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. 2025 Mar;55(3):729-738.
doi: 10.1007/s40279-024-02112-2. Epub 2024 Sep 17.

A Machine Learning Approach to Concussion Risk Estimation Among Players Exhibiting Visible Signs in Professional Hockey

Jared M Bruce  1   2   3 , Kaitlin E Riegler  4 , Willem Meeuwisse  5 , Paul Comper  6   7 , Michael G Hutchison  6 , J Scott Delaney  8   9 , Ruben J Echemendia  10
Affiliations

A Machine Learning Approach to Concussion Risk Estimation Among Players Exhibiting Visible Signs in Professional Hockey

Jared M Bruce et al. Sports Med. 2025 Mar.

Abstract

Background: The identification of concussion risk factors, such as visible signs and mechanisms of injury, improves concussion identification. Exploring individual risk factors, such as concussion history, may help to improve existing concussion risk models and algorithms.

Objectives: The primary aim of the current study was to use machine learning techniques to develop a comprehensive, prospectively coded concussion risk model in professional hockey among players exhibiting visible signs. The secondary aim was to examine whether including concussion history improves model performance.

Methods: Data from the National Hockey League (NHL) spotter program, including coded visible signs and mechanisms of injury associated with possible concussive events, were extracted from the 2018-2019 to the 2021-2022 seasons. Each unique spotter event was matched with data extracted from the medical record to determine whether the event was associated with a subsequent physician diagnosed concussion. We compared the ability of three machine learning-based approaches to identify the likelihood of physician diagnosed concussion: conditional inference tree, conditional inference random forest, and logistic regression.

Results: A total of 1563 unique events with visible signs were identified by spotters (183 leading to a concussion diagnosis). A randomly selected training sample had 1250 events (146 concussions) and the remaining set-aside test sample had 313 events (37 concussions). The obtained models performed at a high level with large effects in the training [area under the receiver operating characteristic curve (AUC) = 0.79] and set-aside test data (AUC = 0.82). Concussion history was retained in the tree and logistic regression models, with each additional prior concussion associated with a 1.32 times increased odds of concussion diagnosis.

Conclusions: We present simple tree and logistic algorithms for concussion screening and as diagnostic aids. Our results show that player concussion history can explain additional risk above and beyond that explained by visible signs and mechanisms of injury alone.

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Conflict of interest statement

Declarations. Conflict of interest: W.M. is the chief medical officer for the NHL and an employee of the NHL. M.G.H. is a member of the NHL/NHLPA Concussion Subcommittee and a consultant to the NHLPA, for which he receives remuneration. P.C. is a member of the NHL/NHLPA Concussion Subcommittee and a paid consultant to the NHLPA. K.R. is a part-time employee of the NHL. J.M.B. is a part-time employee of the NHL. J.M.B. is a paid consultant for Med-IQ and Sporting KC. J.M.B. also receives grant funding from Genzyme. R.J.E. is a paid consultant for the NHL and co-chair of the NHL/NHLPA Concussion Subcommittee. He is also a paid consultant for Major League Soccer. He is currently a co-principal investigator (PI) and occasionally provides expert testimony in matters related to MTBI and sports concussion. R.J.E. was funded by the NFL (NFL-Long) through Boston Children’s Hospital. S.D. serves as the chief medical consultant for the NHLPA and the chief medical officer for CF Montreal of Major League Soccer, roles for which he is remunerated. Funding: No specific sources of funding were used to assist in the conduct of this study or the preparation of this article. Author contributions: J.M.B. conceived of the study idea and contributed to design, statistical analysis, writing, and critical revision of the manuscript; K.E.R. contributed to writing and critical revision; R.J.E. contributed to design, writing, and critical revision; P.C., W.M., J.S.D., and M.H. contributed to design and critical revision. All authors read and approved the final version of the manuscript. Ethics approval: This study was approved (IRB project # 2019826) by the UMKC IRB (FWA # 00005427). Consent to participate: All players provided consent to use their data for program evaluation and quality improvement. This project posed minimal risk and used de-identified archival data. As such, need for written informed consent was waived by the institutional review board. Data availability statement: The data are not publicly available because they contain information that could compromise the privacy of participants.

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