Aims. Robotic-assisted total knee arthroplasty (RA-TKA) is theoretically more accurate for component positioning than TKA performed with mechanical instruments (M-TKA). Furthermore, the ability to incorporate soft-tissue laxity data into the plan prior to bone resection should reduce variability between the planned polyethylene thickness and the final implanted polyethylene. The purpose of this study was to compare accuracy to plan for component positioning and precision, as demonstrated by deviation from plan for polyethylene insert thickness in measured-resection RA-TKA versus M-TKA. Methods. A total of 220 consecutive primary TKAs between May 2016 and November 2018, performed by a single surgeon, were reviewed. Planned coronal plane component alignment and overall limb alignment were all 0° to the mechanical axis; tibial posterior slope was 2°; and polyethylene thickness was 9 mm. For RA-TKA, individual component position was adjusted to assist gap-balancing but planned coronal plane alignment for the femoral and tibial components and overall limb alignment remained 0 ± 3°; planned tibial posterior slope was 1.5°. Mean deviations from plan for each parameter were compared between groups for positioning and size and outliers were assessed. Results. In all, 103 M-TKAs and 96 RA-TKAs were included. In RA-TKA versus M-TKA, respectively: mean femoral positioning (0.9° (SD 1.2°) vs 1.7° (SD 1.1°)), mean tibial positioning (0.3° (SD 0.9°) vs 1.3° (SD 1.0°)), mean posterior tibial slope (-0.3° (SD 1.3°) vs 1.7° (SD 1.1°)), and mean mechanical axis limb alignment (1.0° (SD 1.7°) vs 2.7° (SD 1.9°)) all deviated significantly less from the plan (all p < 0.001); significantly fewer knees required a distal femoral recut (10 (10%) vs 22 (22%), p = 0.033); and deviation from planned polyethylene thickness was significantly less (1.4 mm (SD 1.6) vs 2.7 mm (SD 2.2), p < 0.001). Conclusion. RA-TKA is significantly more accurate and precise in planning both component positioning and final polyethylene insert thickness. Future studies should investigate whether this increased accuracy and precision has an impact on clinical outcomes. The greater accuracy and reproducibility of RA-TKA may be important as precise new goals for component positioning are developed and can be further individualized to the patient. Cite this article: Bone Joint J 2021;103-B(6 Supple A):74–80

The object of this study was to develop a method to assess the accuracy of an image-free total knee replacement navigation system in legs with normal or abnormal mechanical axes. A phantom leg was constructed with simulated hip and knee joints and provided a means to locate the centre of the ankle joint. Additional joints located at the midshaft of the tibia and femur allowed deformation in the flexion/extension, varus/valgus and rotational planes. Using a digital caliper unit to measure the coordinates precisely, a software program was developed to convert these local coordinates into a determination of actual leg alignment. At specific points in the procedure, information was compared between the digital caliper measurements and the image-free navigation system. Repeated serial measurements were undertaken. In the setting of normal alignment the mean error of the system was within 0.5°. In the setting of abnormal plane alignment in both the femur and the tibia, the error was within 1°. This is the first study designed to assess the accuracy of a clinically-validated navigation system. It demonstrates in vitro accuracy of the image-free navigation system in both normal and abnormal leg alignment settings

We aimed to determine the reliability, accuracy and the clinical role of digital templating in the pre-operative work-up for total knee replacement. Initially a sample of ten pre-operative digital radiographs were templated by four independent observers to determine the inter- and intra-observer reliability of the process. Digital templating was then performed on the radiographs of 40 consecutive patients undergoing total knee replacement by a consultant surgeon not involved with the operation, who was blinded to the size of the implant inserted. The Press Fit Condylar Sigma Knee system was used in all the patients. The size of the implant as judged by templating was then compared to that of the size used. Good inter- and intra-observer agreement was demonstrated for both femoral and tibial templating. However, the correct size of the implant was predicted in only 48% of the femoral and 55% of the tibial components. Albeit reproducible, digital templating does not currently predict the correct size of component often enough to be of clinical benefit

Our study evaluated the accuracy of an image-guided total knee replacement system based on CT with regard to preparation of the femoral and tibial bone using nine limbs from five cadavers. The accuracy was assessed by direct measurement using an extramedullary alignment rod without radiographs. The mean angular errors of the femur and tibia, which represent angular gaps from the real mechanical axis in the coronal plane, were 0.3° and 1.1°, respectively. The CT-based system, provided almost perfect alignment of the femoral component with less than 1° of error and excellent alignment with less than 3° of error for the tibial component. Our results suggest that standardisation of knee replacement by the use of this system will lead to improved long-term survival of total knee arthroplasty

