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. The aims of this study were to assess quality of life after hip fractures, to characterize respondents to patient-reported outcome measures (PROMs), and to describe the recovery trajectory of hip fracture patients. Methods. Data on 35,206 hip fractures (2014 to 2018; 67.2% female) in the Norwegian Hip Fracture Register were linked to data from the Norwegian Patient Registry and Statistics Norway. PROMs data were collected using the EuroQol five-dimension three-level questionnaire (EQ-5D-3L) scoring instrument and living patients were invited to respond at four, 12, and 36 months post fracture. Multiple imputation procedures were performed as a model to substitute missing PROM data. Differences in response rates between categories of covariates were analyzed using chi-squared test statistics. The association between patient and socioeconomic characteristics and the reported EQ-5D-3L scores was analyzed using linear regression. Results. The median age was 83 years (interquartile range 76 to 90), and 3,561 (10%) lived in a healthcare facility. Observed mean pre-fracture EQ-5D-3L index score was 0.81 (95% confidence interval 0.803 to 0.810), which decreased to 0.66 at four months, to 0.70 at 12 months, and to 0.73 at 36 months. In the imputed datasets, the reduction from pre-fracture was similar (0.15 points) but an improvement up to 36 months was modest (0.01 to 0.03 points). Patients with higher age, male sex, severe comorbidity, cognitive impairment, lower income, lower education, and those in residential care facilities had a lower proportion of respondents, and systematically reported a lower health-related quality of life (HRQoL). The response pattern of patients influenced scores significantly, and the highest scores are found in patients reporting scores at all observation times. Conclusion. Hip fracture leads to a persistent reduction in measured HRQoL, up to 36 months. The patients’ health and socioeconomic status were associated with the proportion of patients returning PROM data for analysis, and affected the results reported. Observed EQ-5D-3L scores are affected by attrition and selection bias mechanisms and motivate the use of statistical modelling for adjustment. Cite this article: Bone Joint J 2024;106-B(4):394–400

Aims

Physician burnout and its consequences have been recognized as increasingly prevalent and important issues for both organizations and individuals involved in healthcare delivery. The purpose of this study was to describe and compare the patterns of self-reported wellness in orthopaedic surgeons and trainees from multiple nations with varying health systems.

Introduction. The increased prevalence of osteoporosis in the patient population undergoing reverse shoulder arthroplasty (RSA) results in significantly increased complication rates. Mainly demographic and clinical predictors are currently taken into the preoperative assessment for risk stratification without quantification of preoperative computed tomography (CT) data (e.g. bone density). It was hypothesized that preoperative CT bone density measures would provide objective quantification with subsequent classification of the patients’ humeral bone quality. Methods. Thirteen bone density parameters from 345 preoperative CT scans of a clinical RSA cohort represented the data set in this study. The data set was divided into testing (30%) and training data (70%), latter included an 8-fold cross validation. Variable selection was performed by choosing the variables with the highest descriptive value for each correlation clustered variables. Machine learning models were used to improve the clustering (Hierarchical Ward) and classification (Support Vector Machine (SVM)) of bone densities at risk for complications and were compared to a conventional statistical model (Logistic Regression (LR)). Results. Clustering partitioned this cohort (training data set) into a high bone density subgroup consisting of 96 patients and a low bone density subgroup consisting of 146 patients. The optimal number of clusters (n = 2) was determined based on optimization metrics. Discrimination of the cross validated classification model showed comparable performance for the training (accuracy=91.2%; AUC=0.967) and testing data (accuracy=90.5 %; AUC=0.958) while outperforming the conventional statistical model (Logistic Regression (LR)). Local interpretable model-agnostic explanations (LIME) were created for each patient to explain how the predicted output was achieved. Conclusion. The trained and tested model provides preoperative information for surgeons treating patients with potentially poor bone quality. The use of machine learning and patient-specific calibration showed that multiple 3D bone density scores improved accuracy for objective preoperative bone quality assessment

