Predictive Models for Spine and Lower Extremity Injury After Discharge From Rehab
Condition: Musculoskeletal Injury; Spinal Injuries; Leg Injuries
MeSH terms: Wounds and Injuries; Spinal Injuries; Leg Injuries
Study type: Observational

Summary: 
      The purpose of this study is to develop algorithms that will help predict future injury
      and/or re-injury after being returned to duty from a musculoskeletal injury. After completion
      of an episode of care with a physical therapist, the subjects will undergo a battery of
      physical performance tests and fill out associated surveys. The subjects will then be
      followed for a year to identify the occurrence/re-occurence of any injuries. Based on the
      performance on the physical evaluation tests, algorithms will be derived using regression
      analysis to predict injury.

      Subjects will be recruited from the pool of patients that have recently completed physical
      rehabilitation in physical therapy clinics for their lower extremity or lumbar/thoracic spine
      injury.
    

Detailed description: 
      Subjects will be recruited across 4 medical centers after having completed a regimen of
      physical therapy for a spine or lower extremity injury. Upon discharge back to full duty,
      they will be given the opportunity to enroll in the study and undergo a battery of physical
      performance tests and associated surveys. The subjects will then be followed for a year to
      identify the occurrence of any injuries. Prediction algorithms will be derived using
      regression analysis to predict injury based on performance on the physical evaluation tests.

      The overall hypothesis is that Service Member performance on a battery of physical
      performance tests performed upon discharge from care and return to duty, will be able to
      predict 1) the risk of sustaining any injury as well as 2) reoccurrence of the same injury
      that they were seeking care for during the year following discharge from rehabilitation. The
      current assumption is that when a Service Member is discharged from medical care, it has been
      done based on the expectation that it is appropriate and safe for them to return to function
      in their operational environment. Because history of prior injury is a well-established risk
      factor, every single Service Member that is returned to duty after medical care for a
      musculoskeletal (MSK) injury is already at a higher risk for future injury than his or her
      non-injured counterpart. The investigators hypothesize that decreased performance on the
      proposed testing protocol will be related to increase in the risk of 1 year-injury and
      recurrence of injury. Successfully identifying those at increased risk of recurrence provides
      the ability for secondary and tertiary prevention programs to optimize return to duty rates.
      Injury will be defined as any new musculoskeletal injury or the re-occurrence of the same
      injury during the 1-year surveillance period.

      The battery of physical performance tests will include: Selective Functional Movement
      Assessment (SFMA), Functional Movement Screen (FMS), Upper Quarter Y-balance Test (YBT-UQ),
      Lower Quarter Y-balance Test (YBT-LQ), Closed Kinetic Chain Dorsiflexion (CKC DF), a Single
      Hop Test, Triple Hop Test, Triple Crossover Hop Test, Carry Test, and a un-weighted and
      weighted 300 yard Shuttle Run Test.

      Each subject will then also be contacted monthly via a SMS (Short Message Service, e.g. text
      message) survey for the following year to identify information about additional injury or
      profile that they may have sustained during the prior period of time. Information about
      injury will also be calculated from patient chart reviews and Department of Defense
      healthcare utilization database (claims data). This will provide a robust method in which to
      capture data injury data regardless of subject availability for follow-up.

      Subjects will be dichotomized as injured or non-injured based on the injury surveillance
      data. Key demographic, physical performance (FMS, YBT, SFMA, Hop Test, Carry test, & Shuttle
      Run), and self-report measures will be examined for group differences. Potential predictor
      variables will be entered into a backward stepwise logistic regression model to determine the
      most accurate set of variables predictive of musculoskeletal injury status.

      Risk stratification (low, moderate, or high) will be based on likelihood ratios (LR)
      associated with the clinical prediction rule for injury outlined above. A positive LR > 10
      will place the individual as high risk, a LR between 2 and 10 would place the individual as
      moderate risk. Those with a positive LR less than 2 will be listed as low risk.
    

Observational model: Cohort
Time perspective: Prospective
Sampling method: Non-Probability Sample
Study population: 
        Active duty service members that have completed a course of physical therapy for a spine or
        lower extremity injury, and then discharged to return to full duty.
      
Keywords: Injury Prediction; Return to Work; Injury Prevention