
  12. Pivec R, Issa K, Given K, et al. A prospective, longitudinal study of patient satisfaction following total knee arthroplasty using the Short-Form 36 (SF-36) survey stratified by various demographic and comorbid factors. J Arthroplasty. 2015; 30(3):374–378. doi:10.1016/j.arth.2014.10.013 [CrossRef]
  13. Clement ND, Burnett R. Patient satisfaction after total knee arthroplasty is affected by their general physical well-being. Knee Surg Sports Traumatol Arthrosc. 2013; 21(11):2638–2646. doi:10.1007/s00167-013-2523-y [CrossRef]
  14. Hernandez-Vaquero D, Noriega-Fernandez A, Suarez-Vazquez A. Total knee arthroplasties performed with a mini-incision or a standard incision: similar results at six months follow-up. BMC Musculoskelet Disord. 2010; 11:27. doi:10.1186/1471-2474-11-27 [CrossRef]
  15. Shin YS, Kim HJ, Ko YR, Yoon JR. Minimally invasive navigation-assisted versus conventional total knee arthroplasty: a meta-analysis. Knee Surg Sports Traumatol Arthrosc. 2016; 24(11):3425–3432. doi:10.1007/s00167-016-4016-2 [CrossRef]
  16. Manzotti A, Cerveri P, DeMomi E, Pullen C, Confalonieri N. Relationship between cutting errors and learning curve in computer-assisted total knee replacement. Int Orthop. 2010; 34(5):655–662. doi:10.1007/s00264-009-0816-z [CrossRef]
  17. Ritter MA, Faris PM, Keating EM, Meding JB. Postoperative alignment of total knee replacement: its effect on survival. Clin Orthop Relat Res. 1994; 299:153–156.
  18. Jenny JY, Miehlke RK, Giurea A. Learning curve in navigated total knee replacement: a multi-centre study comparing experienced and beginner centres. Knee. 2008; 15(2):80–84. doi:10.1016/j.knee.2007.12.004 [CrossRef]
  19. Smith BR, Deakin AH, Baines J, Picard F. Computer navigated total knee arthroplasty: the learning curve. Comput Aided Surg. 2010; 15(1–3):40–48. doi:10.3109/10929088.2010.486559 [CrossRef]
  20. Karia M, Masjedi M, Andrews B, Jaffry Z, Cobb J. Robotic assistance enables inexperienced surgeons to perform unicompartmental knee arthroplasties on dry bone models with accuracy superior to conventional methods. Adv Orthop. 2013; 2013:481039. doi:10.1155/2013/481039 [CrossRef]
  21. Chan WC, Pinder E, Loeffler M. Patient-specific instrumentation versus conventional instrumentation in total knee arthroplasty. J Orthop Surg (Hong Kong). 2016; 24(2):175–178. doi:10.1177/1602400211 [CrossRef]
  22. Saragaglia D, Picard F, Leitner F. An 8- to 10-year follow up of 26 computer assisted total knee arthroplasties. Orthopedics. 2007; 30(10)(suppl):S121–S123.
  23. Anderson KC, Buehler KC, Markel DC. Computer assisted navigation in total knee arthroplasty: comparison with conventional methods. J Arthroplasty. 2005; 20(7)(suppl 3):132–138. doi:10.1016/j.arth.2005.05.009 [CrossRef]
  24. Stiehl JB. Computer navigation in primary total knee arthroplasty. In: Scott WN, Diduch DR, Hanssen AD, Iorio R, Long WJ, eds. Insall & Scott Surgery of the Knee, 6th ed. Philadelphia, PA: Elsevier; 2018:1775–1783.
  25. Lionberger DR. The attraction of electromagnetic computer-assisted navigation in orthopaedic surgery. In: Stiehl JB, Konermann WH, Haaker RG, DiGioia AM, eds. Navigation and MIS in Orthopedic Surgery. Berlin, Germany: Springer; 2007:44. doi:10.1007/978-3-540-36691-1_6 [CrossRef]
  26. Lionberger DR, Weise J, Ho DM, Haddad JL. How does electromagnetic navigation stack up against infrared navigation in minimally invasive total knee arthroplasties?J Arthroplasty. 2008; 23(4):573–580. doi:10.1016/j.arth.2007.07.005 [CrossRef]
  27. Stulberg SD, Picard F, Saragaglia D. Computer-assisted total knee replacement arthroplasty. Oper Tech Orthop. 2000; 10(1):25–39. doi:10.1016/S1048-6666(00)80040-3 [CrossRef]
  28. Hafez MA, Schemitsch EH, Fadel M, Hosny G. Computer assisted surgery for correction of skeletal deformities. In: Stiehl JB, Konermann WH, Haaker RG, DiGioia AM, eds. Navigation and MIS in Orthopaedic Surgery. Berlin, Germany: Springer; 2007:528.
