A recent systematic review with meta-analysis of eight randomised controlled trials concluded that Cognitive Functional Therapy (CFT) for low back pain might be effective in reducing disability, pain and fear-avoidance beliefs. However, the descriptions of a CFT intervention are not always clear. This study aimed to rate the replicability of the CFT interventions and control groups in the systematic review. Two reviewers independently extracted data from the study articles, protocols and appendices into Microsoft Excel using the Template for Intervention Description and Replication (TIDieR) checklist. This checklist has 12 items to describe the ‘why’, ‘what’, ‘who’, ‘how’, ‘where’, ‘when and how much’, ‘tailoring’, ‘modifications’, and ‘how well’ for each intervention. We rated the replicability of the CFT interventions and control groups as ‘reported’, ‘partially reported’ and ‘not reported’ and resolved discrepancies by consensus.Background and study purpose
Methods
The medial-stabilised (MS) knee implant, characterised by a spherical medial condyle on the femoral component and a medially congruent tibial bearing, was developed to improve knee kinematics and stability relative to performance obtained in posterior-stabilised (PS) and cruciate-retaining (CR) designs. We aimed to compare in vivo six-degree-of-freedom (6-DOF) kinematics during overground walking for these three knee designs. Seventy-five patients (42 males, 33 females, age 68.4±6.6 years) listed for total knee arthroplasty (TKA) surgery were recruited to this study, which was approved by the relevant Human Research Ethics committees. Each patient was randomly- assigned a PS, CR or MS knee (Medacta International AB, Switzerland) resulting in three groups of 23, 26 and 26 patients, respectively. Patients visited the Biomotion Laboratory at the University of Melbourne 6±1.1 months after surgery, where they walked overground at their self-selected speed. A custom Mobile Biplane X-ray (MoBiX) imaging system tracked and imaged the implanted knee at 200 Hz. The MoBiX system measures 6-DOF tibiofemoral kinematics of TKA knees during overground gait with maximum RMS errors of 0.65° and 0.33 mm for rotations and translations, respectively.INTRODUCTION
METHODS
Accurate knowledge of knee joint kinematics following total knee arthroplasty (TKA) is critical for evaluating the functional performance of specific implant designs. Biplane fluoroscopy is currently the most accurate method for measuring 3D knee joint kinematics in vivo during daily activities such as walking. However, the relatively small imaging field of these systems has limited measurement of knee kinematics to only a portion of the gait cycle. We developed a mobile biplane X-ray (MoBiX) fluoroscopy system that enables concurrent tracking and imaging of the knee joint for multiple cycles of overground gait. The primary aim of the present study was to measure 6-degree-of-freedom (6-DOF) knee joint kinematics for one complete cycle of overground walking. A secondary aim was to quantify the position of the knee joint centre of rotation (COR) in the transverse plane during TKA gait. Ten unilateral posterior-stabilised TKA patients (5 females, 5 males) were recruited to the study. Each subject walked over ground at their self-selected speed (0.93±0.12 m/s). The MoBiX imaging system tracked and recorded biplane X-ray images of the knee, from which tibiofemoral kinematics were calculated using an image processing and pose-estimation pipeline created in MATLAB. Mean 6-DOF tibiofemoral joint kinematics were plotted against the mean knee flexion angle for one complete cycle of overground walking. The joint COR in the transverse plane was calculated as the least squares intersection of the femoral flexion axis projected onto the tibial tray during the stance and swing phases. The femoral and tibial axes and 6-DOF kinematics were defined in accordance with the convention defined by Grood and Suntay in 1983.INTRODUCTION
METHODS
Orthopaedic surgeons use stems in revision knee surgery to obtain
stability when metaphyseal bone is missing. No consensus exists
regarding stem size or method of fixation. This A custom test rig using differential variable reluctance transducers
(DVRTs) was developed to record all translational and rotational
motions at the bone–implant interface. Composite femurs were used.
