Objective . Dunkin Hartley guinea pigs, a commonly used animal model of osteoarthritis, were used to determine if high frequency ultrasound can ensure intra-articular injections are accurately positioned in the knee joint. Methods. A high-resolution small animal ultrasound system with a 40 MHz transducer was used for image-guided injections. A total of 36 guinea pigs were anaesthetised with isoflurane and placed on a heated stage. Sterile needles were inserted directly into the knee joint medially, while the transducer was placed on the lateral surface, allowing the femur, tibia and fat pad to be visualised in the images. B-mode cine loops were acquired during 100 µl. We assessed our ability to visualise 1) important anatomical landmarks, 2) the needle and 3) anatomical changes due to the injection. . Results. From the ultrasound images, we were able to visualise clearly the movement of anatomical landmarks in 75% of the injections. The majority of these showed separation of the fat pad (67.1%), suggesting the injections were correctly delivered in the joint space. We also observed dorsal joint expansion (23%) and patellar tendon movement (10%) in a smaller subset of injections. Conclusion. The results demonstrate that this image-guided technique can be used to visualise the location of an intra-articular injection in the joints of guinea pigs. Future studies using an ultrasound-guided approach could help improve the injection accuracy in a variety of anatomical locations and animal models, in the hope of developing anti-arthritic therapies. Cite this article: Bone Joint Res 2015;4:1–5

Objectives

We performed in vitro validation of a non-invasive skin-mounted system that could allow quantification of anteroposterior (AP) laxity in the outpatient setting.

Aims. The diagnosis of periprosthetic joint infection (PJI) can be challenging as the symptoms are similar to other conditions, and the markers used for diagnosis have limited sensitivity and specificity. Recent research has suggested using blood cell ratios, such as platelet-to-volume ratio (PVR) and platelet-to-lymphocyte ratio (PLR), to improve diagnostic accuracy. The aim of the study was to further validate the effectiveness of PVR and PLR in diagnosing PJI. Methods. A retrospective review was conducted to assess the accuracy of different marker combinations for diagnosing chronic PJI. A total of 573 patients were included in the study, of which 124 knees and 122 hips had a diagnosis of chronic PJI. Complete blood count and synovial fluid analysis were collected. Recently published blood cell ratio cut-off points were applied to receiver operating characteristic curves for all markers and combinations. The area under the curve (AUC), sensitivity, specificity, and positive and negative predictive values were calculated. Results. The results of the analysis showed that the combination of ESR, CRP, synovial white blood cell count (Syn. WBC), and polymorphonuclear neutrophil percentage (PMN%) with PVR had the highest AUC of 0.99 for knees, with sensitivity of 97.73% and specificity of 100%. Similarly, for hips, this combination had an AUC of 0.98, sensitivity of 96.15%, and specificity of 100.00%. Conclusion. This study supports the use of PVR calculated from readily available complete blood counts, combined with established markers, to improve the accuracy in diagnosing chronic PJI in both total hip and knee arthroplasties. Cite this article: Bone Jt Open 2023;4(11):881–888

Aims. We aimed to assess the reliability and validity of OpenPose, a posture estimation algorithm, for measurement of knee range of motion after total knee arthroplasty (TKA), in comparison to radiography and goniometry. Methods. In this prospective observational study, we analyzed 35 primary TKAs (24 patients) for knee osteoarthritis. We measured the knee angles in flexion and extension using OpenPose, radiography, and goniometry. We assessed the test-retest reliability of each method using intraclass correlation coefficient (1,1). We evaluated the ability to estimate other measurement values from the OpenPose value using linear regression analysis. We used intraclass correlation coefficients (2,1) and Bland–Altman analyses to evaluate the agreement and error between radiography and the other measurements. Results. OpenPose had excellent test-retest reliability (intraclass correlation coefficient (1,1) = 1.000). The R. 2. of all regression models indicated large correlations (0.747 to 0.927). In the flexion position, the intraclass correlation coefficients (2,1) of OpenPose indicated excellent agreement (0.953) with radiography. In the extension position, the intraclass correlation coefficients (2,1) indicated good agreement of OpenPose and radiography (0.815) and moderate agreement of goniometry with radiography (0.593). OpenPose had no systematic error in the flexion position, and a 2.3° fixed error in the extension position, compared to radiography. Conclusion. OpenPose is a reliable and valid tool for measuring flexion and extension positions after TKA. It has better accuracy than goniometry, especially in the extension position. Accurate measurement values can be obtained with low error, high reproducibility, and no contact, independent of the examiner’s skills. Cite this article: Bone Joint Res 2023;12(5):313–320