  29. Bäthis H, Perlick L, Tingart M, Luring C, Perlick C, Grifka J. Radiological results of image-based and non-image-based computer-assisted total knee arthroplasty. Int Orthop. 2004; 28(2):87–90. doi:10.1007/s00264-003-0533-y [CrossRef]
  30. Mizu-uchi H, Matsuda S, Miura H, Okazaki K, Akasaki Y, Iwamoto Y. The evaluation of post-operative alignment in total knee replacement using a CT-based navigation system. J Bone Joint Surg Br. 2008; 90(8):1025–1031. doi:10.1302/0301-620X.90B8.20265 [CrossRef]
  31. Panahi P, Stroh M, Casper DS, Parvizi J, Austin MS. Operating room traffic is a major concern during total joint arthroplasty. Clin Orthop Relat Res. 2012; 470(10):2690–2694. doi:10.1007/s11999-012-2252-4 [CrossRef]
  32. Victor J, Hoste D. Image-based computer-assisted total knee arthroplasty leads to lower variability in coronal alignment. Clin Orthop Relat Res. 2004; 428:131–139. doi:10.1097/01.blo.0000147710.69612.76 [CrossRef]
  33. Lad DG, Thilak J, Thadi M. Component alignment and functional outcome following computer assisted and jig based total knee arthroplasty. Indian J Orthop. 2013; 47(1):77–82. doi:10.4103/0019-5413.106915 [CrossRef]
  34. Glassman AH, Lachiewicz PF, Tanzer M. Orthopaedic Knowledge Update. Rosemont, IL: American Academy of Orthopaedic Surgeons; 2011.
  35. Saragaglia D, Picard F, Chaussard C, Montbarbon E, Leitner F, Cinquin P. Computer-assisted knee arthroplasty: comparison with a conventional procedure. Results of 50 cases in a prospective randomized study. Rev Chir Orthop Reparatrice Appar Mot. 2001; 87(1):18–28.
  36. Tigani D, Rimondi E, Trentani P, Ansaloni M, Amendola L, Testi D. Three-dimensional analysis of image-free navigation system for total knee arthroplasty. Musculoskelet Surg. 2011; 95(2):81–87. doi:10.1007/s12306-010-0090-7 [CrossRef]
  37. Haritinian EG, Pimpalnerkar AL. Computer assisted total knee arthroplasty: does it make a difference?Mædica (Buchar). 2013; 8(2):176–181.
  38. Böhling U, Schamberger H, Grittner U, Scholz J. Computerised and technical navigation in total knee arthroplasty. J Orthopaed Traumatol. 2005; 6(2):69–75. doi:10.1007/s10195-005-0084-7 [CrossRef]
  39. Chin PL, Yang KL, Yeo SJ, Lo NN. Randomized control trial comparing radiographic total knee arthroplasty implant placement using computer navigation versus conventional technique. J Arthroplasty. 2005; 20(5):618–626. doi:10.1016/j.arth.2005.04.004 [CrossRef]
  40. Mason JB, Fehring TK, Estok R, Banel D, Fahrbach K. Meta-analysis of alignment outcomes in computer-assisted total knee arthroplasty surgery. J Arthroplasty. 2007; 22(8):1097–1106. doi:10.1016/j.arth.2007.08.001 [CrossRef]
  41. Del Gaizo D, Soileau ES, Lachiewicz PF. Value of preoperative templating for primary total knee arthroplasty. J Knee Surg. 2009; 22(4):284–293. doi:10.1055/s-0030-1247765 [CrossRef]
  42. Fu Y, Wang M, Liu Y, Fu Q. Alignment outcomes in navigated total knee arthroplasty: a meta-analysis. Knee Surg Sports Traumatol Arthrosc. 2012; 20(6):1075–1082. doi:10.1007/s00167-011-1695-6 [CrossRef]
  43. Hetaimish BM, Khan MM, Simunovic N, Al-Harbi HH, Bhandari M, Zalzal PK. Meta-analysis of navigation vs conventional total knee arthroplasty. J Arthroplasty. 2012; 27(6):1177–1182. doi:10.1016/j.arth.2011.12.028 [CrossRef]
  44. Zamora LA, Humphreys KJ, Watt AM, Forel D, Cameron AL. Systematic review of computer-navigated total knee arthroplasty. ANZ J Surg. 2013; 83(1):22–30. doi:10.1111/j.1445-2197.2012.06255.x [CrossRef]
  45. Ishida K, Matsumoto T, Tsumura N, et al. Mid-term outcomes of computer-assisted total knee arthroplasty. Knee Surg Sports Traumatol Arthrosc. 2011; 19(7):1107–1112. doi:10.1007/s00167-010-1361-4 [CrossRef]
  46. Choong PF, Dowsey MM, Stoney JD. Does accurate anatomical alignment result in better function and quality of life? Comparing conventional and computer-assisted total knee arthroplasty. J Arthroplasty. 2009; 24(4):560–569. doi:10.1016/j.arth.2008.02.018 [CrossRef]
  47. Molfetta L, Caldo D. Computer navigation versus conventional implantation for varus knee total arthroplasty: a case-control study at 5 years follow-up. Knee. 2008; 15(2):75–79. doi:10.1016/j.knee.2007.12.006 [CrossRef]
  48. Spencer JM, Chauhan SK, Sloan K, Taylor A, Beaver RJ. Computer navigation versus conventional total knee replacement: no difference in functional results at two years. J Bone Joint Surg Br. 2007; 89(4):477–480. doi:10.1302/0301-620X.89B4.18094 [CrossRef]
  49. de Steiger RN, Liu YL, Graves SE. Computer navigation for total knee arthroplasty reduces revision rate for patients less than sixty-five years of age. J Bone Joint Surg Am. 2015; 97(8):635–642. doi:10.2106/JBJS.M.01496 [CrossRef]
  50. Kim YH, Kim JS, Hong KS, Kim YJ, Kim JH. Prevalence of fat embolism after total knee arthroplasty performed with or without computer navigation. J Bone Joint Surg Am. 2008; 90(1):123–128. doi:10.2106/JBJS.G.00176 [CrossRef]
  51. Bauwens K, Matthes G, Wich M, et al. Navigated total knee replacement: a meta-analysis. J Bone Joint Surg Am. 2007; 89(2):261–269.
