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Generating the American Shoulder and Elbow Surgeons Score Using Multivariable Predictive Models and Computer Adaptive
Matthew S Tenan1, Joseph W Galvin1, Timothy C Mauntel1
1Investigation performed at the Defense Health Agency, Military Health System for the US Military, Rosslyn, Virginia, USA.
The American Journal of Sports Medicine
|February 1, 2021
Summary
This study developed a new algorithm to predict shoulder surgery outcomes using PROMIS scores, offering a more accurate and efficient assessment than traditional methods. The tool reduces patient survey burden and provides reliable American Shoulder and Elbow Surgeons (ASES) score approximations.
Area of Science:
- Orthopedic surgery outcomes research
- Patient-reported outcome measures (PROMs) evolution
- Musculoskeletal condition assessment
Background:
- Patient-reported outcome measures (PROMs) for shoulder conditions are evolving.
- Previous studies correlating PROMIS CATs to ASES scores focused on single domains (pain or function).
- A multivariable prediction tool to convert PROMIS scores to legacy scores was lacking.
Purpose of the Study:
- To establish a valid predictive model of American Shoulder and Elbow Surgeons (ASES) scores.
- Utilize a nonlinear combination of PROMIS domains for physical function and pain.
- Develop a tool for converting PROMIS scores to ASES scores.
Main Methods:
- Utilized the Military Orthopaedics Tracking Injuries and Outcomes Network (MOTION) database.
- Included patients who underwent shoulder surgery and completed ASES, PROMIS Physical Function, and PROMIS Pain Interference.
- Created and validated nonlinear multivariable predictive models using "leave 1 out" techniques and MCID/SCB analysis.
Main Results:
- 909 patients provided 1502 complete observations.
- PROMIS CAT predictive models strongly validated to predict ASES scores (Pearson coefficient = 0.76-0.78; R² = 0.57-0.62).
- The derived ASES index demonstrated effectiveness and reliability in recreating ASES scores with a lower MCID/SCB than the ASES itself.
Conclusions:
- PROMIS CAT predictive models approximate ASES scores within 13-14 points, improving accuracy.
- The developed ASES index algorithm is freely available online and offers a lower MCID/SCB.
- This algorithm reduces patient survey burden by 11 questions and provides a reliable ASES analog for clinicians.