The diagnosis of a meniscal tear may require MRI, which is costly. Ultrasonography has been used to image the meniscus, but there are no reliable data on its accuracy. We performed a prospective study investigating the sensitivity and specificity of ultrasonography in comparison with MRI; the final outcome was determined at arthroscopy. The study included 35 patients with a mean age of 47 years (14 to 73). There was a sensitivity of 86.4% (95% confidence interval (CI) 75 to 97.7), a specificity of 69.2% (95% CI 53.7 to 84.7), a positive predictive value of 82.6% (95% CI 70 to 95.2) and a negative predictive value of 75% (95% CI 60.7 to 81.1) for ultrasonography. This compared favourably with a sensitivity of 86.4% (95% CI 75 to 97.7), a specificity of 100.0%, a positive predictive value of 100.0% and a negative predictive value of 81.3% (95% CI 74.7 to 87.9) for MRI. Given that the sensitivity matched that of MRI we feel that ultrasonography can reasonably be applied to confirm the clinical diagnosis before undertaking arthroscopy. However, the lower specificity suggests that there is still a need to improve the technique to reduce the number of false-positive diagnoses and thus to avoid unnecessary arthroscopy

Aims. The diagnosis of periprosthetic joint infection (PJI) continues to present a significant clinical challenge. New biomarkers have been proposed to support clinical decision-making; among them, synovial fluid alpha-defensin has gained interest. Current research methodology suggests reference methods are needed to establish solid evidence for use of the test. This prospective study aims to evaluate the diagnostic accuracy of high-performance liquid chromatography coupled with the mass spectrometry (LC-MS) method to detect alpha-defensin in synovial fluid. Methods. Between October 2017 and September 2019, we collected synovial fluid samples from patients scheduled to undergo revision surgery for painful total knee arthroplasty (TKA). The International Consensus Meeting criteria were used to classify 33 PJIs and 92 aseptic joints. LC-MS assay was performed to measure alpha-defensin in synovial fluid of all included patients. Sensitivity, specificity, positive predictive value, negative predictive value, and the area under the receiver operating characteristic curve (AUC) were calculated to define the test diagnostic accuracy. Results. The AUC was 0.99 (95% confidence interval (CI) 0.98 to 1.00). Receiver operating characteristic (ROC) analysis showed that the optimal cut-off value of synovial fluid alpha-defensin was 1.0 μg/l. The sensitivity of alpha-defensin was 100% (95% CI 96 to 100), the specificity was 97% (95% CI 90 to 98), the positive predictive value was 89.2% (95% CI 82 to 94), and negative predictive value was 100% (95% CI 96 to 100). ROC analysis demonstrated an AUC of 0.99 (95% CI 0.98 to 1.0). Conclusion. The present study confirms the utility of alpha-defensin in the synovial fluid in patients with painful TKA to select cases of PJI. Since LC-MS is still a time-consuming technology and is available in highly specialized laboratories, further translational research studies are needed to take this evidence into routine procedures and promote a new diagnostic approach. Cite this article: Bone Joint J 2022;104-B(9):1047–1051

Aims. The purpose of this study was to develop a personalized outcome prediction tool, to be used with knee arthroplasty patients, that predicts outcomes (lengths of stay (LOS), 90 day readmission, and one-year patient-reported outcome measures (PROMs) on an individual basis and allows for dynamic modifiable risk factors. Methods. Data were prospectively collected on all patients who underwent total or unicompartmental knee arthroplasty at a between July 2015 and June 2018. Cohort 1 (n = 5,958) was utilized to develop models for LOS and 90 day readmission. Cohort 2 (n = 2,391, surgery date 2015 to 2017) was utilized to develop models for one-year improvements in Knee Injury and Osteoarthritis Outcome Score (KOOS) pain score, KOOS function score, and KOOS quality of life (QOL) score. Model accuracies within the imputed data set were assessed through cross-validation with root mean square errors (RMSEs) and mean absolute errors (MAEs) for the LOS and PROMs models, and the index of prediction accuracy (IPA), and area under the curve (AUC) for the readmission models. Model accuracies in new patient data sets were assessed with AUC. Results. Within the imputed datasets, the LOS (RMSE 1.161) and PROMs models (RMSE 15.775, 11.056, 21.680 for KOOS pain, function, and QOL, respectively) demonstrated good accuracy. For all models, the accuracy of predicting outcomes in a new set of patients were consistent with the cross-validation accuracy overall. Upon validation with a new patient dataset, the LOS and readmission models demonstrated high accuracy (71.5% and 65.0%, respectively). Similarly, the one-year PROMs improvement models demonstrated high accuracy in predicting ten-point improvements in KOOS pain (72.1%), function (72.9%), and QOL (70.8%) scores. Conclusion. The data-driven models developed in this study offer scalable predictive tools that can accurately estimate the likelihood of improved pain, function, and quality of life one year after knee arthroplasty as well as LOS and 90 day readmission. Cite this article: Bone Joint J 2020;102-B(9):1183–1193