These were secured to permit variation in flexion angle from 0°
to 90°. Cyclic loads were applied through a tibial component based
on three peaks corresponding to 0°, 10° and 20° flexion from a normal
walking cycle. Three different femoral components were investigated
in this study for cementless and cemented interface conditions.Objectives
Methods
Iterative finite element (FE) models are used to simulate bone remodelling that takes place due to the surgical insertion of an implant or to simulate fracture healing. In such simulations element material properties are calculated after each iteration of solving the model. New material properties are calculated based on the results derived by the model during the last iteration. Once the FE model has gone through a number of such iterations it is often necessary to assess the remodelling that has taken place. The method widely used to do this is to analyse element Young's modulus plots taken at particular sections through the model. Although this method gives relevant information which is often helpful when comparing different implants, the information is rather abstract and is difficult to compare with patient data which is commonly in the form of radiographs. The authors suggest a simple technique that can be used to generate synthetic radiograph images from FE models. These images allow relatively easy comparisons of FE derived information with patient radiographs. Another clear advantage of this technique is that clinicians (who are familiar with reading radiographs) are able to understand and interpret them readily. To demonstrate the technique a three dimensional (3D) model of the proximal tibia implanted with an Oxford Unicompartmental Knee replacement was created based on CT data obtained from a cadaveric tibia. The model's initial element material properties were calculated from the same CT data set using a relationship between radiographic density and Young's modulus. The model was subject to simplified loading conditions and solved over 365 iterations representing one year of in vivo remodelling. After each iteration the element material properties were recalculated based on previously published remodelling rules. Next, synthetic anteroposterior radiographs were generated by back calculating radiographic densities from material properties of the model after 365 iterations. A 3D rectangular grid of sampling points which encapsulated the model was defined. For each of the elements in the FE model radiographic densities were back calculated based on the same relationships used to calculate material properties from radiographic densities. The radiographic density of each element was assigned to all the sampling grid points within the element. The 3D array of radiographic densities was summed in the anteroposterior direction thereby creating a 2D array of radiographic densities. This 2D array was plotted giving an image analogous to anteroposterior patient radiographs. Similar to a patient radiograph denser material appeared lighter while less dense material appeared darker. The resulting synthetic radiographs were compared to patient radiographs and found to have similar patterns of dark and light regions. The synthetic radiographs were relatively easy to produce based on the FE model results, represented FE results in a manner easily comparable to patient radiographs, and represented FE results in a clinician friendly manner.
patients’ pre-operative demographics for age, weight, height, BMI, intraoperative variables such as the operating surgeon (n=2), insert and component sizes, and clinical assessment criteria including pre-operative and five-year post-operative Oxford knee (OKS) and Tegner (TS) scores.
In finite element (FE) analysis of long bones it is now common practice to calculate the material properties based on CT data. Although a unique material property is calculated for each element, assigning each element an individual material property results in excessively large models. To avoid this, it is usual to group the elements based on their material properties and to assign each group a single material property (Zannoni 1998). No study has analysed the effect the number of material properties used in a long bone FE model has on the accuracy of the results. The aim of this study was to evaluate the variation in the calculated mechanical environment as a function of the number of material properties used in an FE model. An FE mesh of a cadaveric human tibia containing 47,696 ten-node tetrahedron elements and 75,583 nodes was created using CT scans. Material properties were calculated for each element of the mesh based on previous work (Rho 1995, 1996). Eleven FE models were created by varying the number of groups (1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024) the elements were divided into. A single material property was assigned to each group. All models were subject to an axial point load of 300N applied on the medial condyle of the tibial plateau while the distal end was fixed. The variation in maximum and minimum principal strains and deflections, at 17 well distributed surface nodes and at 65 randomly distributed nodes within the bone were plotted against the number of element groups. The total strain energy was also plotted against the number of groups. The errors for strain, deflection, and total strain energy were calculated for each model assuming that the model using 1024 element groups was accurate. The parameter to converge with the least number of element groups was the total strain energy. At 512 element groups the error was less than 0.001% (0.7% for the two material model). The next to converge were the displacements. Using 512 materials the maximum error in displacement at the surface nodes was 0.001% (4.7% for the 2 material model), while for the internal nodes the maximum error was 0.53% (36.7% for the 2 material model). The least convergence occurred for principal strains. The maximum errors when 512 materials were used were 1.06% (57.7% for the 2 material model) and 3.02% (104.5% for the 2 material model) for the surface and the internal nodes respectively. This study demonstrates the relationship between the accuracy of calculated mechanical environment and the number of material properties assigned to the model. While this study will allow the analyst to make an informed decision on the number of material properties for modelling the human tibia it also helps examine the validity of previous studies which, usually due to limited resources, used fewer material properties.
Accurate placement of unicompartmental knee arthroplasty components is thought to be essential for the long-term survival and efficacy of the prosthesis. Computer navigation is being explored as a means of improving the accuracy of component position. There are few published studies comparing conventional and computer-navigated techniques using the same prosthesis. Twenty-two Allegretto [Zimmer] medial unicompartmental knee prostheses were placed in 18 patients using the AxiEM [Medtronic] computer-navigated system. The immediate post-operative AP and lateral radiographs were analysed and compared with an equivalent cohort of 30 prostheses in 29 patients with medial unicompartmental arthritis in whom the Allegretto was placed without the aid of computer navigation. All operations were performed by the senior author in a rural Queensland hospital. No cases were lost to follow-up. The data was not normally distributed. The mean, SD and variance of the data sets was calculated and significance tested with a 2-tailed Mann-Whitney U-test. Computer navigated tibial components were implanted with a mean of 2 degrees of varus compared with 1 degree of valgus with conventional navigation [p = 0.027]. Our target was 0–4 degrees of varus. Eighteen of the 20 computer-navigated cases, 90% fell within the recommended range [0–4 degrees of varus] compared with only 40%, 12 of the 30 conventionally-implanted cases. This is demonstrated by the greater range and variance of the conventional navigation data set. Posterior slope for the computer navigated components was 1 degree compared with 3 degrees for conventional navigation [0.010]; only 1 computed navigated component [5%] was implanted with anterior slope compared with 4 cases for conventional navigation [13%]. Measurements of femoral component flexion and position with respect to the tibial component were not significantly different but demonstrated greater variance for the conventionally navigated data set. Accurate component positioning improves efficacy and prosthesis survival for patients who meet the indications for unicompartmental surgery. However proponents acknowledge the weaknesses of conventional jigs for unicompartmental prostheses. In this study computer navigation has been shown to improve the accuracy of component placement.
