Total knee arthroplasty (TKA) using functional alignment aims to implant the components with minimal compromise of the soft-tissue envelope by restoring the plane and obliquity of the non-arthritic joint. The objective of this study was to determine the effect of TKA with functional alignment on mediolateral soft-tissue balance as assessed using intraoperative sensor-guided technology. This prospective study included 30 consecutive patients undergoing robotic-assisted TKA using the Stryker PS Triathlon implant with functional alignment. Intraoperative soft-tissue balance was assessed using sensor-guided technology after definitive component implantation; soft-tissue balance was defined as intercompartmental pressure difference (ICPD) of < 15 psi. Medial and lateral compartment pressures were recorded at 10°, 45°, and 90° of knee flexion. This study included 18 females (60%) and 12 males (40%) with a mean age of 65.2 years (SD 9.3). Mean preoperative hip-knee-ankle deformity was 6.3° varus (SD 2.7°).Aims
Methods
Neither a surgeon’s intraoperative impression nor the parameters of computer navigation have been shown to be predictive of the outcomes following total knee arthroplasty (TKA). The aim of this study was to determine whether a surgeon, with robotic assistance, can predict the outcome as assessed using the Knee Injury and Osteoarthritis Outcome Score (KOOS) for pain (KPS), one year postoperatively, and establish what factors correlate with poor KOOS scores in a well-aligned and balanced TKA. A total of 134 consecutive patients who underwent TKA using a dynamic ligament tensioning robotic system with a tibia first resection technique and a cruciate sacrificing ultracongruent TKA system were enrolled into a prospective study. Each TKA was graded based on the final mediolateral ligament balance at 10° and 90° of flexion: 1) < 1 mm difference in the thickness of the tibial insert and that which was planned (n = 75); 2) < 1 mm difference (n = 26); 3) between 1 mm to 2 mm difference (n = 26); and 4) > 2 mm difference (n = 7). The mean one-year KPS score for each grade of TKA was compared and the likelihood of achieving an KPS score of > 90 was calculated. Finally, the factors associated with lower KPS despite achieving a high-grade TKA (grade A and B) were analyzed.Aims
Methods
Limb alignment in total knee arthroplasty (TKA) influences periarticular soft-tissue tension, biomechanics through knee flexion, and implant survival. Despite this, there is no uniform consensus on the optimal alignment technique for TKA. Neutral mechanical alignment facilitates knee flexion and symmetrical component wear but forces the limb into an unnatural position that alters native knee kinematics through the arc of knee flexion. Kinematic alignment aims to restore native limb alignment, but the safe ranges with this technique remain uncertain and the effects of this alignment technique on component survivorship remain unknown. Anatomical alignment aims to restore predisease limb alignment and knee geometry, but existing studies using this technique are based on cadaveric specimens or clinical trials with limited follow-up times. Functional alignment aims to restore the native plane and obliquity of the joint by manipulating implant positioning while limiting soft tissue releases, but the results of high-quality studies with long-term outcomes are still awaited. The drawbacks of existing studies on alignment include the use of surgical techniques with limited accuracy and reproducibility of achieving the planned alignment, poor correlation of intraoperative data to long-term functional outcomes and implant survivorship, and a paucity of studies on the safe ranges of limb alignment. Further studies on alignment in TKA should use surgical adjuncts (e.g. robotic technology) to help execute the planned alignment with improved accuracy, include intraoperative assessments of knee biomechanics and periarticular soft-tissue tension, and correlate alignment to long-term functional outcomes and survivorship.
The application of robotics in the operating theatre for knee arthroplasty remains controversial. As with all new technology, the introduction of new systems might be associated with a learning curve. However, guidelines on how to assess the introduction of robotics in the operating theatre are lacking. This systematic review aims to evaluate the current evidence on the learning curve of robot-assisted knee arthroplasty. An extensive literature search of PubMed, Medline, Embase, Web of Science, and Cochrane Library was conducted. Randomized controlled trials, comparative studies, and cohort studies were included. Outcomes assessed included: time required for surgery, stress levels of the surgical team, complications in regard to surgical experience level or time needed for surgery, size prediction of preoperative templating, and alignment according to the number of knee arthroplasties performed. A total of 11 studies met the inclusion criteria. Most were of medium to low quality. The operating time of robot-assisted total knee arthroplasty (TKA) and unicompartmental knee arthroplasty (UKA) is associated with a learning curve of between six to 20 cases and six to 36 cases respectively. Surgical team stress levels show a learning curve of seven cases in TKA and six cases for UKA. Experience with the robotic systems did not influence implant positioning, preoperative planning, and postoperative complications. Robot-assisted TKA and UKA is associated with a learning curve regarding operating time and surgical team stress levels. Future evaluation of robotics in the operating theatre should include detailed measurement of the various aspects of the total operating time, including total robotic time and time needed for preoperative planning. The prior experience of the surgical team should also be evaluated and reported. Cite this article:
Robotic-assisted unicompartmental knee arthroplasty (UKA) promises accurate implant placement with the potential of improved survival and functional outcomes. The aim of this study was to present the current evidence for robotic-assisted UKA and describe the outcome in terms of implant positioning, range of movement (ROM), function and survival, and the types of robot and implants that are currently used. A search of PubMed and Medline was performed in October 2018 in line with the Preferred Reporting Items for Systematic Review and Meta-Analysis statement. Search terms included “robotic”, “knee”, and “surgery”. The criteria for inclusion was any study describing the use of robotic UKA and reporting implant positioning, ROM, function, and survival for clinical, cadaveric, or dry bone studies.Aims
Materials and Methods
The use of robotics in arthroplasty surgery is expanding rapidly as improvements in the technology evolve. This article examines current evidence to justify the usage of robotics, as well as the future potential in this emerging field.
Robots have been used in surgery since the late
1980s. Orthopaedic surgery began to incorporate robotic technology
in 1992, with the introduction of ROBODOC, for the planning and
performance of total hip replacement. The use of robotic systems
has subsequently increased, with promising short-term radiological
outcomes when compared with traditional orthopaedic procedures.
Robotic systems can be classified into two categories: autonomous
and haptic (or surgeon-guided). Passive surgery systems, which represent
a third type of technology, have also been adopted recently by orthopaedic
surgeons. While autonomous systems have fallen out of favour, tactile systems
with technological improvements have become widely used. Specifically,
the use of tactile and passive robotic systems in unicompartmental
knee replacement (UKR) has addressed some of the historical mechanisms
of failure of non-robotic UKR. These systems assist with increasing
the accuracy of the alignment of the components and produce more
consistent ligament balance. Short-term improvements in clinical
and radiological outcomes have increased the popularity of robot-assisted
UKR. Robot-assisted orthopaedic surgery has the potential for improving
surgical outcomes. We discuss the different types of robotic systems
available for use in orthopaedics and consider the indication, contraindications
and limitations of these technologies.