Aims. The primary aim of this study was to compare the postoperative systemic inflammatory response in conventional jig-based total knee arthroplasty (conventional TKA) versus robotic-arm assisted total knee arthroplasty (robotic TKA). Secondary aims were to compare the macroscopic soft tissue injury, femoral and tibial bone trauma, localized thermal response, and the accuracy of component positioning between the two treatment groups. Methods. This prospective randomized controlled trial included 30 patients with osteoarthritis of the knee undergoing conventional TKA versus robotic TKA. Predefined serum markers of inflammation and localized knee temperature were collected preoperatively and postoperatively at six hours, day 1, day 2, day 7, and day 28 following TKA. Blinded observers used the Macroscopic Soft Tissue Injury (MASTI) classification system to grade intraoperative periarticular soft tissue injury and bone trauma. Plain radiographs were used to assess the accuracy of achieving the planned postioning of the components in both groups. Results. Patients undergoing conventional TKA and robotic TKA had comparable changes in the postoperative systemic inflammatory and localized thermal response at six hours, day 1, day 2, and day 28 after surgery. Robotic TKA had significantly reduced levels of interleukin-6 (p < 0.001), tumour necrosis factor-α (p = 0.021), ESR (p = 0.001), CRP (p = 0.004), lactate dehydrogenase (p = 0.007), and creatine kinase (p = 0.004) at day 7 after surgery compared with conventional TKA. Robotic TKA was associated with significantly improved preservation of the periarticular soft tissue envelope (p < 0.001), and reduced femoral (p = 0.012) and tibial (p = 0.023) bone trauma compared with conventional TKA. Robotic TKA significantly improved the accuracy of achieving the planned limb alignment (p < 0.001), femoral component positioning (p < 0.001), and tibial component positioning (p < 0.001) compared with conventional TKA. Conclusion. Robotic TKA was associated with a transient reduction in the early (day 7) postoperative inflammatory response but there was no difference in the immediate (< 48 hours) or late (day 28) postoperative systemic inflammatory response compared with conventional TKA. Robotic TKA was associated with decreased iatrogenic periarticular soft tissue injury, reduced femoral and tibial bone trauma, and improved accuracy of component positioning compared with conventional TKA. Cite this article: Bone Joint J 2021;103-B(1):113–122

Aims. The aim of this study was to evaluate the ability of a machine-learning algorithm to diagnose prosthetic loosening from preoperative radiographs and to investigate the inputs that might improve its performance. Methods. A group of 697 patients underwent a first-time revision of a total hip (THA) or total knee arthroplasty (TKA) at our institution between 2012 and 2018. Preoperative anteroposterior (AP) and lateral radiographs, and historical and comorbidity information were collected from their electronic records. Each patient was defined as having loose or fixed components based on the operation notes. We trained a series of convolutional neural network (CNN) models to predict a diagnosis of loosening at the time of surgery from the preoperative radiographs. We then added historical data about the patients to the best performing model to create a final model and tested it on an independent dataset. Results. The convolutional neural network we built performed well when detecting loosening from radiographs alone. The first model built de novo with only the radiological image as input had an accuracy of 70%. The final model, which was built by fine-tuning a publicly available model named DenseNet, combining the AP and lateral radiographs, and incorporating information from the patient’s history, had an accuracy, sensitivity, and specificity of 88.3%, 70.2%, and 95.6% on the independent test dataset. It performed better for cases of revision THA with an accuracy of 90.1%, than for cases of revision TKA with an accuracy of 85.8%. Conclusion. This study showed that machine learning can detect prosthetic loosening from radiographs. Its accuracy is enhanced when using highly trained public algorithms, and when adding clinical data to the algorithm. While this algorithm may not be sufficient in its present state of development as a standalone metric of loosening, it is currently a useful augment for clinical decision making. Cite this article: Bone Joint J 2020;102-B(6 Supple A):101–106

