Validation of a machine learning-derived clinical metric to quantify outcomes after total shoulder arthroplasty.

Background: We propose a new clinical assessment tool constructed using machine learning, called the Shoulder Arthroplasty Smart (SAS) score to quantify outcomes following total shoulder arthroplasty (TSA).

Methods: Clinical data from 3667 TSA patients with 8104 postoperative follow-up reports were used to quantify the psychometric properties of validity, responsiveness, and clinical interpretability for the proposed SAS score and each of the Simple Shoulder Test (SST), Constant, American Shoulder and Elbow Surgeons Standardized Shoulder Assessment Form (ASES), University of California Los Angeles (UCLA), and Shoulder Pain and Disability Index (SPADI) scores.

Results: Convergent construct validity was demonstrated, with all 6 outcome measures being moderately to highly correlated preoperatively and highly correlated postoperatively when quantifying TSA outcomes.