  52. Kalairajah Y, Simpson D, Cossey AJ, Verrall GM, Spriggins AJ. Blood loss after total knee replacement: effects of computer-assisted surgery. J Bone Joint Surg Br. 2005; 87(11):1480–1482. doi:10.1302/0301-620X.87B11.16474 [CrossRef]
  53. Siston RA, Giori NJ, Goodman SB, Delp SL. Surgical navigation for total knee arthroplasty: a perspective. J Biomech. 2007; 40(4):728–735. doi:10.1016/j.jbiomech.2007.01.006 [CrossRef]
  54. Bae DK, Song SJ. Computer assisted navigation in knee arthroplasty. Clin Orthop Surg. 2011; 3(4):259–267. doi:10.4055/cios.2011.3.4.259 [CrossRef]
  55. Panasiuk M, Bonczak O. Fatigue fracture of the femur after navigated total knee replacement. Ortop Traumatol Rehabil. 2009; 11(1):72–77.
  56. Novak EJ, Silverstein MD, Bozic KJ. The cost-effectiveness of computer-assisted navigation in total knee arthroplasty. J Bone Joint Surg Am. 2007; 89(11):2389–2397.
  57. Slover JD, Tosteson AN, Bozic KJ, Rubash HE, Malchau H. Impact of hospital volume on the economic value of computer navigation for total knee replacement. J Bone Joint Surg Am. 2008; 90(7):1492–1500. doi:10.2106/JBJS.G.00888 [CrossRef]
  58. Liow MH, Xia Z, Wong MK, Tay KJ, Yeo SJ, Chin PL. Robot-assisted total knee arthroplasty accurately restores the joint line and mechanical axis: a prospective randomised study. J Arthroplasty. 2014; 29(12):2373–2377. doi:10.1016/j.arth.2013.12.010 [CrossRef]
  59. Koulalis D, O'Loughlin PF, Plaskos C, Kendoff D, Cross MB, Pearle AD. Sequential versus automated cutting guides in computer-assisted total knee arthroplasty. Knee. 2011; 18(6):436–442. doi:10.1016/j.knee.2010.08.007 [CrossRef]
  60. Clark TC, Schmidt FH. Robot-assisted navigation versus computer-assisted navigation in primary total knee arthroplasty: efficiency and accuracy. ISRN Orthop. 2013; 2013:794–827. doi:10.1155/2013/794827 [CrossRef]
  61. Robotics and Motion Planning. http://robotics.stanford.edu/~jks/pubs/2004-Camarillo-Krummel-Salisbury-Robotic_Technology_in_Surgery_Am_J_Surgery.pdf. Accessed August 10, 2016.
  62. Davies B. A review of robotics in surgery. Proc Inst Mech Eng H. 2000; 214(1):129–140. doi:10.1243/0954411001535309 [CrossRef]
  63. Moon Y, Ha C, Do K, et al. Comparison of robot-assisted and conventional total knee arthroplasty: a controlled cadaver study using multiparameter quantitative three-dimensional CT assessment of alignment. Computer Aided Surgery. 2012; 17(2):86–95. doi:10.3109/10929088.2012.654408 [CrossRef]
  64. Taylor RH. Robotic systems for orthopaedic surgery. In: Nolte LP. Computer and Robotic Assisted Hip and Knee Surgery. Oxford, England: Oxford University Press; 2004:57.
  65. DiGioia AM III, Jaramaz B, Colgan BD. Computer assisted orthopaedic surgery: image guided and robotic assistive technologies. Clin Orthop Relat Res. 1998; 354:8–16. doi:10.1097/00003086-199809000-00003 [CrossRef]
  66. Jacofsky DJ, Allen M. Robotics in arthroplasty: a comprehensive review. J Arthroplasty. 2016; 31(10):2353–2363. doi:10.1016/j.arth.2016.05.026 [CrossRef]
  67. MAKO Surgical Corporation. Common stock. https://www.sec.gov/Archives/ed-gar/data/1411861/000095014408001109/x09308b4e424b4.htm. Accessed August 22, 2017.
  68. Lang JE, Mannava S, Floyd AJ, et al. Robotic systems in orthopaedic surgery. J Bone Joint Surg Br. 2011; 93(10):1296–1299. doi:10.1302/0301-620X.93B10.27418 [CrossRef]
  69. Pearle AD, O'Loughlin PF, Kendoff DO. Robot-assisted unicompartmental knee arthroplasty. J Arthroplasty. 2010; 25(2):230–237. doi:10.1016/j.arth.2008.09.024 [CrossRef]
  70. Lonner JH, John TK, Conditt MA. Robotic arm-assisted UKA improves tibial component alignment: a pilot study. Clin Orthop Relat Res. 2010; 468(1):141–146. doi:10.1007/s11999-009-0977-5 [CrossRef]
  71. Jones B, Blyth MJ, MacLean A, et al. Accuracy of UKA implant positioning and early clinical outcomes in a RCT comparing robotic assisted and manual surgery [abstract]. Presented at: 13th annual CAOS Meeting. ; June 12–15, 2013. ; Orlando, FL. .
  72. Hansen DC, Kusuma SK, Palmer RM, Harris KB. Robotic guidance does not improve component position or short-term outcome in medial unicompartmental knee arthroplasty. J Arthroplasty. 2014; 29(9):1784–1789. doi:10.1016/j.arth.2014.04.012 [CrossRef]
  73. van der List JP, Chawla H, Pearle AD. Robotic-assisted knee arthroplasty: an overview. Am J Orthop (Belle Mead, NJ). 2016; 45(4):202–211.
