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170 Risk factors for post-concussion subsequent musculoskeletal injuries
  1. Thomas Buckley1,
  2. Jessie Oldham3,
  3. Caroline Howard1,
  4. Robert Lynall2,
  5. Buz Swanik1,
  6. Nancy Getchell1
  1. 1University of Delaware, Newark, USA
  2. 2University of Georgia, Athens, GA, USA
  3. 3Boston Children’s Hospital, Waltham, MA., USA


Background Elevated rates (1.5 to 3.0 times) of musculoskeletal (MSK) injuries in the first year post-concussion have been recently identified in diverse athletic populations; however, clinically feasible risk factors have received limited attention.

Objective To identify clinical predictors of post-concussion subsequent musculoskeletal (MSK) injuries.

Design Prospective longitudinal.

Setting U.S. Intercollegiate Athletics .

Patients (or Participants) We enrolled 66 student-athletes (53.0% Female, Age: 20.0 ± 1.1 years old, Height: 1.75 ± 0.11m, Weight: 78.7 ± 20.9kg) from 16 sports who were diagnosed with sports-related concussions.

Interventions (or Assessment of Risk Factors) Electronic medical records were tracked for a year following the concussion for diagnosed lower extremity MSK injuries.

Main Outcome Measurements All participants completed a multifaceted concussion baseline consisting of 1) 22-item 0–6 self-reported symptom checklist with outcomes including, 1) number of symptoms endorsed, 2) total symptom score, 3) Standard Assessment of Concussion, 4) Balance Error Scoring System, 5) Immediate Post-Concussion Assessment and Cognitive Testing composite scores, 6) clinical reaction time, and 7) the King-Devick test. The concussion participants completed the same exam acutely post-concussion (<48 hours) and binary logistic regression was used to identify predictors of subsequent MSK from the change scores (Acute minus Baseline).

Results The participant demographics and injury characteristics (p=0.318,. Exp(B)=1.020) and concussion clinical outcomes (p=0.461, Exp(B)=1.200) did not predict subsequent MSK. Exploratory analysis failed to identify any individual predictive variable from the clinical measures inclduing total symptoms (Δ=9.3, p=0.738), symptoms severity (Δ=21.1, p=0.738), BESS (Δ=-0.6 errors, p=0.474), SAC (Δ=-0.7, p=0.938), Verbal Memory (Δ=1.6, p=0.064), Visual Memory (Δ=5.1, p=0.724), Motor Speed (Δ=0.6, p=0.297), Reaction Time (Δ=0.04 s, p=0.642), CRT (Δ=15.8 ms, p=0.446), King-Devick (Δ=6.9 s, p=0.792).

Conclusions None of the standard concussion assessments significantly predicted MSK injury in the year following concussion. Thus, clinicians are not able to utilize common neurological measures or participant demographics to identify those at risk for subsequent LE MSK suggesting injury prevention programs should be implemented for all post-concussion athletes

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