Aims. The purpose of this investigation was to determine the relationship between height, weight, and sex with implant size in total knee arthroplasty (TKA) using a multivariate linear regression model and a Bayesian model. Methods. A retrospective review of an institutional registry was performed of primary TKAs performed between January 2005 and December 2016. Patient demographics including patient age, sex, height, weight, and body mass index (BMI) were obtained from registry and medical record review. In total, 8,100 primary TKAs were included. The mean age was 67.3 years (SD 9.5) with a mean BMI of 30.4 kg/m. 2. (SD 6.3). The TKAs were randomly split into a training cohort (n = 4,022) and a testing cohort (n = 4,078). A multivariate linear regression model was created on the training cohort and then applied to the testing cohort . A Bayesian model was created based on the frequencies of implant sizes in the training cohort. The model was then applied to the testing cohort to determine the accuracy of the model at 1%, 5%, and 10% tolerance of inaccuracy. Results. Height had a relatively strong correlation with implant size (femoral component anteroposterior (AP) Pearson correlation coefficient (ρ) = 0.73, p < 0.001; tibial component mediolateral (ML) ρ = 0.77, p < 0.001). Weight had a moderately strong correlation with implant size, (femoral component AP ρ = 0.46, p < 0.001; tibial ML ρ = 0.48, p < 0.001). There was a significant linear correlation with height, weight, and sex with implant size (femoral component R. 2. = 0.607, p < 0.001; tibial R. 2. = 0.695, p < 0.001). The Bayesian model showed high accuracy in predicting the range of required implant sizes (94.4% for the femur and 96.6% for the tibia) accepting a 5% risk of inaccuracy. Conclusion. Implant size was correlated with basic demographic variables including height, weight, and sex. The linear regression and Bayesian models accurately predicted required implant sizes across multiple manufacturers based on height, weight, and sex alone. These types of predictive models may help improve operating room and implant supply chain efficiency. Level of Evidence: Level IV. Cite this article: Bone Joint J 2020;102-B(6 Supple A):85–90

Aims. The primary aim of this study was to determine the surgical team’s learning curve for introducing robotic-arm assisted unicompartmental knee arthroplasty (UKA) into routine surgical practice. The secondary objective was to compare accuracy of implant positioning in conventional jig-based UKA versus robotic-arm assisted UKA. Patients and Methods. This prospective single-surgeon cohort study included 60 consecutive conventional jig-based UKAs compared with 60 consecutive robotic-arm assisted UKAs for medial compartment knee osteoarthritis. Patients undergoing conventional UKA and robotic-arm assisted UKA were well-matched for baseline characteristics including a mean age of 65.5 years (. sd. 6.8) vs 64.1 years (. sd. 8.7), (p = 0.31); a mean body mass index of 27.2 kg.m2 (. sd. 2.7) vs 28.1 kg.m2 (. sd. 4.5), (p = 0.25); and gender (27 males: 33 females vs 26 males: 34 females, p = 0.85). Surrogate measures of the learning curve were prospectively collected. These included operative times, the Spielberger State-Trait Anxiety Inventory (STAI) questionnaire to assess preoperative stress levels amongst the surgical team, accuracy of implant positioning, limb alignment, and postoperative complications. Results. Robotic-arm assisted UKA was associated with a learning curve of six cases for operating time (p < 0.001) and surgical team confidence levels (p < 0.001). Cumulative robotic experience did not affect accuracy of implant positioning (p = 0.52), posterior condylar offset ratio (p = 0.71), posterior tibial slope (p = 0.68), native joint line preservation (p = 0.55), and postoperative limb alignment (p = 0.65). Robotic-arm assisted UKA improved accuracy of femoral (p < 0.001) and tibial (p < 0.001) implant positioning with no additional risk of postoperative complications compared to conventional jig-based UKA. Conclusion. Robotic-arm assisted UKA was associated with a learning curve of six cases for operating time and surgical team confidence levels but no learning curve for accuracy of implant positioning. Cite this article: Bone Joint J 2018;100-B:1033–42