  74. NavioPFS. http://www.accessdata.fda.gov/cdrh_docs/pdf12/K121936.pdf. Accessed August 11, 2016.
  75. Navio Overview. http://www.smith-nephew.com/professional/microsites/navio/navio-technology/product-overview. Accessed August 11, 2016.
  76. Ponzio DY, Lonner JH. Preoperative mapping in unicompartmental knee arthroplasty using computed tomography scans is associated with radiation exposure and carries high cost. J Arthroplasty. 2014; 30(6):964–967. doi:10.1016/j.arth.2014.10.039 [CrossRef]
  77. Radiation-emitting products and procedures. US Food and Drug Administration site. http://www.fda.gov/radiation-emittingproducts/radiationemittingproductsandprocedures. Accessed November 24, 2016.
  78. Simons M, Riches P. The learning curve of robotically-assisted unicondylar knee arthroplasty. Bone Joint J. 2014; 96-B(11)(suppl):152.
  79. Gregori A, Picard F, Bellemans J, Smith J, Simone A. Handheld precision sculpting tool for unicondylar knee arthroplasty. a clinical review [abstract]. Presented at: 15th EFORT Congress Meeting. ; June 4–6, 2014. ; London, UK. .
  80. 510(k) Premarket notification. US Food and Drug Administration site. http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfPMN/pmn.cfm?ID=K090953. Accessed August 12, 2016.
  81. Plaskos C, Cinquin P, Lavallée S, Hodgson AJ. Praxiteles: a miniature bone-mounted robot for minimal access total knee arthroplasty. Int J Med Robot. 2005; 1(4):67–79. doi:10.1002/rcs.59 [CrossRef]
  82. Bargar WL, Bauer A, Börner M. Primary and revision total hip replacement using the ROBODOC system. Clin Orthop Relat Res. 1998; 354:82–91. doi:10.1097/00003086-199809000-00011 [CrossRef]
  83. ROBODOC. www.robodoc.com/patient_about_history.html. Accessed August 12, 2016.
  84. THINK Surgical Inc. http://thinksurgical.com/history. Accessed November 22, 2016.
  85. Health Quality Ontario. Computer-assisted hip and knee arthroplasty: navigation and active robotic systems. An evidence-based analysis. Ont Health Technol Assess Ser. 2004; 4(2):1–39.
  86. Song EK, Seon JK, Yim JH, Netravali NA, Bargar WL. Robotic-assisted TKA reduces postoperative alignment outliers and improves gap balance compared to conventional TKA. Clin Orthop Relat Res. 2013; 471(1):118–126. doi:10.1007/s11999-012-2407-3 [CrossRef]
  87. Park SE, Lee CT. Comparison of robotic-assisted and conventional manual implantation of a primary total knee arthroplasty. J Arthroplasty. 2007; 22(7):1054–1059. doi:10.1016/j.arth.2007.05.036 [CrossRef]
  88. Gothesen Ø, Slover J, Havelin L, Askildsen JE, Malchau H, Furnes O. An economic model to evaluate cost-effectiveness of computer assisted knee replacement surgery in Norway. BMC Musculoskelet Disord. 2013; 14:202–211. doi:10.1186/1471-2474-14-202 [CrossRef]
  89. Siebert W, Mai S, Kober R, Heeckt PF. Technique and first clinical results of robot-assisted total knee replacement. Knee. 2002; 9(3):173–180. doi:10.1016/S0968-0160(02)00015-7 [CrossRef]
  90. Cobb J, Henckel J, Gomes P, et al. Hands-on robotic unicompartmental knee replacement: a prospective, randomised controlled study of the acrobot system. J Bone Joint Surg Br. 2006; 88(2):188–197. doi:10.1302/0301-620X.88B2.17220 [CrossRef]
  91. Parmar A. Can Stryker get more surgeons to use MAKO robotic platform?MDDI. http://www.mddionline.com/blog/devicetalk/how-stryker-can-get-more-surgeons-use-mako-robotic-platform-3-5-15. Accessed August 12, 2016.
  92. DeMaitre E. Orthopedics firm Smith & Nephew buys Blue Belt for $275M. Robotics Business Review. https://www.roboticsbusi-nessreview.com/health-medical/orthopedics_firm_smith_nephew_buys_blue_belt_for_275m/2015. Accessed August 12, 2016.
  93. Schroer WC, Berend KR, Lombardi AV, et al. Why are total knees failing today? Etiology of total knee revision in 2010 and 2011. J Arthroplasty. 2013; 28(8)(suppl):116–119. doi:10.1016/j.arth.2013.04.056 [CrossRef]
  94. Pagnano MW, Hanssen AD, Lewallen DG, Stuart MJ. Flexion instability after primary posterior cruciate retaining total knee arthroplasty. Clin Orthop Relat Res. 1998; 356:39–46. doi:10.1097/00003086-199811000-00008 [CrossRef]
  95. Attfield SF, Wilton TJ, Pratt DJ, Sambatakakis A. Soft-tissue balance and recovery of proprioception after total knee replacement. J Bone Joint Surg Br. 1996; 78(4):540–545.
