Faculty Bibliography

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. The SAS score was most correlated with the UCLA score and least correlated with the SST. No clinical outcome score exhibited significant floor effects preoperatively or postoperatively or significant ceiling effects preoperatively; however, significant ceiling effects occurred postoperatively for each of the SST (44.3%), UCLA (13.9%), ASES (18.7%), and SPADI (19.3%) measures. Ceiling effects were more pronounced for anatomic than reverse TSA, and generally, men, younger patients, and whites who received TSA were more likely to experience a ceiling effect than TSA patients who were female, older, and of non-white race or ethnicity. The SAS score had the least number of patients with floor and ceiling effects and also exhibited no response bias in any patient characteristic analyzed in this study. Regarding clinical interpretability, patient satisfaction anchor-based thresholds for minimal clinically importance difference and substantial clinical benefit were quantified for all 6 outcome measures; the SAS score thresholds were most similar in magnitude to the Constant score. Regarding responsiveness, all 6 outcome measures detected a large effect, with the UCLA exhibiting the most responsiveness and the SST exhibiting the least. Finally, each of the SAS, ASES, Constant, and SPADI scores had similarly large standardized response mean and effect size responsiveness. DISCUSSION/CONCLUSIONS:The 6-question SAS score is an efficient TSA-specific outcome measure with equivalent or better validity, responsiveness, and clinical interpretability as 5 other historical assessment tools. The SAS score has an appropriate response range without floor or ceiling effects and without bias in any target patient characteristic, unlike the age, gender, or race/ethnicity bias observed in the ceiling scores with the other outcome measures. Because of these substantial benefits, we recommend the use of the new SAS score for quantifying TSA outcomes.

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

INTRODUCTION/BACKGROUND:It is important to understand the current characteristics of orthopaedic surgery program leadership, especially in the current climate of modern medicine. The purpose of this report was to describe the demographic, academic, and geographic characteristics of current orthopaedic chairs and program directors (PDs). METHODS:Orthopaedic surgery residency programs were obtained from the Accreditation Council for Graduate Medical Education website and cross-referenced with the Electronic Residency Application Service, identifying 161 residency programs for the 2018 to 2019 cycle. All data were collected in January 2020 to best control for changes in leadership. Demographic and academic information were collected from public websites. For geographic analysis, the United States was divided into five regions, and training locations were categorized as appropriate. RESULTS:A total of 153 chairs and 161 PDs were identified. 98.0% of chairs were men versus 88.8% of PDs (P = 0.001). Chairs had been in practice and in their current position for longer than PDs (26.4 vs 16.8 years [P < 0.005] and 9.1 vs 7.1 years [P = 0.014], respectively). Chairs had more publications and were more likely to be professors than PDs. PDs were more likely to remain at both the same region and institution that they trained in residency. The most common subspecialty was sports among chairs and trauma among PDs, although when compared with national averages orthopaedic trauma and orthopaedic oncology were the most overrepresented subspecialties. CONCLUSION/CONCLUSIONS:Orthopaedic chairs are more likely to be men, have had longer careers, and have more academic accomplishments than their PD counterparts. Geography appears to have an association with where our leaders end up, especially for PDs. Subspecialization does not notably influence leadership positions, although orthopaedic trauma and orthopaedic oncology surgeons are more commonly represented than expected. This report serves to identify the current state of orthopaedic leadership and may provide guidance for those who seek these leadership positions.