Introduction and Objective. Forced external rotation is hypothesized as the key mechanism of syndesmotic ankle injuries. This complex trauma pattern ruptures the syndesmotic ligaments and induces a three-dimensional deviation from the normal distal tibiofibular joint configuration. However, current diagnostic imaging modalities are impeded by a two-dimensional assessment, without taking into account ligamentous stabilizers. Therefore, our aim is two-fold: (1) to construct an articulated statistical shape model of the normal ankle with inclusion of ligamentous morphometry and (2) to apply this model in the assessment of a clinical cohort of patients with syndesmotic ankle injuries. Materials and Methods. Three-dimensional models of the distal tibiofibular joint were analyzed in asymptomatic controls (N= 76; Mean age 63 +/− 19 years), patients with syndesmotic ankle injury (N = 13; Mean age 35 +/− 15 years), and their healthy contralateral equivalent (N = 13). Subsequently, the statistical shape model was generated after aligning all ankles based on the distal tibia. The position of the syndesmotic ligaments was predicted based on previously validated iterative shortest path calculation methodology. Evaluation of the model was described by means of accuracy, compactness and generalization. Canonical Correlation Analysis was performed to assess the influence of syndesmotic lesions on the distal tibiofibular joint congruency. Results. Our presented model contained an accuracy of 0.23 +/− 0.028 mm. Mean prediction accuracy of ligament insertions was 0.53 +/− 12 mm. A statistically significant difference in anterior syndesmotic distance was found between ankles with syndesmotic lesions and healthy controls (95% CI [0.32, 3.29], p = 0.017). There was a significant correlation between presence of syndesmotic injury and the morphological distal tibiofibular configuration (r = 0.873, p <0,001). Conclusions. In this study, we constructed a bony and ligamentous statistical model representing the distal tibiofibular joint Furthermore, the presented model was able to detect an elongation injury of the anterior inferior tibiofibular ligament after traumatic syndesmotic lesions in a clinical patient cohort

Intra-articular cartilage pressure distribution in the knee joint is critical in the understanding of osteoarthritis. Combining personalized statistical modeling of the morphological characteristics with discrete element modeling enables patient-specific predictions of the pressure on the tibial plateau. However, modeling of the meniscus during gait is complicated by the dynamic nature of the structure. Nevertheless, the position of the meniscus has a substantial impact on intra-articular stress distribution. Therefore, the focus of this presentation will be on how modeling of meniscal movement during knee flexion improves insight in general meniscal kinematics for the use in tibiofemoral stress distribution calculations

During shoulder arthroplasty the native functionality of the diseased shoulder joint is restored, this functionality is strongly dependent upon the native anatomy of the pre-diseased shoulder joint. Therefore, surgeons often use the healthy contralateral scapula to plan the surgery, however in bilateral diseases such as osteoarthritis this is not always feasible. Virtual reconstructions are then used to reconstruct the pre-diseased anatomy and plan surgery or subject-specific implants. In this project, we develop and validate a statistical shape modeling method to reconstruct the pre-diseased anatomy of eroded scapulae with the aim to investigate the existence of predisposing anatomy for certain shoulder conditions. The training dataset for the statistical shape model consisted of 110 CT images from patients without observable scapulae pathologies as judged by an experienced shoulder surgeon. 3D scapulae models were constructed from the segmented images. An open-source non-rigid B-spline-based registration algorithm was used to obtain point-to-point correspondences between the models. The statistical shape model was then constructed from the dataset using principle component analysis. The cross-validation was performed similarly to the procedure described by Plessers et al. Virtual defects were created on each of the training set models, which closely resemble the morphology of glenoid defects according to the Wallace classification method. The statistical shape model was reconstructed using the leave-one-out method, so the corresponding training set model is no longer incorporated in the shape model. Scapula reconstruction was performed using a Monte Carlo Markov chain algorithm, random walk proposals included both shape and pose parameters, the closest fitting proposal was selected for the virtual reconstruction. Automatic 3D measurements were performed on both the training and reconstructed 3D models, including glenoid version, critical shoulder angle, glenoid offset and glenoid center position. The root-mean-square error between the measurements of the training data and reconstructed models was calculated for the different severities of glenoid defects. For the least severe defect, the mean error on the inclination, version and critical shoulder angle (°) was 2.22 (± 1.60 SD), 2.59 (± 1.86 SD) and 1.92 (± 1.44 SD) respectively. The reconstructed models predicted the native glenoid offset and centre position (mm) an accuracy of 0.87 (± 0.96 SD) and 0.88 (± 0.57 SD) respectively. The overall reconstruction error was 0.71 mm for the reconstructed part. For larger defects each error measurement increased significantly. A virtual reconstruction methodology was developed which can predict glenoid parameters with high accuracy. This tool will be used in the planning of shoulder surgeries and investigation of predisposing scapular morphologies