  96. Babazadeh S, Stoney JD, Lim K, Choong PF. The relevance of ligament balancing in total knee arthroplasty: how important is it? A systematic review of the literature. Orthop Rev (Pavia). 2009; 1(2):e26. doi:10.4081/or.2009.e26 [CrossRef]
  97. Roche M, Elson L, Anderson C. Dynamic soft tissue balancing in total knee arthroplasty. Orthop Clin North Am. 2014; 45(2):157–165. doi:10.1016/j.ocl.2013.11.001 [CrossRef]
  98. D'Lima DD, Patil S, Steklov N, Colwell CW Jr, . Dynamic intraoperative ligament balancing for total knee arthroplasty. Clin Orthop Relat Res. 2007; 463:208–212.
  99. Smith T, Elson L, Anderson C, Leone W. How are we addressing ligament balance in TKA? A literature review of revision etiology and technological advancement. J Clin Orthop Trauma. 2016; 7(4):248–255. doi:10.1016/j.jcot.2016.04.001 [CrossRef]
  100. Wasielewski RC, Galat DD, Komistek RD. An intraoperative pressure-measuring device used in total knee arthroplasties and its kinematics correlations. Clin Orthop Relat Res. 2004; 427:171–178. doi:10.1097/01.blo.0000145555.34318.46 [CrossRef]
  101. Nodzo SR, Franceschini V, Gonzalez Della Valle A. Intraoperative load-sensing variability during cemented, posterior-stabilized total knee arthroplasty. J Arthroplasty. 2017; 32(1):66–70. doi:10.1016/j.arth.2016.06.029 [CrossRef]
  102. Gustke KA, Golladay GJ, Roche MW, Elson LC, Anderson CR. A new method for defining balance: promising short-term clinical outcomes of sensor-guided TKA. J Arthroplasty. 2014; 29(5):955–960. doi:10.1016/j.arth.2013.10.020 [CrossRef]
  103. Gustke KA, Golladay GJ, Roche MW, Elson LC, Anderson CR. Primary TKA patients with quantifiably balanced soft-tissue achieve significant clinical gains sooner than unbalanced patients. Adv Orthop. 2014; 2014:628695. doi:10.1155/2014/628695 [CrossRef]
  104. Wasielewski RC, Galat DD, Komistek RD. Correlation of compartment pressure data from an intraoperative sensing device with postoperative fluoroscopic kinematic results in TKA patients. J Biomech. 2005; 38(2):333–339. doi:10.1016/j.jbiomech.2004.02.040 [CrossRef]
  105. Stiehl JB, Heck DA. How precise is computer-navigated gap assessment in TKA?Clin Orthop Relat Res. 2015; 473(1):115–118. doi:10.1007/s11999-014-3785-5 [CrossRef]
  106. Pang HN, Yeo SJ, Chong HC, Chin PL, Ong J, Lo NN. Computer-assisted gap balancing technique improves outcome in total knee arthroplasty, compared with conventional measured resection technique. Knee Surg Sports Traumatol Arthrosc. 2011; 19(9):1496–1503. doi:10.1007/s00167-011-1483-3 [CrossRef]
  107. Davis ET, Pagkalos J, Gallie PA, Macgroarty K, Waddell JP, Schemitsch EH. Defining the errors in the registration process during imageless computer navigation in total knee arthroplasty: a cadaveric study. J Arthroplasty. 2014; 29(4):698–701. doi:10.1016/j.arth.2013.06.034 [CrossRef]
  108. List JP, Chawla H, Joskowicz L, Pearle AD. Current state of computer navigation and robotics in unicompartmental and total knee arthroplasty: a systematic review with meta-analysis. Knee Surg Sports Traumatol Arthrosc. 2016; 24(11):3482–3495. doi:10.1007/s00167-016-4305-9 [CrossRef]
  109. An VV, Sivakumar BS, Phan K, Levy YD, Bruce WJ. Accuracy of MRI-based vs. CT-based patient-specific instrumentation in total knee arthroplasty: a meta-analysis. J Orthop Sci. 2017; 22(1):116–120. doi:10.1016/j.jos.2016.10.007 [CrossRef]
  110. Voleti PB, Hamula MJ, Baldwin KD, Lee GC. Current data do not support routine use of patient-specific instrumentation in total knee arthroplasty. J Arthroplasty. 2014; 29(9):1709–1712. doi:10.1016/j.arth.2014.01.039 [CrossRef]
  111. Jiang J, Kang X, Lin Q, et al. Accuracy of patient-specific instrumentation compared with conventional instrumentation in total knee arthroplasty. Orthopedics. 2015; 38(4):e305–e313. doi:10.3928/01477447-20150402-59 [CrossRef]
  112. Thienpont E, Schwab PE, Fennema P. A systematic review and meta-analysis of patient-specific instrumentation for improving alignment of the components in total knee replacement. Bone Joint J. 2014; 96-B(8):1052–1061. doi:10.1302/0301-620X.96B8.33747 [CrossRef]
  113. Huijbregts H, Khan RJ, Sorensen E, Fick DP, Haebich S. Patient-specific instrumentation does not improve radiographic alignment or clinical outcomes after total knee arthroplasty. Acta Orthop. 2016; 87(4):386–394. doi:10.1080/17453674.2016.1193799 [CrossRef]
  114. Hamilton WG, Parks NL, Saxena A. Patient-specific instrumentation does not shorten surgical time: a prospective, randomized trial. J Arthroplasty. 2013; 28(8):96–100. doi:10.1016/j.arth.2013.04.049 [CrossRef]
  115. Stronach BM, Pelt CE, Erickson J, Peters CL. Patient-specific total knee arthroplasty required frequent surgeon-directed changes. Clin Orthop Relat Res. 2013; 471(1):169–174. doi:10.1007/s11999-012-2573-3 [CrossRef]
  116. Sassoon A, Nam D, Nunley R, Barrack R. Systematic review of patient-specific instrumentation in total knee arthroplasty: new but not improved. Clin Orthop Relat Res. 2015; 473(1):151–158. doi:10.1007/s11999-014-3804-6 [CrossRef]
  117. Centers for Medicare & Medicaid Services. Comprehensive care for joint replacement model. https://innovation.cms.gov/initiatives/CJR. Accessed July 21, 2016.
