The goal of shoulder arthroplasty is to improve comfort and function for a variety of degenerative conditions. Shoulder surgeons who engage an increasingly complex array of clinical problems must understand the factors that may negatively impact results and lead to a higher risk of complications. A better understanding of risk factors will help not only educate patients about potential adverse outcomes, but it also leads to improved ways to mitigate complications by addressing their root cause where possible.
Thomas Obermeyer, MD, reviews a comparison study of readmissions and complications in inpatient and outpatient settings for patients 65 years and older. Read his thoughts on reevaluating shoulder arthroplasty as an inpatient-only procedure in our latest blog post.
Shoulder Arthroplasty Smart Score: The World’s First Machine Learning-Derived Outcome Measure | Surgeons and researchers worldwide can now quantify shoulder patient outcomes with a new, more efficient measure called “Smart Score”.
Teaming up with KenSci, a data science company located in Seattle, Wash., Exactech has been at the forefront of using ML to better predict outcomes and complications after shoulder arthroplasty. This work is based on Exactech’s clinical database which includes over 11,000 patient visits from 35 centers around the world–all using a standardized data collection tool that records information on demographics, diagnosis, comorbidities, preoperative function, implant information and post-operative function at multiple time points. Predict+ was built on ML algorithms which established a 19-input minimal feature set that was most highly predictive of outcomes and complications after anatomic or reverse shoulder arthroplasty.
Computer navigation leads to more accurate glenoid targeting during Total Shoulder Arthroplasty (TSA) compared with three-dimensional (3D) preoperative planning alone.
The FDA approved the use of the reverse shoulder prosthesis in the United States in 2004, nearly 25 years following its re-debut in France.