Glenoid and humeral head bone defects have long been recognized as major determinants in recurrent shoulder instability as well as main predictors of outcomes after surgical stabilization. However, a universally accepted method to quantify them is not available yet. The purpose of the present study is to describe a new CT method to quantify bipolar bone defects volume on a virtually generated 3D model and to evaluate its reproducibility. A cross-sectional observational study has been conducted. Forty CT scans of both shoulders were randomly selected from a series of exams previously acquired on patients affected by anterior shoulder instability. Inclusion criterion was unilateral anterior shoulder instability with at least one episode of dislocation. Exclusion criteria were: bilateral shoulder instability; posterior or multidirectional instability, previous fractures and/or surgery to both shoulders; congenital or acquired inflammatory, neurological, or degenerative diseases. For all patients, CT exams of both shoulders were acquired at the same time following a standardized imaging protocol. The CT data sets were analysed on a standard desktop PC using the software 3D Slicer. Computer-based reconstruction of the Hill-Sachs and glenoid bone defect were performed through Boolean subtraction of the affected side from the contralateral one, resulting in a virtually generated bone fragment accurately fitting the defect. The volume of the bone fragments was then calculated. All measurements were conducted by two fellowship-trained orthopaedic shoulder surgeons. Each measurement was performed twice by one observer to assess intra-observer reliability. Inter and intra-observer reliability were calculated. Intraclass Correlation Coefficients (ICC) were calculated using a two-way random effect model and evaluation of absolute agreement. Confidence intervals (CI) were calculated at 95% confidence level for reliability coefficients. Reliability values range from 0 (no agreement) to 1 (maximum agreement). The study included 34 males and 6 females. Mean age (+ SD) of patients was 36.7 + 10.10 years (range: 25 – 73 years). A bipolar bone defect was observed in all cases. Reliability of humeral head bone fragment measurements showed excellent intra-observer agreement (ICC: 0.92, CI 95%: 0.85 – 0.96) and very good interobserver agreement (ICC: 0.89, CI 95%: 0.80 – 0.94). Similarly, glenoid bone loss measurement resulted in excellent intra-observer reliability (ICC: 0.92, CI 95%: 0.85 – 0.96) and very good inter-observer agreement (ICC: 0.84, CI 95%:0.72 – 0.91). In conclusion, matching affected and intact contralateral humeral head and glenoid by reconstruction on a computer-based virtual model allows identification of bipolar bone defects and enables quantitative determination of bone loss

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

Pre-operative 3D glenoid planning improves component placement in terms of version, inclination, offset and orientation. Version and inclination measurements require the position of the inferior angle. As a consequence, current planning tools require a 3D model of the full scapula to accurately determine the glenoid parameters. Statistical shape models (SSMs) can be used to reconstruct the missing anatomy of bones. Therefore, the objective of this study is to develop and validate an SSM for the reconstruction of the inferior scapula, hereby reducing the irradiation exposure for patients. The training dataset for the statistical shape 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. A statistical shape model was then constructed from the dataset using principal component analysis. Leave-one-out cross-validation was performed to evaluate the accuracy of the predicted glenoid parameters from virtual partial scans. Five types of virtual partial scans were created on each of the training set models, where an increasing amount of scapular body was removed to mimic a partial CT scan. 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. 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, inclination, glenoid centre point position and glenoid offset. In terms of inclination and version we found a mean absolute difference between the complete model and the different virtual partial scan models of 0.5° (SD 0.4°). The maximum difference between models was 3° for inclination and 2° for version. For offset and centre point position the mean absolute difference was 0 mm with an absolute maximum of 1 mm. The magnitude of the mean and maximum differences for all anatomic measurements between the partial scan and complete models is smaller than the current surgical accuracy. Considering these findings, we believe a SSM based reconstruction technique can be used to accurately reconstruct the glenoid parameters from partial CT scans

Objectives. To explore whether orthopaedic surgeons have adopted the Proximal Fracture of the Humerus: Evaluation by Randomisation (PROFHER) trial results routinely into clinical practice. Methods. A questionnaire was piloted with six orthopaedic surgeons using a ‘think aloud’ process. The final questionnaire contained 29 items and was distributed online to surgeon members of the British Orthopaedic Association and British Elbow and Shoulder Society. Descriptive statistics summarised the sample characteristics and fracture treatment of respondents overall, and grouped them by whether they changed practice based on PROFHER trial findings. Free-text responses were analysed qualitatively for emerging themes using Framework Analysis principles. Results. There were complete responses from 265 orthopaedic and trauma surgeons who treat patients with proximal humeral fractures. Around half (137) had changed practice to various extents because of PROFHER, by operating on fewer PROFHER-eligible fractures. A third (43) of the 128 respondents who had not changed practice were already managing patients non-operatively. Those who changed practice were more likely to be younger, work in a trauma unit rather than a major trauma centre, be specialist shoulder surgeons and treat fewer PROFHER-eligible fractures surgically. This group gave higher scores when assessing validity and applicability of PROFHER. In contrast, a quarter of the non-changers were critical, sometimes emphatically, of PROFHER. The strongest theme that emerged overall was the endorsement of evidence-based practice. Conclusion. PROFHER has had an impact on surgeons’ clinical practice, both through changing it, and through underpinning existing non-operative practice. Although some respondents expressed reservations about the trial, evidence from such trials was found to be the most important influence on surgeons’ decisions to change practice. Cite this article: L. Jefferson, S. Brealey, H. Handoll, A. Keding, L. Kottam, I. Sbizzera, A. Rangan. Impact of the PROFHER trial findings on surgeons’ clinical practice: An online questionnaire survey. Bone Joint Res 2017;6:590–599. DOI: 10.1302/2046-3758.610.BJR-2017-0170

This protocol describes a pragmatic multicentre randomised controlled trial (RCT) to assess the clinical and cost effectiveness of arthroscopic and open surgery in the management of rotator cuff tears. This trial began in 2007 and was modified in 2010, with the removal of a non-operative arm due to high rates of early crossover to surgery.

Cite this article: Bone Joint Res 2014;3:155–60.

In an osteological collection of 3100 specimens, 70 were found with unilateral clavicular fractures which were matched with 70 randomly selected normal specimens. This formed the basis of a study of the incidence of arthritis of the acromioclavicular joint and the effect of clavicular fracture on the development of arthritis in the ipsilateral acromioclavicular joint. This was graded visually on a severity scale of 0 to 3. The incidence of moderate to severe arthritis of the acromioclavicular joint in normal specimens was 77% (100 specimens). In those with a clavicular fracture, 66 of 70 (94%) had arthritis of the acromioclavicular joint, compared to 63 of 70 (90%) on the non-injured contralateral side (p = 0.35).

Clavicles with shortening of 15 mm or less had no difference in the incidence of arthritis compared to those with shortening greater than 15 mm (p = 0.25). The location of the fracture had no effect on the development of arthritis.