  118. Center for the Evaluation of Value and Risk in Health. The cost-effectiveness analysis registry. Tufts-New England Medical Center, ICRHPS. https://www.tuftsmedicalcenter.org/Research-Clinical-Trials/Institutes-Centers-Labs/Center-for-Evaluation-of-Value-and-Risk-in-Health/CEVR-Value-Databases.aspx. Accessed August 16, 2016.
  119. Brauer CA, Rosen AB, Olchanski NV, Neumann PJ. Cost-utility analyses in orthopaedic surgery. J Bone Joint Surg Am. 2005; 87(6):1253–1259.
  120. Laupacis A, Feeny D, Detsky AS, Tugwell PX. How attractive does a new technology have to be to warrant adoption and utilization? Tentative guidelines for using clinical and economic evaluations. CMAJ. 1992; 146(4):473–481.
  121. Dong H, Buxton M. Early assessment of the likely cost-effectiveness of a new technology: a Markov model with probabilistic sensitivity analysis of computer-assisted total knee replacement. Int J Technol Assess Health Care. 2006; 22(2):191–202. doi:10.1017/S0266462306051014 [CrossRef]
  122. Centers for Medicare & Medicaid Services. MEDPAR (Medicare Provider Analysis and Review File). https://www.cms.gov/Research-Statistics-Data-and-Systems/Files-for-Order/LimitedDataSets/MEDPARLD-SHospitalNational.html. Accessed August 12, 2016.
  123. Leone W, Geller J, Chow J, et al. Using sensors to evaluate revision TKA: treating the “looks good, feels bad” knee. EC Orthopaedics. 2016; 3(5):381–385.
  124. Gustke K. Use of smart trials for soft-tissue balancing in total knee replacement surgery. J Bone Joint Surg Br. 2012; 94(11)(suppl A):147–150. doi:10.1302/0301-620X.94B11.30621 [CrossRef]
  125. Roche MW, Elson LC, Anderson CR. A novel technique using sensor-based technology to evaluate tibial tray rotation. Orthopedics. 2015; 38(3):e217–e222. doi:10.3928/01477447-20150305-60 [CrossRef]
  126. Gustke KA. Soft-tissue and alignment correction: the use of smart trials in total knee replacement. Bone Joint J. 2014; 96-B(11) (suppl A):78–83. doi:10.1302/0301-620X.96B11.34339 [CrossRef]
  127. Leone WA, Elson LC, Anderson CR. A systematic literature review of three modalities in technologically assisted TKA. Adv Orthop. 2015; 2015:719091. doi:10.1155/2015/719091 [CrossRef]
  128. Brin YS, Nikolaou VS, Joseph L, Zukor DJ, Antoniou J. Imageless computer assisted versus conventional total knee replacement: a Bayesian meta-analysis of 23 comparative studies. Int Orthop. 2011; 35(3):331–339. doi:10.1007/s00264-010-1008-6 [CrossRef]
  129. Cheng T, Zhao S, Peng X, Zhang X. Does computer-assisted surgery improve postoperative leg alignment and implant positioning following total knee arthroplasty? A meta-analysis of randomized controlled trials. Knee Surg Sports Traumatol Arthrosc. 2012; 20(7):1307–1322. doi:10.1007/s00167-011-1588-8 [CrossRef]
  130. Delanois R, Mistry JB, Chughtai M, et al. Novel sensor tibial inserts in total knee arthroplasty: a review. Surg Technol Int. 2016; 10:255–260.
