WHAT DOES RETURN TO PRE-INJURY RISK MEAN?

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Abstract

Background

Previous injury is the strongest predictor of subsequent injury. Some authors suggest injury always causes permanent structural damage, or that rehabilitation programs are grossly inadequate.

Objective

To describe the limitations of commonly used analyses assessing subsequent injury risk, and to explicitly define what “100% effective rehabilitation” means.

Design

Simulated studies based on observed data professional sport and performing arts.

Setting

Simulations were conducted with 100% rehabilitation post first injury (return to pre-injury state), in contexts where injury risk was stable over the season, and increased over the season.

Patients (or Participants)

Simulated data.

Interventions (or Assessment of Risk Factors)

The main independent variables are previous injury, time of first injury, and position. We included “participant” as a random effect.

Main Outcome Measurements

Injury risk expressed as injuries per games played.

Results

When our simulated data with 100% effective rehabilitation was based on constant injury risk during the season, traditional analyses found that previous injury was still strongly associated with subsequent injury. A causal analysis that included the participant as a random effect correctly showed that the injury risk for participants remained constant. When injury risk increased over the season, the traditional analysis found that previous injury was even more strongly associated with subsequent injury. A causal analysis that only included the participant as a random effect also suggested a higher injury risk. However, when the appropriate control group was chosen for comparison, the causal analysis correctly illustrated that pre-injury risk did not increase following an initial injury.

Conclusions

Conclusions about the effectiveness of rehabilitation programs using traditional risk factor analysis will always show an increased risk of subsequent injury even if none exists. A causal analytical approach correctly identifies when the risk is present, but requires careful consideration about what is the appropriate control group for the given question.

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