The Effects of Altitude on Concussion Incidence in the 2013-2017 National Hockey League Seasons

Limitations of Separating Athletes into High or Low-Risk Groups based on a Cut-Off. A Clinical Commentary

Background

Athlete injury risk assessment and management is an important, yet challenging task for sport and exercise medicine professionals. A common approach to injury risk screening is to stratify athletes into risk groups based on their performance on a test relative to a cut-off threshold. However, one potential reason for ineffective injury prevention efforts is the over-reliance on identifying these 'at-risk' groups using arbitrary cut-offs for these tests and measures. The purpose of this commentary is to discuss the conceptual and technical issues related to the use of a cut-off in both research and clinical practice.

Clinical Question

How can we better assess and interpret clinical tests or measures to enable a more effective injury risk assessment in athletes?

Key Results

Cut-offs typically lack strong biologic plausibility to support them; and are typically derived in a data-driven manner and thus not generalizable to other samples. When a cut-off is used in analyses, information is lost, leading to potentially misleading results and less accurate injury risk prediction. Dichotomizing a continuous variable using a cut-off should be avoided. Using continuous variables on its original scale is advantageous because information is not discarded, outcome prediction accuracy is not lost, and personalized medicine can be facilitated.

Clinical Application

Researchers and clinicians are encouraged to analyze and interpret the results of tests and measures using continuous variables and avoid relying on singular cut-offs to guide decisions. Injury risk can be predicted more accurately when using continuous variables in their natural form. A more accurate risk prediction will facilitate personalized approaches to injury risk mitigation and may lead to a decline in injury rates.

Level of Evidence

5.

Modifying Factors for Concussion Incidence and Severity in the 2013-2017 National Hockey League Seasons

Introduction

In the past few years, there has been a rising interest in both the prevalence and the short- and long-term consequences of concussions. While the main focus of concussion-based research revolves around the National Football League (NFL), attention is now shifting to other high contact leagues like the National Hockey League (NHL), where there is constant player-to-player contact as well as collisions with the perimeter boards. While the body of evidence surrounding injury and concussion rates in the NHL has substantially grown in size over the previous few years, there is still a void pertaining to the in-game effects that could modulate concussion incidence. Our study takes a novel approach to evaluate several “style of play” factors such as home/away perspective, win/loss outcome, points scored, real time length of game, time of season, and player position in modulating concussion rates.

Methods

Data on concussion incidence for the 2013-2017 National Hockey League seasons was collected utilizing FOX Sports injury tracker. Only injuries specifically diagnosed as concussions during regular and postseason games were utilized in our data set. A Google search on the reported injury was performed in order to correlate the concussion to the correct game in which the player sustained it. NHL season schedules were acquired through the online source “Hockey Reference.” There were a total of 5281 games when considering the regular and postseason games between the 2013-2017 seasons. Concussions sustained during team practices and preseason contests were not accounted for in our data set to control for inconsistent reporting. Our data set does not account for the current 2017-2018 NHL expansion with the addition of a Las Vegas team to the league.

Results

Statistical analysis of several "Style of Play" factors such as home/away perspective, win/loss outcome, points scored, real time length of game and time of season produced non-significant results pertaining to modulating concussion rate during the 2013-2017 NHL seasons. When evaluating on-ice position we noted offensive players combined to have the highest rate of concussion. Forwards (left wing (LW), right wing (RW)) demonstrated similar concussion rates, while goalies encountered the lowest concussion rate.

Conclusion

The results of our analysis demonstrated non-significance for home/away effects, win/loss results, average points scored, real time length of game, and time of season on influencing concussion rates. We noted offensive players combined to have the highest rate of concussions, while goalies encountered the least. The key limitation in our data set is the lack of reliable and publicly available data surrounding concussion incidence in the National Hockey League. Due to this drawback, our data set should be considered as an under-reported representation of the total amount of concussions spanning the 2013-2017 seasons.

Evaluation of Concussion Incidence and Modulating Factors in the 2013-2017 Australian Football League

Introduction

The increasing awareness and popularization of concussions in the research realm over the last few years have begun to shed more light on the detrimental effects associated with repetitive head trauma. While the majority of the current literature focuses on the National Football League (NFL) and National Hockey League (NHL), several other high-impact sports have been implementing concussion management protocols to protect their players. The Australian Football League (AFL) is a prime example of a major contact sport that has undertaken recent changes to its concussion assessment and management modalities. We recognize the benefit of reporting potential changes in concussion rates over the 2013-2017 AFL seasons. We were interested in some of the factors not yet evaluated before, which may contribute to the overall concussion incidence such as “style-of-play” factors” (home/away, win/loss, points scored, time of season). We hope the results of this analysis shed light on the mechanisms by which concussion rates can be mitigated across major contact sports.

Methods

Data were obtained from the weekly injury list published by the AFL, a publicly available website. Details of players listed with concussions were collected from regular season games (890 games total) of 18 AFL teams from 2013 to 2017. Weekly injury lists were retrospectively compared using THE AGE, a publicly available newspaper published and owned by Fairfax Media. Data were analyzed utilizing GraphPad Prism 6 (GraphPad Software Inc., CA, US). In addition to descriptive statistics, Fisher's exact tests, Welch’s two-tailed t-tests, and correlation tests were used. Alpha level < 0.05 was considered significant for all tests.

Results

The dataset comprised 236 total concussions between the 2013 and 2017 AFL seasons. We noted a significant increase in the concussion rate (p = 0.0010) between the 2013 and 2017 seasons. This result was complemented by a significant increase in average games missed between the 2014 and 2015 seasons (p = 0.0002); however, this trend was not significant when evaluating the 2013 and 2017 seasons directly (p = 0.0951). Further analysis into the "style of play" factors on concussion incidence, such as average points scored, win/loss, home/away, and time of season, produced non-significant results.

Conclusion

Our study identified a significant increasing trend in concussion rate and average games missed that correlate to the data analysis in other high-impact sports such as the NFL and NHL. However, further research is necessary to determine if these findings indicate the improvement in concussion management and player safety measures beginning to develop in high-impact sports. We also noted that certain “style of play” factors (points scored, win/loss, home/away, and time of season) have no significant implication on concussion rate during the 2013-2017 AFL seasons. While we consider our data source to be reliable in the reporting of concussions from the AFL, the ideal data set would comprise a medical diagnosis from the team of doctors. It may be possible that our data set is underreporting the total amount of concussions between the 2013 and 2017 AFL seasons. Return-to-play times were not ascertained directly from the team doctor for the clearance date. It may be possible that this data collection modality led to missed cases of head injury or return to play times, which could impact the reliability of our dataset.