Bruce Jared M, Riegler Kaitlin E, Meeuwisse Willem, Comper Paul, Hutchison Michael G, Delaney J Scott, Echemendia Ruben J
Department of Biomedical and Health Informatics, University of Missouri-Kansas City School of Medicine, Kansas City, MO, 64108, USA.
Department of Neurology, University Health, Kansas City, MO, 64108, USA.
Sports Med. 2025 Mar;55(3):729-738. doi: 10.1007/s40279-024-02112-2. Epub 2024 Sep 17.
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.
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.
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.
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.
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.
识别脑震荡风险因素，如可见体征和损伤机制，有助于提高脑震荡的识别率。探索个体风险因素，如脑震荡病史，可能有助于改进现有的脑震荡风险模型和算法。
本研究的主要目的是使用机器学习技术，为出现可见体征的职业曲棍球运动员开发一个全面的、前瞻性编码的脑震荡风险模型。次要目的是检验纳入脑震荡病史是否能提高模型性能。
从国家冰球联盟（NHL）的观察项目中提取2018 - 2019赛季至2021 - 2022赛季的数据，包括与可能的脑震荡事件相关的编码可见体征和损伤机制。将每个独特的观察事件与从医疗记录中提取的数据进行匹配，以确定该事件是否与随后医生诊断的脑震荡相关。我们比较了三种基于机器学习的方法识别医生诊断脑震荡可能性的能力：条件推断树、条件推断随机森林和逻辑回归。
观察人员共识别出1563个有可见体征的独特事件（183个导致脑震荡诊断）。随机选择的训练样本有1250个事件（146例脑震荡），其余预留的测试样本有313个事件（37例脑震荡）。所得到的模型在训练数据（受试者操作特征曲线下面积[AUC] = 0.79）和预留测试数据（AUC = 0.82）中表现出色，效果显著。脑震荡病史被保留在树模型和逻辑回归模型中，每增加一次先前的脑震荡，脑震荡诊断的几率增加1.32倍。
我们提出了用于脑震荡筛查和作为诊断辅助工具的简单树模型和逻辑算法。我们的数据表明，运动员的脑震荡病史能够解释除仅由可见体征和损伤机制所解释的风险之外的额外风险。