Purpose. To achieve 3D kinematic analysis of total knee arthroplasty (TKA), 2D/3D registration techniques, which use X-ray fluoroscopic images and computer aided design model of the knee implants, have been applied to clinical cases. However, most conventional methods have needed time-consuming and labor-intensive manual operations in some process. In particular, for the 3D pose estimation of tibial component model from X-ray images, these manual operations were carefully performed because the pose estimation of symmetrical tibial component get severe local minima rather than that of unsymmetrical femoral component. In this study, therefore, we propose an automated 3D kinematic estimation method of tibial component based on statistical motion model, which is created from previous analyzed 3D kinematic data of TKA. Methods. The used 2D/3D registration technique is based on a robust feature-based (contour-based) algorithm. In our proposed method, a statistical motion model which represents average and variability of joint motion is incorporated into the robust feature-based algorithm, particularly for the pose estimation of tibial component. The statistical motion model is created from previous a lot of analyzed 3D kinematic data of TKA. In this study, a statistical motion model for relative knee motion of the tibial component with respect to the femoral component was created and utilized. Fig. 1 shows each relative knee motion model for six degree of freedom (three translations and three rotations parameter). Thus, after the pose estimation of the femoral component model, 3D pose of the tibial component model is determined by maximum a posteriori (MAP) estimation using the new cost function introduced the statistical motion model. Experimental results. To validate the feasibility and effectiveness of 3D pose estimation for the tibial component using the proposed method, experiments using X-ray fluoroscopic images of 20 TKA patients under the squatting knee motion were performed. For the creation of correct pose (reference data) and the statistical motion model, we used the 3D pose data which were got by carefully applying previous method to the contour images which spurious edges and noises were removed manually. In order to ensure the validity for the statistical motion model of the proposed method, leave-one-out cross validation method was applied. In the 3D pose estimation of tibial component model, for the only first frame, initial guess pose of the model was manually given. For all images except for the first frame, the 3D pose of the model was automatically estimated without manual initial guess pose of the model. To assess the automation performance, the automation rate was calculated, and the rate was defined as the X-ray frame number of satisfying clinical required accuracy (error within 1mm, 1 degree) relative to all X-ray frame number. As results of the experiments, 3D pose of the tibial component model for all X-ray images except for the first frame was full-automatically stably-estimated, and the automation rate was 80.1 %. Conclusions. The proposed method by MAP estimation introduced the statistical motion model was successfully performed, and did not need labor-intensive manual operations for 3D pose estimation of tibial component. For any figures or tables, please contact authors directly (see Info & Metrics tab above).

Slip and fall injuries represent a significant burden to the Canadian general public and healthcare system; the annual financial cost of these accidents in Canada is estimated to be $2 billion (2014). Interestingly, slip and fall accidents are not evenly distributed across the provinces, with the rate of hospitalization due to falls in Alberta being nearly three times greater than the rate in Ontario. Our research aim was to create the Alberta Slip and Fall Index (ASFI) – a simple scale like the UV or Air Quality index – that could be used to warn the general public about the presence of slippery conditions. The ASFI could be paired with interventions proven to prevent outdoor slips and falls, like promoting the use of ice cleats. Eleven years (January 2008 - December 2018) of emergency room presentations to the four adult hospitals in Calgary, Alberta were filtered based on the ICD-10 diagnostic code W00 (slip and fall due to ice and snow). Multivariable dispersion-corrected Poisson regression models were used to analyze the weather conditions and time of year most predictive of slip and fall injuries. A slip and fall risk calculator (the ASFI) was designed using output from statistical modelling. To validate the ASFI we compared model predicted slip and fall risk to real presentations using retrospective weather and patient data. The final dataset included 14,977 slip and fall incidents. The three months with the most emergency room presentations were January(n = 3591), February(n = 2997), and March(n = 2954); each of these predicted increased slip and fall accidents(p < 0 .001). Same day ice was significantly associated with more slip and fall accidents, as was the presence of ice one, two, and three days prior(p < 0 .001). Snow one day prior was mildly protective against slip and fall accidents, but this effect was not significant(p = 0.861). Snow, ice, and time of year variables can be input into the ASFI calculator, which computes the likelihood of slip and fall accidents on a 0-40 point scale, with 40 indicating maximum fall risk. Upon validation of the ASFI, we generally found days with the highest raw frequency of slip and fall accidents had higher ASFI scores. Although the ASFI can theoretically result in a score of 40, when we entered realistic weather conditions it was impossible to create a score higher than 20. The ASFI represents a tool that can be used to prevent slip and fall accidents due to icy and snowy conditions. As demonstrated by our inability to maximize the risk score when using realistic weather conditions, the ASFI is imperfect. Despite its shortcomings, the ASFI is a preliminary step towards effectively disseminating information about the weather conditions likely to lead to falls. Ideally, a refined ASFI will help people better understand when to use protective equipment and take extra precaution outdoors. If implementing the ASFI led to even a 1% decrease in injuries caused by falls, the annual Canadian healthcare savings would be roughly $2 million

