Background: Implant manufacturers typically offer several sizes of a humeral stem for shoulder arthroplasty so that time zero fixation can be achieved with the optimal size. Stem size can be templated preoperatively but is definitively determined intraoperatively. The purpose of this study was to determine if preoperatively acquired parameters, including patient demographics and imaging, could be used to reliably predict intraoperative humeral stem size.
Methods: A cohort of 290 patients that underwent shoulder arthroplasty (116 anatomic and 174 reverse) was analyzed to create a regression formula to predict intraoperative stem size. The initial cohort was separated into train and test groups (randomly selected 80% and 20%, respectively). Patient demographics, anatomical measurements, and statistical shape model parameters determined from a preoperative shoulder arthroplasty planning software program were used for multilinear regression. The implant used for all cases was a short-stemmed metaphyseal-fit prosthesis.
Results: Metaphyseal bone density, humeral statistical shape model parameters, and humeral intramedullary canal diameter were identified as highly predictive of intraoperative final humeral prosthesis size. On the train group, a coefficient of determination R2 of 0.63 was obtained for the multilinear regression equation combining these parameters. When analyzing the cohort for the prediction of stem size in the test group, 95% were within plus or minus one size of that used during surgery.
Conclusion: Preoperative criteria such as humeral geometry and proximal humeral bone density can be combined in a single multilinear equation to predict intraoperative humeral stem size within one size variation. Embedding the surgeon's decision-making process into an automated algorithm potentially allows this process to be applied across the surgical community. Predicting intraoperative decisions such as humeral stem size also has potential implications for the management of implant stocks for both manufacturers and health-care facilities.
|Number of pages||6|
|Publication status||Published - Nov 2022|