Aims. To identify variables independently associated with same-day discharge (SDD) of patients following revision total knee arthroplasty (rTKA) and to develop machine learning algorithms to predict suitable candidates for outpatient rTKA. Methods. Data were obtained from the American College of Surgeons National Quality Improvement Programme (ACS-NSQIP) database from the years 2018 to 2020. Patients with elective, unilateral rTKA procedures and a total hospital length of stay between zero and four days were included. Demographic, preoperative, and intraoperative variables were analyzed. A multivariable logistic regression (MLR) model and various machine learning techniques were compared using area under the curve (AUC), calibration, and decision curve analysis. Important and significant variables were identified from the models. Results. Of the 5,600 patients included in this study, 342 (6.1%) underwent SDD. The random forest (RF) model performed the best overall, with an internally validated AUC of 0.810. The ten crucial factors favoring SDD in the RF model include operating time, anaesthesia type, age, BMI, American Society of Anesthesiologists grade, race, history of diabetes, rTKA type, sex, and smoking status. Eight of these variables were also found to be significant in the MLR model. Conclusion. The RF model displayed excellent accuracy and identified clinically important variables for determining candidates for SDD following rTKA. Machine learning techniques such as RF will allow clinicians to accurately risk-stratify their patients preoperatively, in order to optimize resources and improve patient outcomes. Cite this article: Bone Jt Open 2023;4(6):399–407

Aims. The aims of this study were: 1) to describe extended restricted kinematic alignment (E-rKA), a novel alignment strategy during robotic-assisted total knee arthroplasty (RA-TKA); 2) to compare residual medial compartment tightness following virtual surgical planning during RA-TKA using mechanical alignment (MA) and E-rKA, in the same set of osteoarthritic varus knees; 3) to assess the requirement of soft-tissue releases during RA-TKA using E-rKA; and 4) to compare the accuracy of surgical plan execution between knees managed with adjustments in component positioning alone, and those which require additional soft-tissue releases. Methods. Patients who underwent RA-TKA between January and December 2022 for primary varus osteoarthritis were included. Safe boundaries for E-rKA were defined. Residual medial compartment tightness was compared following virtual surgical planning using E-rKA and MA, in the same set of knees. Soft-tissue releases were documented. Errors in postoperative alignment in relation to planned alignment were compared between patients who did (group A) and did not (group B) require soft-tissue releases. Results. The use of E-rKA helped restore all knees within the predefined boundaries, with appropriate soft-tissue balancing. E-rKA compared with MA resulted in reduced residual medial tightness following surgical planning, in full extension (2.71 mm (SD 1.66) vs 5.16 mm (SD 3.10), respectively; p < 0.001), and 90° of flexion (2.52 mm (SD 1.63) vs 6.27 mm (SD 3.11), respectively; p < 0.001). Among the study population, 156 patients (78%) were managed with minor adjustments in component positioning alone, while 44 (22%) required additional soft-tissue releases. The mean errors in postoperative alignment were 0.53 mm and 0.26 mm among patients in group A and group B, respectively (p = 0.328). Conclusion. E-rKA is an effective and reproducible alignment strategy during RA-TKA, permitting a large proportion of patients to be managed without soft-tissue releases. The execution of minor alterations in component positioning within predefined multiplanar boundaries is a better starting point for gap management than soft-tissue releases. Cite this article: Bone Jt Open 2024;5(8):628–636