Introduction. From 2004 to 2015, elective lumbar fusions increased by 62% in the US. The largest increases were for among age 65 or older (139% in volume) and scoliosis (187%) [1]. Age is a well known factor of osteoporosis. The load-sharing may exceed the pedicular screws constructs in aging spine and lead to non-union and re-do. Surgical options may increase the screw purchase (e.g.: augmentation, extensions) at supplementary risks. Pedicular screw are known to cause vascular, nerve root or cord injuries. Facing these pitfalls, the surgeon's experience and rule of thumbs are the most deciding factors for the surgical planning. The aim of this study is to assess the accuracy of a patient specific tool, designed to plan a safe pedicular trajectory and to provide an intraoperative screw pullout strength estimate. Materials and Methods. Clinical QCT were taken for nine cadaveric spines (82 y. [61; 87], 6 females, 3 males). The experimental maximum axial pullout resistance (FMax) of twenty-seven pedicular screws inserted (nine T12, nine L4 and nine L5) was obtained as described in a previous study [2]. A custom 3D-WYSIWYG software simulated a medio-lateral surgical insertion technique in the QCTs coordinates reference, respecting the cortical walls. Repeatable density, morphometric and hardware parameters were recorded for each vertebrae. A statistical model was built to match predictive and experimental data. Preliminary results. Experimental FMax(N) were [104;953] (359 ±223). A further displacement of 1,81mm ±0,35 halved the experimental FMax. Predictive FMax(N) were [142;862] (359 ±220). A high positive correlation between experimental and predictive FMax was revealed (Pearson, ρ = 0.93, R2 = 0.87, p < .001, figure 1). Absolute differences ranged between 3N and 177N. Discussion. A high screw purchase in primary fixation is paramount to achieve spine surgical procedures (e.g.: kyphosis, scoliosis) and postoperative stability for vertebrae fusion. High losses of screw purchase by bone plastic deformation, begin with tiny pullouts. Theses unwanted intraoperative millimetric over-displacements are hard to avoid when monitoring at the same time tens of screws surrounded by bleedings. This advocates for including predictive FMax for each implantable pedicular screw in the surgical planning decision making process to prevent failures and assess risks. For the first time, this study presents an experimentally validated statistical model for FMax prediction with a safe trajectory definition tool, including patients’ vertebrae and hardware properties and referring to the patient's clinical 3D quantitative imagery. The model was able to differentiate between bone quality and vertebrae variations. More extensive model validation is currently ongoing to interface with robotics & navigation systems and to produce meshes for 3D printing of sterilizable insertion guides

Aims. To investigate the effect of polyethylene manufacturing characteristics and irradiation dose on the survival of cemented and reverse hybrid total hip arthroplasties (THAs). Methods. In this registry study, data from the National Joint Registry of England, Wales, Northern Ireland and the Isle of Man (NJR) were linked with manufacturing data supplied by manufacturers. The primary endpoint was revision of any component. Cox proportional hazard regression was a primary analytic approach adjusting for competing risk of death, patient characteristics, head composition, and stem fixation. Results. A total of 290,770 primary THAs were successfully linked with manufacturing characteristics. Overall 4,708 revisions were analyzed, 1,260 of which were due to aseptic loosening. Total radiation dose was identified as a risk factor and included in the Cox model. For statistical modelling of aseptic loosening, THAs were grouped into three categories: G1 (no radiation); G2 ( > 0 to < 5 Mrad); and G3 ( ≥ 5 Mrad). G1 had the worst survivorship. The Cox regression hazard ratio for revision due to aseptic loosening for G2 was 0.7 (95% confidence interval (CI) 0.58 to 0.83), and for G3 0.4 (95% CI 0.30 to 0.53). Male sex and uncemented stem fixation were associated with higher risk of revision and ceramic heads with lower risk. Conclusion. Polyethylene irradiation was associated with reduced risk of revision for aseptic loosening. Radiation doses of ≥ 5 Mrad were associated with a further reduction in risk. Cite this article: Bone Joint Res 2020;9(9):563–571

