ACL Injury Predictions, Christmas Trees, and String Theory
📝 Weekly paper summary
Category
Systematic Review & Meta-Analysis
Context
Previous research suggests that non-contact anterior cruciate ligament (ACL) injuries result from various kinematic and kinetic factors during landings. As a result, researchers have conducted numerous studies assessing the strength of these kinematic and kinetic risk factors, such as knee valgus angle and moment, knee flexion angle and moment, trunk angle, and the vertical ground reaction force. When designing (p)rehabilitation programs, practitioners have used this research to reduce the risk of sustaining future ACL injuries. However, some researchers have argued the association between these risk factors and ACL injury has yielded unclear results. Therefore, this study's purpose was to aggregate the existing prospective studies to evaluate the risk of sustaining an ACL injury given the proposed risk factors.
Correctness
Although not a criticism of the final work of the authors, it is worth noting that of the 2867 abstracts screened, only nine studies were able to be included in this analysis. Of these nine studies, the kinematic measurement techniques (3D Cardan-Euler angle, 2D projection angle, minimum knee distance) differed between studies. Thus, the researchers aggregated data from 2-4 studies for each biomechanical risk factor. Furthermore, the tasks/demands were not always consistent between studies within this smaller pool of studies (e.g., some performed drop vertical jumps and others performed single-leg squats). In addition to making statistical aggregation challenging, it did not permit the researchers to build any funnel plots to explore publication bias. These are significant limitations to consider when interpreting the aggregated findings.
Contributions
- There is no difference between baseline 3D knee abduction, 2D peak knee abduction angle, 2D minimum knee difference, or peak knee abduction moment in those who did versus did not sustain an ACL injury.
- I've written much about the challenges associated with predicting injuries on my blog, which I think is worth checking out as well. Mainly, there's a big difference between the two statements:
- The probability that someone displays large knee valgus angles and moments given an ACL injury (i.e., \(P(risk \space factor | ACL \space injury)\)
- The probability of an ACL injury given that someone displays large knee valgus angles and moments (i.e., \(P(ACL \space injury | risk \space factor)\)
In practice, we are trying to do (2) using data from (1), which will require additional information regarding the base rate of the injury and the false-positive rate of the injury prediction tool. Predicting injuries, much like anything else, is hard. Thankfully, we don't need to predict injuries to prevent them!
🧠 Fun fact of the week
Christmas trees are synonymous with the holiday season in North America. Did you know, though, that Queen Victoria's husband, Prince Albert, initially popularized the tradition in the mid-1800s? Evergreen trees were already popular in the winter season for thousands of years, but it was initially in Germany where people used them to celebrate the holiday season around the 1500s! Even more fun is that for a while, the tradition was to hang the trees upside down to represent the Holy Trinity to the Pagans:
🎙 Podcast recommendation
🗣 Quote of the week
"Don’t fight stress. Embrace it. Turn it on itself. Use it to make yourself sharper and more alert. Use it to make you think and learn and get better and smarter and more effective."
- Jocko Willink, 'Discipline Equals Freedom.'