Use of machine learning to assess the predictive value of 3 commonly used clinical measures to quantify outcomes after total shoulder arthroplasty

Background An important psychometric parameter of validity that is rarely assessed is predictive value. In this study we utilize machine learning to analyze the predictive value of 3 commonly used clinical measures to assess 2-year outcomes after total shoulder arthroplasty (TSA). Methods XGBoost was used to analyze data from 2790 TSA patients and create predictive algorithms for the American Shoulder and Elbow Surgeons (ASES), Constant, and the University of California Los Angeles (UCLA) scores and also quantify the most meaningful predictive features utilized by these measures and for all questions comprising each measure to rank and compare their value to predict 2-year outcomes after TSA. Results Our results demonstrate that the ASES, Constant, and UCLA measures rarely considered the most-predictive features relevant to 2-year TSA outcomes and that each outcome measure was composed of questions with different distributions of predictive value. Specifically, the questions composing the UCLA score were of greater predictive value than the Constant questions, and the questions composing the Constant score were of greater predictive value than the ASES questions. We also found the preoperative Shoulder Pain and Disability Index (SPADI) score to be of greater predictive value than the preoperative ASES, Constant, and UCLA scores. Finally, we identified the types of preoperative input questions that were most-predictive (subjective self-assessments of pain and objective measurements of active range of motion and strength) and also those that were least-predictive of 2-year TSA outcomes (subjective task-specific activities of daily living questions). Discussion Machine learning can quantify the predictive value of the ASES, Constant, and UCLA scores after TSA. Future work should utilize this and related techniques to construct a more efficient and effective clinical outcome measure that incorporates subjective and objective input questions to better account for the preoperative factors that influence postoperative outcomes after TSA. Level of Evidence Level III; Retrospective Comparative Study