Using machine learning to predict internal rotation after anatomic and reverse total shoulder arthroplasty.

Background: Improvement in internal rotation (IR) after anatomic (aTSA) and reverse (rTSA) total shoulder arthroplasty is difficult to predict, with rTSA patients experiencing greater variability and more limited IR improvements than aTSA patients. The purpose of this study is to quantify and compare the IR score for aTSA and rTSA patients and create supervised machine learning that predicts IR after aTSA and rTSA at multiple postoperative time points.

Methods: Clinical data from 2270 aTSA and 4198 rTSA patients were analyzed using 3 supervised machine learning techniques to create predictive models for internal rotation as measured by the IR score at 6 postoperative time points.

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.

Using machine learning to predict clinical outcomes after shoulder arthroplasty with a minimal feature set.

Background: A machine learning analysis was conducted on 5774 shoulder arthroplasty patients to create predictive models for multiple clinical outcome measures after anatomic total shoulder arthroplasty (aTSA) and reverse total shoulder arthroplasty (rTSA). The goal of this study was to compare the accuracy associated with a full-feature set predictive model (ie, full model, comprising 291 parameters) and a minimal-feature set model (ie, abbreviated model, comprising 19 input parameters) to predict clinical outcomes to assess the efficacy of using a minimal feature set of inputs as a shoulder arthroplasty clinical decision-support tool.

Methods: Clinical data from 2153 primary aTSA patients and 3621 primary rTSA patients were analyzed using the XGBoost machine learning technique to create and test predictive models for multiple outcome measures at different postoperative time points via the full and abbreviated models.

What Is the Accuracy of Three Different Machine Learning Techniques to Predict Clinical Outcomes After Shoulder Arthroplasty?

Background: Machine learning techniques can identify complex relationships in large healthcare datasets and build prediction models that better inform physicians in ways that can assist in patient treatment decision-making. In the domain of shoulder arthroplasty, machine learning appears to have the potential to anticipate patients' results after surgery, but this has not been well explored.

Questions/purposes: (1) What is the accuracy of machine learning to predict the American Shoulder and Elbow Surgery (ASES), University of California Los Angeles (UCLA), Constant, global shoulder function, and VAS pain scores, as well as active abduction, forward flexion, and external rotation at 1 year, 2 to 3 years, 3 to 5 years, and more than 5 years after anatomic total shoulder arthroplasty (aTSA) or reverse total shoulder arthroplasty (rTSA)? (2) What is the accuracy of machine learning to identify whether a patient will achieve clinical improvement that exceeds the minimum clinically important difference (MCID) threshold for each outcome measure? (3) What is the accuracy of machine learning to identify whether a patient will achieve clinical improvement that exceeds the substantial clinical benefit threshold for each outcome measure?

Methods: A machine learning analysis was conducted on a database of 7811 patients undergoing shoulder arthroplasty of one prosthesis design to create predictive models for multiple clinical outcome measures.

Development and Prospective Validation of a Machine Learning-Based Risk of Readmission Model in a Large Military Hospital.

Background: Thirty-day hospital readmissions are a quality metric for health care systems. Predictive models aim to identify patients likely to readmit to more effectively target preventive strategies. Many risk of readmission models have been developed on retrospective data, but prospective validation of readmission models is rare.