  131. Elmallah RK, Mistry JB, Cherian JJ, et al. Can we really “feel” a balanced total knee arthroplasty?J Arthroplasty. 2016; 31(9)(suppl):102–105. doi:10.1016/j.arth.2016.03.054 [CrossRef]
  132. Xie C, Liu K, Xiao L, Tang R. Clinical outcomes after computer-assisted versus conventional total knee arthroplasty. Orthopedics. 2012; 35(5):e647–e653. doi:10.3928/01477447-20120426-17 [CrossRef]
  133. Hoffart HE, Langenstein E, Vasak N. A prospective study comparing the functional outcome of computer-assisted and conventional total knee replacement. J Bone Joint Surg Br. 2012; 94(2):194–199. doi:10.1302/0301-620X.94B2.27454 [CrossRef]
  134. Huang NF, Dowsey MM, Ee E, Stoney JD, Babazadeh S, Choong PF. Coronal alignment correlates with outcome after total knee arthroplasty: five-year follow-up of a randomized controlled trial. J Arthroplasty. 2012; 27(9):1737–1741. doi:10.1016/j.arth.2012.03.058 [CrossRef]
  135. Lutzner J, Dexel J, Kirchner S. No difference between computer-assisted and conventional total knee arthroplasty: five-year results of a prospective randomized study. Knee Surg Sports Traumatol Arthrosc. 2013; 21(10):2241–2247. doi:10.1007/s00167-013-2608-7 [CrossRef]

Main Robotic-Assisted Systems in the United States and Europe

TypeNameManufacturerCountryIntroduction YearIndicationTechniqueImageCurrent Status
Semi-activeMAKOMAKO-Stryker, Fort Lauderdale, FloridaUS2005UKA, TKA, THABurring sawCTCurrently used
Semi-activeNavio Surgical SystemSmith & Nephew, Memphis, TennesseeUS2012UKA, TKABurringImagelessCurrently used
Semi-activeOMNIBotics iBlockOMNI, Raynham, MassachusettsUS2004TKACutting guideCTCurrently used, iBlock FDA approved
ActiveROBODOCTHINK Surgical Inc, Fremont, CaliforniaUS Germany1992 1994THA, TKAMillingCTAvailable but use declining
ActiveTSolution OneTHINK Surgical IncUS Europe2015THA, TKAMillingCTTKA not available in US
ActiveComputer-Assisted Surgical Planning and Robotics (CASPAR)URS Ortho GmbH & Co KG, Mecklenburg-Vorpommern, GermanyGermany2000THA, TKAMillingCTNo longer available
Semi-activeStanmore Sculptor System (SSS)Acrobot, London, EnglandEngland2002UKA, TKACuttingCTNo longer available

Main Patient-Specific Instrumentation Systems Currently on the Market

NameManufacturerPreoperative Imaging
PSIZimmer, Inc, Warsaw, IndianaMRI
SignatureBiomet, Warsaw, IndianaMRI or CT
TruMatchDePuy Synthes, Warsaw, IndianaCT
VisionaireSmith & Nephew, Memphis, TennesseeMRI ± radiograph
iTotal G2ConforMIS, Bedford, MassachusettsCT
GMK MyKneeMedacta International, Castel San Pietro, SwitzerlandMRI or CT
ProphecyMicroPort Orthopedics, Shanghai, ChinaMRI or CT

Advanced Technology for Unicompartmental or Total Knee Arthroplasty With Radiographic Outcomes

StudyYearNo. of StudiesSystemProcedureNo. of Navigation CasesNo. of Conventional CasesLatest Follow-up, moConclusionsLevel of EvidenceGrade of Recommendation
Hetaimish et al43201223CAN meta-analysisTKA137413027.5MAA >3° from neutral: 30.1% conventional TKA; 12.8% CAN TKA. Overall: No significant differences in femoral or tibial alignment.1A
Cheng et al129201241CAN meta-analysisTKA22682018-MAA >3° from neutral: 28.3% conventional TKA; 12.2% CAN TKA. Overall: Significant improvement in MAA and component orientation with CAN TKA except tibial sagittal alignment.1A
Zamora et al44201313CAN meta-analysisTKA694656-Significant odds ratio of 2.32 (P<.00001) favoring CAN TKA to obtain satisfactory postoperative neutral MAA.1A
Pang et al10620111CAN gap balancingTKA707024Decreased laxity, improved MAA, fewer outliers in CAN soft tissue TKA group vs conventional TKA group. Overall: CAN gap-balancing technique was able to achieve more precise soft tissue balance and restoration of limb alignment.1A
Song et al8620131ROBODOC; THINK Surgical Inc, Fremont, CaliforniaTKA505041RAS TKA reduced the number of MAA outliers and improved the ability to achieve flexion–extension gap balance vs conventional TKA.1A
Mason et al40200729CAN meta-analysisTKA17451692-MAA >3° from neutral: 31.8% conventional TKA; 9% CAN TKA. Overall: Significant improvement in mechanical, femoral, and tibial alignment with CAN TKA.3B
Brin et al128201123CAN meta-analysisTKA21602039-MAA >3° from neutral: 18.6% conventional TKA; 4.3% CAN TKA. Overall: CAN TKA significantly reduces the number of outliers in MAA and coronal position by a rate of 80%.3B
Bauwens et al51200733CAN meta-analysisTKA17071716-Conventional TKA mean mechanical axis: 179.9°; CAN TKA mean mechanical axis: 179.7°. Overall: No advantage of navigated TKA vs conventional TKA in mean deviance from mechanical axis.3B
Voleti et al11020149PSI meta-analysisTKA529428-No significant differences between PSI TKA and conventional TKA to avoid outliers. Measures of sagittal alignment accuracy were equivalent between the 2 groups for both the femoral and the tibial component. Operative time and blood loss were similar between groups. Overall: Do not support routine use of PSI TKA.3B
Jiang et al111201518PSI meta-analysisTKA12481223-No statistical differences in outliers of MAA, the femoral component in the coronal and the sagittal planes, the tibial component in the coronal and the sagittal planes, and the femoral component rotation. Overall: PSI accuracy not superior to conventional technique.3B
Thienpont et al112201416PSI meta-analysisTKA901854.PSI TKA showed significant advantage of femoral component position in the coronal plane only. Tibial component in PSI TKA in worse alignment compared with conventional TKA. No difference in MAA between PSI and traditional TKA. Overall: PSI does not improve alignment accuracy vs conventional-TKA.3B