Aims. Accurate identification of the ankle joint centre is critical for estimating tibial coronal alignment in total knee arthroplasty (TKA). The purpose of the current study was to leverage artificial intelligence (AI) to determine the accuracy and effect of using different radiological anatomical landmarks to quantify mechanical alignment in relation to a traditionally defined radiological ankle centre. Methods. Patients with full-limb radiographs from the Osteoarthritis Initiative were included. A sub-cohort of 250 radiographs were annotated for landmarks relevant to knee alignment and used to train a deep learning (U-Net) workflow for angle calculation on the entire database. The radiological ankle centre was defined as the midpoint of the superior talus edge/tibial plafond. Knee alignment (hip-knee-ankle angle) was compared against 1) midpoint of the most prominent malleoli points, 2) midpoint of the soft-tissue overlying malleoli, and 3) midpoint of the soft-tissue sulcus above the malleoli. Results. A total of 932 bilateral full-limb radiographs (1,864 knees) were measured at a rate of 20.63 seconds/image. The knee alignment using the radiological ankle centre was accurate against ground truth radiologist measurements (inter-class correlation coefficient (ICC) = 0.99 (0.98 to 0.99)). Compared to the radiological ankle centre, the mean midpoint of the malleoli was 2.3 mm (SD 1.3) lateral and 5.2 mm (SD 2.4) distal, shifting alignment by 0.34. o. (SD 2.4. o. ) valgus, whereas the midpoint of the soft-tissue sulcus was 4.69 mm (SD 3.55) lateral and 32.4 mm (SD 12.4) proximal, shifting alignment by 0.65. o. (SD 0.55. o. ) valgus. On the intermalleolar line, measuring a point at 46% (SD 2%) of the intermalleolar width from the medial malleoli (2.38 mm medial adjustment from midpoint) resulted in knee alignment identical to using the radiological ankle centre. Conclusion. The current study leveraged AI to create a consistent and objective model that can estimate patient-specific adjustments necessary for optimal landmark usage in extramedullary and computer-guided navigation for tibial coronal alignment to match radiological planning. Cite this article: Bone Jt Open 2022;3(10):767–776

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

Aims. No predictive model has been published to forecast operating time for total knee arthroplasty (TKA). The aims of this study were to design and validate a predictive model to estimate operating time for robotic-assisted TKA based on demographic data, and evaluate the added predictive power of CT scan-based predictors and their impact on the accuracy of the predictive model. Methods. A retrospective study was conducted on 1,061 TKAs performed from January 2016 to December 2019 with an image-based robotic-assisted system. Demographic data included age, sex, height, and weight. The femoral and tibial mechanical axis and the osteophyte volume were calculated from CT scans. These inputs were used to develop a predictive model aimed to predict operating time based on demographic data only, and demographic and 3D patient anatomy data. Results. The key factors for predicting operating time were the surgeon and patient weight, followed by 12 anatomical parameters derived from CT scans. The predictive model based only on demographic data showed that 90% of predictions were within 15 minutes of actual operating time, with 73% within ten minutes. The predictive model including demographic data and CT scans showed that 94% of predictions were within 15 minutes of actual operating time and 88% within ten minutes. Conclusion. The primary factors for predicting robotic-assisted TKA operating time were surgeon, patient weight, and osteophyte volume. This study demonstrates that incorporating 3D patient-specific data can improve operating time predictions models, which may lead to improved operating room planning and efficiency. Cite this article: Bone Jt Open 2022;3(5):383–389