Aims. The objective of this study is to assess the use of ultrasound (US) as a radiation-free imaging modality to reconstruct 3D anatomy of the knee for use in preoperative templating in knee arthroplasty. Methods. Using an US system, which is fitted with an electromagnetic (EM) tracker that is integrated into the US probe, allows 3D tracking of the probe, femur, and tibia. The raw US radiofrequency (RF) signals are acquired and, using real-time signal processing, bone boundaries are extracted. Bone boundaries and the tracking information are fused in a 3D point cloud for the femur and tibia. Using a statistical shaping model, the patient-specific surface is reconstructed by optimizing bone geometry to match the point clouds. An accuracy analysis was conducted for 17 cadavers by comparing the 3D US models with those created using CT. US scans from 15 users were compared in order to examine the effect of operator variability on the output. Results. The results revealed that the US bone models were accurate compared with the CT models (root mean squared error (RM)S: femur, 1.07 mm (SD 0.15); tibia, 1.02 mm (SD 0.13). Additionally, femoral landmarking proved to be accurate (transepicondylar axis: 1.07° (SD 0.65°); posterior condylar axis: 0.73° (SD 0.41°); distal condylar axis: 0.96° (SD 0.89°); medial anteroposterior (AP): 1.22 mm (SD 0.69); lateral AP: 1.21 mm (SD 1.02)). Tibial landmarking errors were slightly higher (posterior slope axis: 1.92° (SD 1.31°); and tubercle axis: 1.91° (SD 1.24°)). For implant sizing, 90% of the femora and 60% of the tibiae were sized correctly, while the remainder were only one size different from the required implant size. No difference was observed between moderate and skilled users. Conclusion. The 3D US bone models were proven to be closely matched compared with CT and suitable for preoperative planning. The 3D US is radiation-free and offers numerous clinical opportunities for bone visualization rapidly during clinic visits, to enable preoperative planning with implant sizing. There is potential to extend its application to 3D dynamic ligament balancing, and intraoperative registration for use with robots and navigation systems. Cite this article: Bone Joint J 2021;103-B(6 Supple A):81–86

Aims. Ideal component sizing may be difficult to achieve in unicompartmental knee arthroplasty (UKA). Anatomical variants, incremental implant size, and a reduced surgical exposure may lead to over- or under-sizing of the components. The purpose of this study was to compare the accuracy of UKA sizing with robotic-assisted techniques versus a conventional surgical technique. Methods. Three groups of 93 medial UKAs were assessed. The first group was performed by a conventional technique, the second group with an image-free robotic-assisted system (Image-Free group), and the last group with an image-based robotic arm-assisted system, using a preoperative CT scan (Image-Based group). There were no demographic differences between groups. We compared six parameters on postoperative radiographs to assess UKA sizing. Incorrect sizing was defined by an over- or under-sizing greater than 3 mm. Results. There was a higher rate of tibial under-sizing posteriorly in the conventional group compared to robotic-assisted groups (47.3% (n = 44) in conventional group, 29% (n = 27) in Image-Free group, 6.5% (n = 6) in Image-Based group; p < 0.001), as well as a higher rate of femoral under-sizing posteriorly (30.1% (n = 28) in conventional group, 7.5% (n = 7) in Image-Free group, 12.9% (n = 12) in Image-Based group; p < 0.001). The posterior femoral offset was more often increased in the conventional group, especially in comparison to the Image-Based group (43% (n = 40) in conventional group, 30.1% (n = 28) in Image-Free group, 8.6% (n = 8) in Image-Based group; p < 0.001). There was no significant overhang of the femoral or tibial implant in any groups. Conclusion. Robotic-assisted surgical techniques for medial UKA decrease the risk of tibial and femoral under-sizing, particularly with an image-based system using a preoperative CT scan. Cite this article: Bone Joint J 2021;103-B(4):610–618

