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Concussion Risk Between Individual Football Players: Survival Analysis of Recurrent Events and Non-events
Annals of Biomedical Engineering ( IF 3.8 ) Pub Date : 2020-10-28 , DOI: 10.1007/s10439-020-02675-x
Steven Rowson 1 , Eamon T Campolettano 1 , Stefan M Duma 1 , Brian Stemper 2 , Alok Shah 2 , Jaroslaw Harezlak 3 , Larry Riggen 4 , Jason P Mihalik 5 , Alison Brooks 6 , Kenneth L Cameron 7, 8 , Steven J Svoboda 7 , Megan N Houston 7 , Thomas McAllister 9 , Steven Broglio 10 , Michael McCrea 2
Affiliation  

Concussion tolerance and head impact exposure are highly variable among football players. Recent findings highlight that head impact data analyses need to be performed at the subject level. In this paper, we describe a method of characterizing concussion risk between individuals using a new survival analysis technique developed with real-world head impact data in mind. Our approach addresses the limitations and challenges seen in previous risk analyses of football head impact data. Specifically, this demonstrative analysis appropriately models risk for a combination of left-censored recurrent events (concussions) and right-censored recurrent non-events (head impacts without concussion). Furthermore, the analysis accounts for uneven impact sampling between players. In brief, we propose using the Consistent Threshold method to develop subject-specific risk curves and then determine average risk point estimates between subjects at injurious magnitude values. We describe an approach for selecting an optimal cumulative distribution function to model risk between subjects by minimizing injury prediction error. We illustrate that small differences in distribution fit can result in large predictive errors. Given the vast amounts of on-field data researchers are collecting across sports, this approach can be applied to develop population-specific risk curves that can ultimately inform interventions that reduce concussion incidence



中文翻译:

单个足球运动员之间的脑震荡风险:复发事件和非事件的生存分析

足球运动员的脑震荡耐受性和头部撞击暴露程度差异很大。最近的研究结果强调,头部撞击数据分析需要在学科层面进行。在本文中,我们描述了一种描述个体之间脑震荡风险的方法,该方法使用一种新的生存分析技术,该技术是根据真实世界的头部撞击数据开发的。我们的方法解决了之前足球头部撞击数据风险分析中的局限性和挑战。具体而言,这种示范性分析适当地模拟了左删失复发性事件(脑震荡)和右删失复发性非事件(无脑震荡的头部撞击)组合的风险。此外,该分析考虑了玩家之间不均匀的影响采样。简单来说,我们建议使用一致阈值方法来开发特定于受试者的风险曲线,然后确定受试者之间在有害幅度值下的平均风险点估计值。我们描述了一种通过最小化伤害预测误差来选择最佳累积分布函数以对受试者之间的风险进行建模的方法。我们说明分布拟合的微小差异会导致较大的预测误差。鉴于研究人员在各种运动中收集了大量现场数据,这种方法可用于制定特定人群的风险曲线,最终为降低脑震荡发生率的干预措施提供信息 我们描述了一种通过最小化伤害预测误差来选择最佳累积分布函数以对受试者之间的风险进行建模的方法。我们说明分布拟合的微小差异会导致较大的预测误差。鉴于研究人员在各种运动中收集了大量现场数据,这种方法可用于制定特定人群的风险曲线,最终为降低脑震荡发生率的干预措施提供信息 我们描述了一种通过最小化伤害预测误差来选择最佳累积分布函数以对受试者之间的风险进行建模的方法。我们说明分布拟合的微小差异会导致较大的预测误差。鉴于研究人员在各种运动中收集了大量现场数据,这种方法可用于制定特定人群的风险曲线,最终为降低脑震荡发生率的干预措施提供信息

更新日期:2020-10-30
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