Aims. The purpose of this study was to compare the radiological outcomes of manual versus robotic-assisted medial unicompartmental knee arthroplasty (UKA). Methods. Postoperative radiological outcomes from 86 consecutive robotic-assisted UKAs (RAUKA group) from a single academic centre were retrospectively reviewed and compared to 253 manual UKAs (MUKA group) drawn from a prior study at our institution. Femoral coronal and sagittal angles (FCA, FSA), tibial coronal and sagittal angles (TCA, TSA), and implant overhang were radiologically measured to identify outliers. Results. When assessing the accuracy of RAUKAs, 91.6% of all alignment measurements and 99.2% of all overhang measurements were within the target range. All alignment and overhang targets were simultaneously met in 68.6% of RAUKAs. When comparing radiological outcomes between the RAUKA and MUKA groups, statistically significant differences were identified for combined outliers in FCA (2.3% vs 12.6%; p = 0.006), FSA (17.4% vs 50.2%; p < 0.001), TCA (5.8% vs 41.5%; p < 0.001), and TSA (8.1% vs 18.6%; p = 0.023), as well as anterior (0.0% vs 4.7%; p = 0.042), posterior (1.2% vs 13.4%; p = 0.001), and medial (1.2% vs 14.2%; p < 0.001) overhang outliers. Conclusion. Robotic system navigation decreases alignment and overhang outliers compared to manual UKA. Given the association between component placement errors and revision in UKA, this strong significant improvement in accuracy may improve implant survival. Level of Evidence: III. Cite this article: Bone Jt Open 2021;2-3:191–197

Aims. An algorithm to determine the constitutional alignment of the lower limb once arthritic deformity has occurred would be of value when undertaking kinematically aligned total knee arthroplasty (TKA). The purpose of this study was to determine if the arithmetic hip-knee-ankle angle (aHKA) algorithm could estimate the constitutional alignment of the lower limb following development of significant arthritis. Methods. A matched-pairs radiological study was undertaken comparing the aHKA of an osteoarthritic knee (aHKA-OA) with the mechanical HKA of the contralateral normal knee (mHKA-N). Patients with Grade 3 or 4 Kellgren-Lawrence tibiofemoral osteoarthritis in an arthritic knee undergoing TKA and Grade 0 or 1 osteoarthritis in the contralateral normal knee were included. The aHKA algorithm subtracts the lateral distal femoral angle (LDFA) from the medial proximal tibial angle (MPTA) measured on standing long leg radiographs. The primary outcome was the mean of the paired differences in the aHKA-OA and mHKA-N. Secondary outcomes included comparison of sex-based differences and capacity of the aHKA to determine the constitutional alignment based on degree of deformity. Results. A total of 51 radiographs met the inclusion criteria. There was no significant difference between aHKA-OA and mHKA-N, with a mean angular difference of −0.4° (95% SE −0.8° to 0.1°; p = 0.16). There was no significant sex-based difference when comparing aHKA-OA and mHKA-N (mean difference 0.8°; p = 0.11). Knees with deformities of more than 8° had a greater mean difference between aHKA-OA and mHKA-N (1.3°) than those with lesser deformities (-0.1°; p = 0.009). Conclusion. This study supports the arithmetic HKA algorithm for prediction of the constitutional alignment once arthritis has developed. The algorithm has similar accuracy between sexes and greater accuracy with lesser degrees of deformity. Cite this article: Bone Joint Open 2020;1-7:339–345

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. 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. The mobile bearing Oxford unicompartmental knee arthroplasty (OUKA) is recommended to be performed with the leg in the hanging leg (HL) position, and the thigh placed in a stirrup. This comparative cadaveric study assesses implant positioning and intraoperative kinematics of OUKA implanted either in the HL position or in the supine leg (SL) position. Methods. A total of 16 fresh-frozen knees in eight human cadavers, without macroscopic anatomical defects, were selected. The knees from each cadaver were randomized to have the OUKA implanted in the HL or SL position. Results. Tibial base plate rotation was significantly more variable in the SL group with 75% of tibiae mal-rotated. Multivariate analysis of navigation data found no difference based on all kinematic parameters across the range of motion (ROM). However, area under the curve analysis showed that knees placed in the HL position had much smaller differences between the pre- and post-surgery conditions for kinematics mean values across the entire ROM. Conclusion. The sagittal tibia cut, not dependent on standard instrumentation, determines the tibial component rotation. The HL position improves accuracy of this step compared to the SL position, probably due to better visuospatial orientation of the hip and knee to the surgeon. The HL position is better for replicating native kinematics of the knee as shown by the area under the curve analysis. In the supine knee position, care must be taken during the sagittal tibia cut, while checking flexion balance and when sizing the tibial component