Bone shape estimation from partial observations, such as fluoroscopy or ultrasound, has been subject of significant interest over the past decade and can be regarded as the driving force behind several advances in statistical modelling of shape. While statistical models were initially used mostly as regularisers constraining shape matching algorithms, they are now increasingly employed due to their predictive ability, when only limited observations are available. With the current efforts toward minimal invasiveness, radiation exposure reduction, and optimization of the cost-effectiveness of procedures, two major challenges emerge on the field of statistical modelling. The first one is to develop methods that enable the use of as much information as possible that can be relevant for a specific shape prediction task, within the aforementioned limits. The second challenge concerns the accuracy of the resulting predictions, which needs to be quantified in order to evaluate the associated risks, and to optimise the data acquisition procedures. In terms of shape prediction, most studies so far have concentrated on individualizing statistical atlases based on imaging data. However, relevant information about skeletal morphology can also be obtained from simple anthropometric and morphometric measurements such as gender, age, body-mass index, and bone specific measurements. We develop a multivariate regression framework that enables to take into account such combinations of predictors simultaneously with sparse observations of the bone surface for improved prediction of the complete bone shape. In particular, we describe in a quantitative and localised fashion the individual contributions but also the complementarities of the heterogeneous sources of information with respect to bone morphology assessment. To do so, we compare the prediction errors obtained with different combinations of predictors, relying on cross-validation experiments. In addition to providing valuable and complementary predictive information, non-imaging measurements can be exploited to automatically initialise surface registration algorithms which increase their robustness for the determination of patient specific morphologies. A statistical model, by essence, is a mathematical model resulting from a learning phase using a set of training data. Statistical model based prediction is affected by three main sources of errors. The pre-processing of the training data, in particular the establishment of anatomical correspondences between the different samples, and the limited number of training elements constitute a first source of uncertainties. Second, the predictors can be affected by measurement noise, which will then propagate through the prediction process. Finally, and this is particularly important in the context of sparse observation data, the limited correlations between the predictors and the shape to predict imply theoretical limits for the achievable accuracy of such approaches. We have developed a framework enabling to account for these various sources of uncertainty, and propagating them through the prediction pipeline to generate confidence regions around the predicted shape. It relies extensively on cross-validation experiments in order to quantify the limitations of the statistical model with respect to the representation of new shapes (generalization ability) and to their prediction from partial data. Furthermore, we demonstrate the reliability of the obtained regions, following the procedure initially proposed in. We evaluate our approaches on a database of 140 femur bones, age range: 23–83, mean 62.57, stdv 15; 46% males and 54% females, with known age, height and weight. Morphometric measurements such as bone length, inter-condyle distance or anteversion angle are considered, either as predictors, together with sparse point clouds around the femoral head and greater trochanter, or as a pose-independent quality-of-fit metric. Cross-validation experiments indicate that a higher accuracy can be reached when complementing surface-based predictors with relevant anthropometric and morphometric information, and that reliable confidence regions can be estimated