Aims. The COVID-19 pandemic led to a swift adoption of telehealth in orthopaedic surgery. This study aimed to analyze the satisfaction of patients and surgeons with the rapid expansion of telehealth at this time within the division of adult reconstructive surgery at a major urban academic tertiary hospital. Methods. A total of 334 patients underging arthroplasty of the hip or knee who completed a telemedicine visit between 30 March and 30 April 2020 were sent a 14-question survey, scored on a five-point Likert scale. Eight adult reconstructive surgeons who used telemedicine during this time were sent a separate 14-question survey at the end of the study period. Factors influencing patient satisfaction were determined using univariate and multivariate ordinal logistic regression modelling. Results. A total of 68 patients (20.4%) and 100% of the surgeons completed the surveys. Patients were “Satisfied” with their telemedicine visits (4.10/5.00 (SD 0.98)) and 19 (27.9%) would prefer telemedicine to in-person visits in the absence of COVID-19. Multivariate ordinal logistic regression modelling revealed that patients were more likely to be satisfied if their surgeon effectively responded to their questions or concerns (odds ratio (OR) 3.977; 95% confidence interval (CI) 1.260 to 13.190; p = 0.019) and if their visit had a high audiovisual quality (OR 2.46; 95% CI 1.052 to 6.219; p = 0.042). Surgeons were “Satisfied” with their telemedicine experience (3.63/5.00 (SD 0.92)) and were “Fairly Confident” (4.00/5.00 (SD 0.53)) in their diagnostic accuracy despite finding the physical examinations to be only “Slightly Effective” (1.88/5.00 (SD 0.99)). Most adult reconstructive surgeons, seven of eight (87.5%) would continue to use telemedicine in the future. Conclusion. Telemedicine emerged as a valuable tool during the COVID-19 pandemic. Patients undergoing arthroplasty and their surgeons were satisfied with telemedicine and see a role for its use after the pandemic. The audiovisual quality and the responsiveness of physicians to the concerns of patients determine their satisfaction. Future investigations should focus on improving the physical examination of patients through telemedicine and strategies for its widespread implementation. Cite this article: Bone Joint J 2021;103-B(6 Supple A):196–204

Aims. Unicompartmental knee arthroplasty (UKA) is a bone-preserving treatment option for osteoarthritis localized to a single compartment in the knee. The success of the procedure is sensitive to patient selection and alignment errors. Robotic arm-assisted UKA provides technological assistance to intraoperative bony resection accuracy, which is thought to improve ligament balancing. This paper presents the five-year outcomes of a comparison between manual and robotically assisted UKAs. Methods. The trial design was a prospective, randomized, parallel, single-centre study comparing surgical alignment in patients undergoing UKA for the treatment of medial compartment osteoarthritis (ISRCTN77119437). Participants underwent surgery using either robotic arm-assisted surgery or conventional manual instrumentation. The primary outcome measure (surgical accuracy) has previously been reported, and, along with secondary outcomes, were collected at one-, two-, and five-year timepoints. Analysis of five-year results and longitudinal analysis for all timepoints was performed to compare the two groups. Results. Overall, 104 (80%) patients of the original 130 who received surgery were available at five years (55 robotic, 49 manual). Both procedures reported successful results over all outcomes. At five years, there were no statistical differences between the groups in any of the patient reported or clinical outcomes. There was a lower reintervention rate in the robotic arm-assisted group with 0% requiring further surgery compared with six (9%) of the manual group requiring additional surgical intervention (p < 0.001). Conclusion. This study has shown excellent clinical outcomes in both groups with no statistical or clinical differences in the patient-reported outcome measures. The notable difference was the lower reintervention rate at five years for roboticarm-assisted UKA when compared with a manual approach. Cite this article: Bone Joint J 2021;103-B(6):1088–1095

Aims

Robotic arm-assisted surgery offers accurate and reproducible guidance in component positioning and assessment of soft-tissue tensioning during knee arthroplasty, but the feasibility and early outcomes when using this technology for revision surgery remain unknown. The objective of this study was to compare the outcomes of robotic arm-assisted revision of unicompartmental knee arthroplasty (UKA) to total knee arthroplasty (TKA) versus primary robotic arm-assisted TKA at short-term follow-up.

Aims

The surgical target for optimal implant positioning in robotic-assisted total knee arthroplasty remains the subject of ongoing discussion. One of the proposed targets is to recreate the knee’s functional behaviour as per its pre-diseased state. The aim of this study was to optimize implant positioning, starting from mechanical alignment (MA), toward restoring the pre-diseased status, including ligament strain and kinematic patterns, in a patient population.

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This study aims to determine the rate of and risk factors for total knee arthroplasty (TKA) after operative management of tibial plateau fractures (TPFs) in older adults.

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This study aims to determine difference in annual rate of early-onset (≤ 90 days) deep surgical site infection (SSI) following primary total knee arthroplasty (TKA) for osteoarthritis, and to identify risk factors that may be associated with infection.

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The aim of this study was to compare a bicruciate-retaining (BCR) total knee arthroplasty (TKA) with a posterior cruciate-retaining (CR) TKA design in terms of kinematics, measured using fluoroscopy and stability as micromotion using radiostereometric analysis (RSA).