Understanding the Impact of ACL Reconstruction on Normalization Methods and Identifying Predictive Factors of Landing Symmetry

Abstract

ACL injuries are one of the most common knee injuries1–4, occurring in 1 out of every 3500 individuals in the Unites States5. Over 200,000 ACL reconstruction surgeries occur each year2,6–9. Following a primary ACL tear, the likelihood of experiencing a second tear increases to 10-25%3,10–15. This rate of reinjury can fluctuate based on activity level3,10,16–18. Athletes returning to sports, specifically, have higher retear rates16,17. Load symmetry has been used to assess performance and risk in patients with ACL reconstruction (ACLR)19–22. While there are ample amounts of research investigating this injury, there are gaps within the literature that need to be addressed to continue to better understand ACL injuries. When analyzing data from patients with ACLR, there are common assumptions used by many different scientists that may influence the way data can be interpreted23. Additionally, previous literature has identified influences of psychological components on injury risk of a primary ACL injury and throughout rehabilitation24–27, but there is minimal knowledge on how these components can be used to predict second ACL risk factors. Therefore, the purpose of this study was to investigate the assumptions made when data is being analyzed for this clinical population, and if psychological components can be used to predict risk factors for a second ACL injury. The common data analysis assumption tested in this study was percent stance normalization because this method has not been validated to produce accurate data in patients with ACLR. Percent stance was then compared to a time independent method. In a cohort of healthy controls and patients with ACLR, using symmetry to assess loading differences, there were differences found in symmetry metrics commonly used to assess performance, including peak impact force (PIF), loading rate, impulse, and time to peak. These results show a need to revisit common assumptions used to analyze data when including patients with ACLR. Future studies could conduct a similar analysis in different clinical populations. Following this analysis, psychological components, ACL-RSI, M-LOC, and GAD-7 surveys, and physical factors were combined in a regression model to predict landing symmetry. In both unilateral and bilateral landings load asymmetry has been identified as a risk factor for reinjury28. Backwards multivariate regression models were created for three unilateral and two bilateral landing tasks. Each model included both one or more psychological components and previously identified risk factors in the final factors to best predict PIF. However, the only models that could explain an adequate amount of variance were the unilateral landing models (single hop R2= .351, triple hop R2= .423). These models show the importance of including psychological components and previously researched risk factors to best understand reinjury risk in patients with ACLR. The results from this study indicate ways to potentially improve analysis of patients with ACLR. When investigating this population, testing common assumptions made for healthy controls and inclusion of psychological components when assessing performance may improve interpretation and can help clinicians better identify risk for patients with ACLR.