Aims. The Mathys Affinis Short is the most frequently used stemless total shoulder prosthesis in the UK. The purpose of this prospective cohort study is to report the survivorship, clinical, and radiological outcomes of the first independent series of the Affinis Short prosthesis. Methods. From January 2011 to January 2019, a total of 141 Affinis Short prostheses were implanted in 127 patients by a single surgeon. Mean age at time of surgery was 68 (44 to 89). Minimum one year and maximum eight year follow-up (mean 3.7 years) was analyzed using the Oxford Shoulder Score (OSS) at latest follow-up. Kaplan-Meier survivorship analysis was performed with implant revision as the endpoint. Most recently performed radiographs were reviewed for component radiolucent lines (RLLs) and proximal humeral migration. Results. Five shoulders underwent revision surgery (3.5%); three for rotator cuff failure, one for infection, and one for component
The aim of this study was to explore why some calcar screws are malpositioned when a proximal humeral fracture is treated by internal fixation with a locking plate, and to identify risk factors for this phenomenon. Some suggestions can be made of ways to avoid this error. We retrospectively identified all proximal humeral fractures treated in our institution between October 2016 and October 2018 using the hospital information system. The patients’ medical and radiological data were collected, and we divided potential risk factors into two groups: preoperative factors and intraoperative factors. Preoperative factors included age, sex, height, weight, body mass index, proximal humeral bone mineral density, type of fracture, the condition of the medial hinge, and medial metaphyseal head extension. Intraoperative factors included the grade of surgeon, neck-shaft angle after reduction, humeral head height, restoration of medial support, and quality of reduction. Adjusted binary logistic regression and multivariate logistic regression models were used to identify pre- and intraoperative risk factors. Area under the curve (AUC) analysis was used to evaluate the discriminative ability of the multivariable model.Aims
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