Lonner et al7020101MAKO; MAKO-Stryker, Fort Lauderdale, FloridaUKA3133Error of the posterior tibial slope was higher using manual techniques vs RAS for bone preparation. In the coronal plane, the average error was 2.7° more varus of the tibial component relative to the mechanical axis of the tibia using manual instruments compared with 0.2° with robotic technology.3B
Delanois et al13020167Tibial sensor systematic reviewTKA26117012Sensor-guided TKA showed significantly lower loads between compartments throughout the ROM. Use of sensor tibial inserts helps achieve appropriate component orientation.3B
Elmallah et al13120161Tibial sensorTKA1012-Sensor cohort had lower mean differences between the medial and the lateral compartment loading pressures at 10°, 45°, and 90° of flexion.3B
Clark and Schmidt6020131iBlock; OMNI, Raynham, MassachusettsTKA5229 (CAN)-RAS TKA had decreased navigation time (9 minutes), decreased final malalignment, and decreased hospitalization length (0.6 days) vs CAN TKA.4C

Advanced Technology for Unicompartmental or Total Knee Arthroplasty With Clinical Outcomes

StudyYearNo. of StudiesSystemProcedureNo. of Navigation CasesNo. of Conventional CasesLatest Follow-up, moConclusionsLevel of EvidenceGrade of Recommendation
Xie et al132201221CAN meta-analysisTKA13761282-Operative time was significantly increased with the use of CAN TKA. No difference between CAN and conventional TKA regarding total operative blood loss. No difference in final Knee Society Score and ROM between CAN and conventional TKA1A
Huang et al13420121CAN CI System; DePuy Synthes, West Chester, PennsylvaniaTKA464460CAN TKA achieved greater accuracy in implant alignment, and this correlated with better knee function (SF-12) and quality of life (IKS) scores.1A
Lutzner et al13520131CANTKA343360No difference in functional outcome (Knee Society Score) or patient-perceived quality of life after CAN TKA vs conventional TKA at 5 years despite significantly better leg alignment in the CAN TKA group.1A
Choong et al4620091CAN CI SystemTKA605512CAN TKA had superior IKS and SF-12 physical scores at 6 weeks, 3 months, 6 months, and 12 months after surgery. Overall: CAN TKA achieves greater accuracy in implant alignment, and this correlates with better knee function and improved quality of life.1A
Pang et al10620111CAN gap balancingTKA707024At 6-month follow-up, CAN gap-balancing TKA showed better outcomes in function score (P=.040) and total Oxford Knee Score (P=.031). At 2 years, CAN TKA had better outcome in the total Oxford Knee Score (P=.03).1A
Huijbregts et al113201621PSI meta-analysisTKA8057823No statistically significant differences were observed regarding new Knee Society Scores, Knee injury and Osteoarthritis Outcome Scores, or SF-12 scores at 3 months postoperatively. One study found no difference in Knee Society Scores at 6-month follow-up. Another study found 1-year Oxford Knee Scores were marginally improved in the PSI group.1A
Song et al8620131ROBODOC; THINK Surgical Inc, Fremont, CaliforniaTKA505041No differences in postoperative ROM, WOMAC scores, and HSS knee score.1A
Hoffart et al13320121CAN PiGalileo; Smith & Nephew Orthopaedics AG, Baar, SwitzerlandTKA989760Navigation was better than conventional TKA in improving mean Knee Society Score (P=.008). Mean function and knee scores significantly better with navigation (P=.034 and .013, respectively). No difference in pain scores. Overall: Clinical results better with CAN TKA at 5 years.2B
Ishida et al4520111CANTKA303060ROMs and Knee Society Scores were significantly better in the CAN group (P<.01) at 5 years. KSFSs were equal in both groups. Overall: Better alignment and clinical results at mid-term follow-up may provide CAN TKA patients with long-term endurance of their implants.2B
Spencer et al4820071Duracon (Stryker Orthopaedics, St Leonards, New South Wales, Australia) Stryker Image Free computer navigation system (version 1.0; Stryker Orthopaedics)TKA303024No significant differences at 2-year follow-up for any of the investigated scores (Knee Society Score, WOMAC score, Oxford Knee Score, and Bartlett Patellar score) despite better alignment in CAN TKA.2B
Molfetta and Caldo4720081CAN Ortho-Pilot; Aesculap AG & Co, Tuttlingen, GermanyTKA303060No difference in Knee Society Scores and knee function scores between the 2 groups.3B
Delanois et al13020167Tibial sensor meta-analysisTKA26117012Four studies found improved clinical outcome with sensor tibial inserts. The balanced sensor tibial insert group had superior Knee Society Scores and WOMAC scores at 6 and 12 months postoperatively.3B
Hansen et al7220141MAKO; MAKO-Stryker, Fort Lauderdale, FloridaUKA30320.5Postoperative ROM was greater in RAS on the day of surgery (P=.045) but not different at 2 weeks.3B
Gregori et al7920141Navio; Smith & Nephew, Memphis, TennesseeUKA5701.5The Oxford Knee Score showed a clinical improvement from preoperatively to 6 weeks postoperatively.4
Authors

The authors are from the Philadelphia College of Osteopathic Medicine (AS) and the Department of Orthopaedic Surgery (WMH, NPS), University of Pennsylvania, Philadelphia, Pennsylvania; and MaxCure Hospitals (KKE), Telangana, India.

Dr Siddiqi, Mr Hardaker, and Dr Eachempati have no relevant financial relationships to disclose. Dr Sheth is an unpaid consultant for Zimmer Education, Smith & Nephew, and Biorad Medisys.

Correspondence should be addressed to: Ahmed Siddiqi, DO, Philadelphia College of Osteopathic Medicine, 4190 City Ave GME, Philadelphia, PA 19131 ( asiddiqi89@gmail.com).

Received: February 13, 2017
Accepted: April 17, 2017
Posted Online: September 07, 2017

10.3928/01477447-20170831-02

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