Purpose: The purpose of this work was to study the reliability and the precision of a lumber vertebra reconstruction method using images obtained from a 3D statistical model and two calibrated radiograms. The technique is designed for surgical approach to the lumbar spine and implantation of osteosynthesis material using enhanced-reality technology. Material and methods: A lumbar vertebra was reconstructed on several specimens using images issuing from a 3D statistical model and two calibrated radiograms. The images obtained from the model of this lumbar vertebra to be reconstructed constituted the preoperative images. Intra-operative images corresponded to two calibrated radiograms acquired with a fluoroscope using advanced technology (silicium receptor). The model was equipped with reflecting patches which can be detected in space using a 3D optical system. Correspondence between the 3D statistical model and the two calibrated radiograms was achieved with appropriate software. Navigation views were displayed on the screen to guide surgical tools at the vertebral level. Pedicular screws were implanted into several anatomic specimens to evaluate the reliability and precision of the system. The exact position of the implanted screws was established with computed tomography. Results: This system demonstrated its reliability and precision for the reconstruction of a lumbar vertebra from a 3D statistical model and two calibrated radiograms. All the implanted screws were perfectly positioned in the pedicles. Precision was to the order of 1 mm. Discussion: This method is a passive system not requiring intraoperative intervention. Reconstruction of a lumbar vertebra from a preoperative 3D statistical model and two intra-operative calibrated radiograms avoids the need to identify anatomic landmarks and/or surface points on the vertebra to be reconstructed. The level of precision is very similar to that obtained with CT-based systems. Preoperative CT is not needed for navigation. Conclusion: With this system, new generation fluoroscopic equipment should appear in the operating room, allowing acquisition of successive calibrated images. The digital data could then be matched with statistical anatomic data, avoiding the need for preoperative imaging (CT or MRI). Progressive introduction of intra-operative ultrasound to replace the calibrated radiograms should open a new approach for percutaneous surgery of the lumbar spine

Aims. The objective of this study was to compare simulated range of motion (ROM) for reverse total shoulder arthroplasty (rTSA) with and without adjustment for scapulothoracic orientation in a global reference system. We hypothesized that values for simulated ROM in preoperative planning software with and without adjustment for scapulothoracic orientation would be significantly different. Methods. A statistical shape model of the entire humerus and scapula was fitted into ten shoulder CT scans randomly selected from 162 patients who underwent rTSA. Six shoulder surgeons independently planned a rTSA in each model using prototype development software with the ability to adjust for scapulothoracic orientation, the starting position of the humerus, as well as kinematic planes in a global reference system simulating previously described posture types A, B, and C. ROM with and without posture adjustment was calculated and compared in all movement planes. Results. All movement planes showed significant differences when comparing protocols with and without adjustment for posture. The largest mean difference was seen in external rotation, being 62° (SD 16°) without adjustment compared to 25° (SD 9°) with posture adjustment (p < 0.001), with the highest mean difference being 49° (SD 15°) in type C. Mean extension was 57° (SD 18°) without adjustment versus 24° (SD 11°) with adjustment (p < 0.001) and the highest mean difference of 47° (SD 18°) in type C. Mean abducted internal rotation was 69° (SD 11°) without adjustment versus 31° (SD 6°) with posture adjustment (p < 0.001), showing the highest mean difference of 51° (SD 11°) in type C. Conclusion. The present study demonstrates that accounting for scapulothoracic orientation has a significant impact on simulated ROM for rTSA in all motion planes, specifically rendering vastly lower values for external rotation, extension, and high internal rotation. The substantial differences observed in this study warrant a critical re-evaluation of all previously published studies that examined component choice and placement for optimized ROM in rTSA using conventional preoperative planning software. Cite this article: Bone Joint J 2024;106-B(11):1284–1292

Aims. To assess the variation in pre-fracture quality of life (QoL) within the UK hip fracture population, and quantify the nature and strength of associations between QoL and other routinely collected patient characteristics and treatment choices. Methods. The World Hip Trauma Evaluation (WHiTE) study, an observational cohort study of UK hip fracture patients, collects a range of routine data and a health-related QoL score (EuroQol five-dimension questionnaire (EQ-5D)). Pre-fracture QoL data are summarized and statistical models fitted to understand associations between QoL, patient characteristics, fracture types, and operations. Results. Fitting a multiple linear regression model indicated that 36.5% of the variance in pre-fracture EQ-5D scores was explained by routinely collected patient characteristics: sex (0.14%), age (0.17%), American Society of Anesthesiologists (ASA) score (0.73%), Abbreviated Mental Test Score (AMTS; 1.3%), pre-fracture mobility (11.2%), and EQ-5D respondent (participant, relative, or carer; 23.0%). There was considerable variation in pre-fracture EQ-5D scores between operations within fracture types. Participants with trochanteric fractures reported statistically significant but not clinically relevant lower pre-fracture QoL than those with intracapsular fractures. Participants with intracapsular fractures treated with internal fixation or total hip arthroplasty (THA) reported better QoL than those treated with hemiarthroplasty with the overall fittest group receiving THA. Conclusion. Pre-fracture QoL varies considerably between hip fracture patients; it is generally higher in younger than older patients, patients with better mobility, and those patients who live more independently. Pre-fracture QoL is significantly associated with a range of patient characteristics (e.g. age, mobility, residency). These data explain ~35% of the variation in QoL. Cite this article: Bone Joint Res 2020;9(8):468–476

