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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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
ASESPROMISpatient-reported outcomespredictionshoulder

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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.