The purpose of this study is to report the clinical and radiological outcomes of patients undergoing primary or revision reverse total shoulder arthroplasty using custom 3D printed components to manage severe glenoid bone loss with a minimum of 2-year follow-up. After ethical approval (reference: 17/YH/0318), patients were identified and invited to participate in this observational study. Inclusion criteria included: 1) severe glenoid bone loss necessitating the need for custom implants; 2) patients with definitive glenoid and humeral components implanted more than 2 years prior; 3) ability to comply with patient reported outcome questionnaires. After seeking consent, included patients underwent clinical assessment utilising the Oxford Shoulder Score (OSS), Constant-Murley score, American Shoulder and Elbow Society Score (ASES), and quick Disabilities of the Arm, Shoulder, and Hand Score (quickDASH). Radiographic assessment included AP and axial projections. Patients were invited to attend a CT scan to confirm osseointegration. Statistical analysis utilised included descriptive statistics (mean and standard deviation) and paired t test for parametric data. 3 patients had revision surgery prior to the 2-year follow-up. Of these, 2/3 retained their custom glenoid components. 4 patients declined to participate. 5 patients were deceased at the time of commencement of the study. 21 patients were included in this analysis. The mean follow-up was 36.1 months from surgery (range 22–60.2 months). OSS improved from a mean 16 (SD 9.1) to 36 (SD 11.5) (p < 0.001). Constant-Murley score improved from mean 9 (SD 9.2) to 50 (SD 16.4) (p < 0.001). QuickDASH improved from mean 67 (SD 24) to 26 (SD 27.2) (p = 0.004). ASES improved from mean 28 (SD 24.8) to 70 (SD 23.9) (p = 0.007). Radiographic evaluation demonstrated good osseointegration in all 21 included patients. The utility of custom 3D-printed components for managing severe glenoid bone loss in primary and revision reverse total shoulder arthroplasty yields significant clinical improvements in this complex patient cohort

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