Introduction. Kienböck's disease is generally defined as the collapse of the lunate bone, and this may lead to early wrist osteoarthritis. Replacing the collapsed lunate with an implant has regained renewed interest with the advancing technology of additive manufacturing, enabling the design of patient-specific implants. The aims of this project are (1) to determine how accurate it is to use the contralateral lunate shape as a template for patient-specific lunate implants, and (2) to study the effects of shape variations wrist kinematics using 4D-computed tomography (CT) scanning. Methods. A 3D statistical shape model (SSM) of the lunate was built based on bilateral CT scans of 54 individuals. Using SMM, shape variations of the lunate were identified and the intra- and inter-subject shape variations were compared by performing an intraclass correlation analysis. A radiolucent motor-controlled wrist-holder was designed to guide flexion/extension and radial/ulnar deviation of ex vivo wrist specimens under 4D-CT scanning. In this pilot, three shape mode variations were tested per specimen in two specimens were. After post-processing each CT, the scapholunate angle (SLA) and capitolunate angle (CLA) were measured. Results. The shape of the lunate was not symmetrical, defined as exceeding the intra-subject variation in five different shape modes. The FE tests show a generalized increase in scapholunate and capitolunate angle when using lunate implants, and comparing variation of shape modes showed that shape mode 3 has a significant effect on the measured angles (p<0.05). Discussion. The design of patient-specific lunate implants may prove to be challenging using a ‘mirror’-design as it will lead to a degree of shape asymmetry. The pilot study, to determine the effects of those shape variations on wrist kinematics suggest that the degree of shape variation observed indeed may alter the wrist kinematics, although this needs to be further investigated in study using more specimens

Tibial pilon fractures are typically the result of high-energy axial loads, with complex intra- articular fractures that are often difficult to reconstruct anatomically. Only nine simultaneous pilon and talus fractures have been published previously, but we hypothesised the chondral surface of the dome is affected more frequently. Data was acquired prospectively from 154 acute distal tibial pilon fractures (AO/OTA 43B/C) in adults. Radiographs, photographs, and intra-operative drawings of each case were utilised to document the presence of any macroscopic injuries of the talus. Detailed 1x1mm maps were created of the injuries in each case and transposed onto a statistical shape model of a talus; this enables the cumulative data to be analysed in Excel. Data was analysed using a Chi-squared test. From 154 cases, 104 were considered at risk and their talar domes were inspected; of these, macroscopic injuries were identified in 55 (52.4%). The prevalence of talar dome injury was greater with B-type fractures (53.5%) than C-type fractures (31.5%) (ρ = .01). Injuries were more common in men than women and presented with different distribution of injuries (ρ = .032). A significant difference in the distribution of injuries was also identified when comparing falls and motor vehicle accidents (ρ = .007). Concomitant injuries to the articular surface of the dome of the talus are relatively common, and this perhaps explains the discordance between the post-operative appearance following internal fixation and the clinical outcomes observed. These injuries were focused on the lateral third of the dome in men and MVAs, whereas women and fall mechanism were more evenly distributed. Surgeons who operatively manage high-energy pilon fractures should consider routine inspection of the talar dome to assess the possibility of associated macroscopic osteochondral injuries

Background. Lateral lumbar spine statistical shape models (SSM) have been used previously to describe associations with osteoarthritis and back pain. However, associations with factors such as osteoporosis, menopause and parity have not been explored. Methods and Results. A 143-point SSM, describing L1 to the top of L5, was applied to lateral spine iDXA scans from UK Biobank. Associations with self-reported osteoporosis, menopause, parity and back pain and the first 10 modes of variation were examined using adjusted binary logistic regression or linear regression (adjusted for age, height, weight and total spine BMD). We report odds ratios with 95% confidence intervals for each standard deviation change in mode. Complete data were available for 2494 women. Mean age was 61.5 (± 7.4) years. 1369 women reported going through menopause, 96 women self-reported osteoporosis and 339 women reported chronic back pain. 80% of women reported at least 1 live birth. Lumbar spine shape was not associated with back pain in this cohort. Two modes were associated with menopause (modes 1 & 2), 1 mode with parity (mode 1) and 2 modes with osteoporosis (modes 3 & 5). Mode 1 (43.6% total variation), describing lumbar curvature was positively associated with both menopause [OR 1.15 95% CI 1.00–1.33, p=0.05] and parity [OR 1.058 95% CI 1.03–1.0, p=0.01]. Mode 3, describing decreased vertebral height was positively associated with osteoporosis [OR 1.40 95% CI 1.14–1.73, p=0.001]. Conclusion. Menopause and parity were associated with a curvier lumbar spine and osteoporosis with decreased vertebral height. Shape was not associated with back pain. No conflicts of interest.  . Sources of funding. Wellcome Trust collaborative award ref 209233