Aims

Distal femoral resection in conventional total knee arthroplasty (TKA) utilizes an intramedullary guide to determine coronal alignment, commonly planned for 5° of valgus. However, a standard 5° resection angle may contribute to malalignment in patients with variability in the femoral anatomical and mechanical axis angle. The purpose of the study was to leverage deep learning (DL) to measure the femoral mechanical-anatomical axis angle (FMAA) in a heterogeneous cohort.

Aims

Unicompartmental knee arthroplasty (UKA) is the preferred treatment for anterior medial knee osteoarthritis (OA) owing to the rapid postoperative recovery. However, the risk factors for UKA failure remain controversial.

Aims

This study aimed to explore the biological and clinical importance of dysregulated key genes in osteoarthritis (OA) patients at the cartilage level to find potential biomarkers and targets for diagnosing and treating OA.

Aims

This Delphi study assessed the challenges of diagnosing soft-tissue knee injuries (STKIs) in acute settings among orthopaedic healthcare stakeholders.

Objectives. An important measure for the diagnosis and monitoring of knee osteoarthritis is the minimum joint space width (mJSW). This requires accurate alignment of the x-ray beam with the tibial plateau, which may not be accomplished in practice. We investigate the feasibility of a new mJSW measurement method from stereo radiographs using 3D statistical shape models (SSM) and evaluate its sensitivity to changes in the mJSW and its robustness to variations in patient positioning and bone geometry. Materials and Methods. A validation study was performed using five cadaver specimens. The actual mJSW was varied and images were acquired with variation in the cadaver positioning. For comparison purposes, the mJSW was also assessed from plain radiographs. To study the influence of SSM model accuracy, the 3D mJSW measurement was repeated with models from the actual bones, obtained from CT scans. Results. The SSM-based measurement method was more robust (consistent output for a wide range of input data/consistent output under varying measurement circumstances) than the conventional 2D method, showing that the 3D reconstruction indeed reduces the influence of patient positioning. However, the SSM-based method showed comparable sensitivity to changes in the mJSW with respect to the conventional method. The CT-based measurement was more accurate than the SSM-based measurement (smallest detectable differences 0.55 mm versus 0. 82 mm, respectively). Conclusion. The proposed measurement method is not a substitute for the conventional 2D measurement due to limitations in the SSM model accuracy. However, further improvement of the model accuracy and optimisation technique can be obtained. Combined with the promising options for applications using quantitative information on bone morphology, SSM based 3D reconstructions of natural knees are attractive for further development. Cite this article: E. A. van IJsseldijk, E. R. Valstar, B. C. Stoel, R. G. H. H. Nelissen, N. Baka, R. van’t Klooster, B. L. Kaptein. Three dimensional measurement of minimum joint space width in the knee from stereo radiographs using statistical shape models. Bone Joint Res 2016;320–327. DOI: 10.1302/2046-3758.58.2000626

Aims

Patient dissatisfaction following primary total knee arthroplasty (TKA) with manual jig-based instruments has been reported to be as high as 30%. Robotic-assisted total knee arthroplasty (RA-TKA) has been increasingly used in an effort to improve patient outcomes, however there is a paucity of literature examining patient satisfaction after RA-TKA. This study aims to identify the incidence of patients who were not satisfied following RA-TKA and to determine factors associated with higher levels of dissatisfaction.