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返回运动预测准确率的 MLG-R 分类系统的腘绳肌损伤在足球运动员:一种机器学习的方法。

Return to Play Prediction Accuracy of the MLG-R Classification System for Hamstring Injuries in Football Players: A Machine Learning Approach.

机构信息

FC Barcelona Medical Department, Barcelona, Spain.

Hospital Universitari Dexeus (ICATME), Barcelona, Spain.

出版信息

Sports Med. 2022 Sep;52(9):2271-2282. doi: 10.1007/s40279-022-01672-5. Epub 2022 May 24.

DOI:10.1007/s40279-022-01672-5
PMID:35610405
Abstract

BACKGROUND AND OBJECTIVE

Muscle injuries are one of the main daily problems in sports medicine, football in particular. However, we do not have a reliable means to predict the outcome, i.e. return to play from severe injury. The aim of the present study was to evaluate the capability of the MLG-R classification system to grade hamstring muscle injuries by severity, offer a prognosis for the return to play, and identify injuries with a higher risk of re-injury. Furthermore, we aimed to assess the consistency of our proposed system by investigating its intra-observer and inter-observer reliability.

METHODS

All male professional football players from FC Barcelona, senior A and B and the two U-19 teams, with injuries that occurred between February 2010 and February 2020 were reviewed. Only players with a clinical presentation of a hamstring muscle injury, with complete clinic information and magnetic resonance images, were included. Three different statistical and machine learning approaches (linear regression, random forest, and eXtreme Gradient Boosting) were used to assess the importance of each factor of the MLG-R classification system in determining the return to play, as well as to offer a prediction of the expected return to play. We used the Cohen's kappa and the intra-class correlation coefficient to assess the intra-observer and inter-observer reliability.

RESULTS

Between 2010 and 2020, 76 hamstring injuries corresponding to 42 different players were identified, of which 50 (65.8%) were grade 3, 54 (71.1%) affected the biceps femoris long head, and 33 of the 76 (43.4%) were located at the proximal myotendinous junction. The mean return to play for grades 2, 3, and 3 injuries were 14.3, 12.4, and 37 days, respectively. Injuries affecting the proximal myotendinous junction had a mean return to play of 31.7 days while those affecting the distal part of the myotendinous junction had a mean return to play of 23.9 days. The analysis of the grade 3 biceps femoris long head injuries located at the free tendon showed a median return to play time of 56 days while the injuries located at the central tendon had a shorter return to play of 24 days (p = 0.038). The statistical analysis showed an excellent predictive power of the MLG-R classification system with a mean absolute error of 9.8 days and an R-squared of 0.48. The most important factors to determine the return to play were if the injury was at the free tendon of the biceps femoris long head or if it was a grade 3 injury. For all the items of the MLG-R classification, the intra-observer and inter-observer reliability was excellent (k > 0.93) except for fibres blurring (κ = 0.68).

CONCLUSIONS

The main determinant for a long return to play after a hamstring injury is the injury affecting the connective tissue structures of the hamstring. We developed a reliable hamstring muscle injury classification system based on magnetic resonance imaging that showed excellent results in terms of reliability, prognosis capability and objectivity. It is easy to use in clinical daily practice, and can be further adapted to future knowledge. The adoption of this system by the medical community would allow a uniform diagnosis leading to better injury management.

摘要

背景与目的

肌肉损伤是运动医学中常见的问题之一,尤其是在足球运动中。然而,我们目前还没有一种可靠的方法来预测严重损伤的预后,即重返赛场。本研究的目的是评估 MLG-R 分类系统对腘绳肌损伤严重程度的分级能力,提供重返赛场的预后,并识别具有更高再损伤风险的损伤。此外,我们旨在通过研究其观察者内和观察者间的可靠性来评估我们提出的系统的一致性。

方法

对 2010 年 2 月至 2020 年 2 月期间来自巴塞罗那足球俱乐部、高级 A 队和 B 队以及两支 U-19 队的所有男性职业足球运动员的损伤进行了回顾。仅纳入有典型的腘绳肌损伤临床表现、完整的临床资料和磁共振成像的运动员。使用线性回归、随机森林和极端梯度提升三种不同的统计和机器学习方法,评估 MLG-R 分类系统中每个因素在确定重返赛场以及提供预期重返赛场预测方面的重要性。我们使用 Cohen's kappa 和组内相关系数来评估观察者内和观察者间的可靠性。

结果

2010 年至 2020 年期间,共确定了 76 例腘绳肌损伤,涉及 42 名不同的运动员,其中 50 例(65.8%)为 3 级,54 例(71.1%)累及股二头肌长头,76 例中有 33 例(43.4%)位于近肌腱肌腹交界处。2 级、3 级和 3 级损伤的平均重返赛场时间分别为 14.3、12.4 和 37 天。累及近肌腱肌腹交界处的损伤平均重返赛场时间为 31.7 天,而累及肌腱肌腹交界处远端的损伤平均重返赛场时间为 23.9 天。对 3 级股二头肌长头损伤位于游离肌腱处的分析显示,平均重返赛场时间为 56 天,而位于中央肌腱处的损伤重返赛场时间较短,为 24 天(p = 0.038)。统计分析显示,MLG-R 分类系统具有出色的预测能力,平均绝对误差为 9.8 天,R-squared 为 0.48。决定重返赛场时间的最重要因素是损伤是否位于游离的股二头肌长头肌腱或是否为 3 级损伤。对于 MLG-R 分类系统的所有项目,观察者内和观察者间的可靠性均为优秀(κ值>0.93),除纤维模糊(κ值=0.68)外。

结论

腘绳肌损伤后重返赛场时间较长的主要决定因素是损伤累及腘绳肌的结缔组织结构。我们基于磁共振成像开发了一种可靠的腘绳肌损伤分类系统,该系统在可靠性、预后能力和客观性方面表现出色。它在临床实践中易于使用,并可进一步适应未来的知识。如果医学界采用该系统,将可以进行统一的诊断,从而更好地管理损伤。

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