Background. Orthopaedic surgeons are exposed to high levels of noise when performing common surgical procedures. Noise induced hearing loss (NIHL) has been demonstrated amongst senior orthopaedic staff. The objective of this study was to investigate the prevalence of hearing loss amongst arthroplasty surgeons compared to non-surgical clinicians and explore the factors associated with hearing loss. Methods. A cross-sectional prevalence study. Arthroplasty surgeons and non-surgical clinicians were recruited from orthopaedic and medical conferences. All participants were given a paper questionnaire including demographic details, hearing history and Tinnitus and Hearing Survey (THS). All participants were screened for hearing loss in a quiet room using the HearCheck Screener™ (Siemens, UK). Logistic regression was used to identify factors associated with hearing loss. All statistical models were adjusted for age, gender, smoking status and personal noise exposure. A power calculation estimated a sample size of 100 participants. Results. The HEARS study recruited 189 participants (107 arthroplasty surgeons; 82 non-surgical clinicians). Prevalence of hearing loss identified by the HearCheck Screener™; 31% arthroplasty surgeons vs 11% non-surgical clinicians. The odds of failing the HearCheck Screener™ were 3.7 times higher in arthroplasty surgeons compared to their non-surgical colleagues (p<0.004). Similarly, the odds of self-reported hearing loss were 2.79 times higher amongst arthroplasty surgeons (p<0.003). Conclusion. The prevalence of hearing loss amongst arthroplasty surgeons is significantly higher than their non-surgical colleagues. Noise generated during arthroplasty surgery should be recognised and managed to create safer working conditions

Inter-subject variability is inherently present in patient anatomy and is apparent in differences in shape, size and relative alignment of the bony structures. Understanding the variability in patient anatomy is useful for distinguishing between pathologies and to assist in surgical planning. With the aim of supporting the development of stratified orthopaedic interventions, this work introduces an Articulated Statistical Shape Model (ASSM) of the lower limb. The model captures inter-subject variability and allows reconstructing ‘virtual’ knee joints of the lower limb shape while considering pose. A training dataset consisting of 173 lower limbs from CT scans of 110 subjects (77 male, 33 female) was used to construct the ASSM of the lower limb. Each bone of the lower limb was segmented using ScanIP (Simpleware Ltd., UK), reconstructed into 3D surface meshes, and a SSM of each bone was created. A series of sizing and positioning procedures were carried out to ensure all the lower limbs were in full extension, had the same femoral length and that the femora were aligned with a coincident centre. All articulated lower limbs were represented as: (femur scale factor) × (full extension articulated lower limb + relative transformation of tibia, fibula and patella to femur). Articulated lower limbs were in full extension were used to construct a statistical shape model, representing the variance of lower limb morphology. Relative transformations of the tibia, fibula and patella versus the femur were used to form a statistical pose model. Principal component analysis (PCA) was used to extract the modes of changes in the model. The first 30 modes of the shape model covered 90% of the variance in shape and the first 10 modes of the pose model covered 90% of the pose variance. The first mode captures changes of the femoral CCD angle and the varus/valgus alignment of the knee. The second mode represents the changes in the ratio of femur to tibia length. The third mode reflects change of femoral shaft diameter and patella size. The first mode characterising pose captures the medial/lateral translation between femur and tibia. The second mode represents variation in knee flexion. The third mode reflects variation in tibio-femoral joint space. An articulated statistical modelling approach was developed to characterize inter-subject variability in lower limb morphology for a set of training specimens. This model can generate large sets of lower limbs to systematically study the effect of anatomical variability on joint replacement performance. Moreover, if a series of images of the lower limb during a dynamic activity are used as training data, this method can be applied to analyse variance of lower limb motion across a population