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Predicting Subsequent Injury after Being Cleared to Return to Work from Initial Lumbar or Lower Extremity Injury.

Daniel I Rhon, Phillip J Plisky1, Kyle Kiesel1

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This study developed a predictive model to identify military service members at high risk for re-injury after returning to full duty. The model accurately identified 25 more individuals per 100 at risk, improving return-to-duty decisions.

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Area of Science:

  • Sports Medicine
  • Musculoskeletal Injury Research
  • Predictive Modeling in Healthcare

Background:

  • Musculoskeletal injuries are common in military service members.
  • Returning to full duty prematurely increases re-injury risk.
  • Accurate risk assessment is crucial for optimal return-to-duty decisions.

Purpose of the Study:

  • To develop and validate a multivariable model for predicting re-injury risk in military personnel after initial injury clearance.
  • To improve decision-making processes for return-to-full-duty protocols.

Main Methods:

  • A prospective cohort study involving 450 military service members cleared for full duty after a musculoskeletal injury.
  • Data collected included medical history, demographics, psychological profile, and extensive physical performance metrics.
  • Injury occurrence within one year was tracked via text messages, medical records, and limited duty databases.

Main Results:

  • The final model included 15 variables and demonstrated good predictive performance with an Area Under the Curve (AUC) of 0.74.
  • The model showed very good calibration (score 1.05).
  • The predictive model identified an additional 25 soldiers per 100 at high risk for re-injury compared to no prediction model, with a net benefit of 0.251.

Conclusions:

  • A multivariable model accurately predicts re-injury risk in tactical athletes returning to full duty.
  • The developed model offers superior performance compared to not using any predictive tool.
  • This tool can guide clinical decisions to prevent subsequent injuries in service members.