AAOS Shoulder & Elbow Registry Debuts Shoulder Arthroplasty Predictive Model
by Elizabeth Hofheinz, M.P.H., M.Ed.
Thanks to a donation from the famed Campbell Clinic Orthopaedics in Memphis, Tennessee, shoulder surgeons everywhere can fine tune their preoperative conversations with patients. The gift, made to the American Academy of Orthopaedic Surgeons Shoulder & Elbow Registry (SER), was the Shoulder Arthroplasty Predictive (SHARP) Model, a tool designed to help surgeons predict postoperative shoulder arthroplasty outcomes.
The SHARP Model, which is exclusively for SER participants, helps guide “preoperative, evidence-based conversations between surgeons and patients and set appropriate expectations around surgical outcomes based on a patient’s individual health.”
The SHARP Model takes into account factors such as age, preoperative American Shoulder and Elbow Surgeons score, disability, chronic obstructive pulmonary disease, and type of shoulder arthroplasty. The model was created by the Campbell Clinic utilizing pooled data from 1,947 patients at Hospital for Special Surgery, Rothman Orthopaedic Institute and Atlantis Orthopaedics.
According to Jessica Welter, D.O., who recently completed her fellowship in sports, shoulder and elbow surgery at Campbell Clinic, “The shoulder predictive model is a great resource for clinicians who need to educate patients on their expected outcomes after shoulder arthroplasty procedures. As an early-practice surgeon, I do not have the benefit of years of surgery experience yet to predict how individual patients will respond to different procedures. This model provides a turnkey, in-office tool to help guide treatment conversations and decisions.”
To date, there are more than 100 SER participating facilities including hospitals, private practices, and ambulatory surgical centers spanning 31 states across the United States. In total, more than 10,500 patient procedures have been submitted.
Providing highlights of the model was Gerald R. Williams Jr., M.D.,chair of the AAOS’ SER Steering Committee.“The model uses preoperative factors such as patient age, preoperative ASES score, disability, chronic obstructive pulmonary disease, and type of shoulder arthroplasty to predict surgical outcomes. With varying outcomes for shoulder arthroplasty, the model offers point-of-care feedback to guide treatment decisions and conversations on an individual level.”
Asked for an example of how this might be used to guide discussions between patients and physicians, Dr. Williams commented to OSN, “Patients are often challenged with deciding whether or not they are candidates for reverse or anatomic shoulder arthroplasty. This model can help physicians inform their patients about what they as an individual might expect as a surgical outcome from each.”
For a demonstration or to learn more about the SER can connect with an AAOS Registry engagement associate via email RegistryEngagement@aaos.org, phone 847-292-0530 or online at www.aaos.org/registries.