Kinematic data from in-vivo fluoroscopy measurements during a step-up activity was used to determine the relative tibial-femoral position as a function of knee flexion angle for each model. Medial and lateral force distribution was adapted from loads measured in-vivo with an instrumented implant during a step-up activity. The affect that varying the bearing thickness has on the stresses in the bearing was investigated. In addition, varus-valgus mal-alignment was investigated by rotating the femoral component through 10 degrees.
patients’ pre-operative demographics for age, weight, height, BMI, intra-operative variables such as the operating surgeon (n=2), insert and component sizes, post-operative varus/valgus deformity, and clinical outcome, assessed by the change in Oxford knee (OKS) and Tegner (TS) scores, from before surgery to five-year post-operatively.
We found no significant relationship between physiological RL, pre-operative demographics, intra-operative variables and clinical outcome scores in this study. Tibial RL remains a common finding following the Oxford UKA yet we do not know why it occurs but in the medium term, clinical outcome is not influenced by RL. In particular, it is not a sign of loosening. Physiological RL can therefore be ignored even if associated with adverse symptoms following the Oxford UKA.
Finite element (FE) analysis is widely used to calculate stresses and strains within human bone in order to improve implant designs. Although validated FE models of the human femur have been created (Lengsfeld et al., 1998), no equivalent yet exists for the tibia. The aim of this study was to create such an FE model, both with and without the tibial component of a knee replacement, and to validate it against experimental Results: A set of reference axes was marked on a cleaned, fresh frozen cadaveric human tibia. Seventeen triaxial stacked strain rosettes were attached along the bone, which was then subjected to nine axial loading conditions, two four-point bending loading conditions, and a torsional loading condition using a materials testing machine (MTS 858). Deflections and strain readings were recorded. Axial loading was repeated after implantation of a knee replacement (medial tibial component, Biomet Oxford Unicompartmental Phase 3). The intact tibia was CT scanned (GE HiSpeed CT/i) and the images used to create a 3D FE mesh. The CT data was also used to map 600 transversely isotropic material properties (Rho, 1996) to individual elements. All experiments were simulated on the FE model. Measured principal strains and displacements were compared to their corresponding FE values using regression analysis. Experimental results were repeatable (mean coefficients of variation for intact and implanted tibia, 5.3% and 3.9%). They correlated well with those of the FE analysis (R squared = 0.98, 0.97, 0.97, and 0.99 for axial (intact), axial (implanted), bending, torsional loading). For each of the load cases the intersects of the regression lines were small in comparison to the maximum measured strains (<
1.5%). While the model was more rigid than the bone under torsional loading (slope =0.92), the opposite was true for axial (slope = 1.14 (intact) 1.24 (implanted)) and bending (slope = 1.06) loads. This is probably due to a discrepancy in the material properties of the model.
Finite element (FE) analysis is widely used to calculate stresses and strains within human bone in order to improve implant designs. Although validated FE models of the human femur have been created (Lengsfeld et al., 1998), no equivalent yet exists for the tibia. The aim of this study was to create such an FE model, both with and without the tibial component of a knee replacement, and to validate it against experimental results. A set of reference axes was marked on a cleaned, fresh frozen cadaveric human tibia. Seventeen triaxial stacked strain rosettes were attached along the bone, which was then subjected to nine axial loading conditions, two four-point bending loading conditions, and a torsional loading condition using a materials testing machine (MTS 858). Deflections and strain readings were recorded. Axial loading was repeated after implantation of a knee replacement (medial tibial component, Biomet Oxford Unicompartmental Phase 3). The intact tibia was CT scanned (GE HiSpeed CT/i) and the images used to create a 3D FE mesh. The CT data was also used to map 600 transversely isotropic material properties (Rho, 1996) to individual elements. All experiments were simulated on the FE model. Measured principal strains and displacements were compared to their corresponding FE values using regression analysis. Experimental results were repeatable (mean coeffi-cients of variation for intact and implanted tibia, 5.3% and 3.9%). They correlated well with those of the FE analysis (R squared = 0.98, 0.97, 0.97, and 0.99 for axial (intact), axial (implanted), bending, torsional loading). For each of the load cases the intersects of the regression lines were small in comparison to the maximum measured strains (<
1.5%). While the model was more rigid than the bone under torsional loading (slope =0.92), the opposite was true for axial (slope = 1.14 (intact) 1.24 (implanted)) and bending (slope = 1.06) loads. This is probably due to a discrepancy in the material properties of the model.