Background:

Injury risk prediction is an emerging field in which more research is needed to recognize the best practices for accurate injury risk assessment. Important issues related to predictive machine learning need to be considered, for example, to avoid overinterpreting the observed prediction performance.

Purpose:

To carefully investigate the predictive potential of multiple predictive machine learning methods on a large set of risk factor data for anterior cruciate ligament (ACL) injury; the proposed approach takes into account the effect of chance and random variations in prediction performance.

Study Design:

Case-control study; Level of evidence, 3.

Methods:

The authors used 3-dimensional motion analysis and physical data collected from 791 female elite handball and soccer players. Four common classifiers were used to predict ACL injuries (n = 60). Area under the receiver operating characteristic curve (AUC-ROC) averaged across 100 cross-validation runs (mean AUC-ROC) was used as a performance metric. Results were confirmed with repeated permutation tests (paired Wilcoxon signed-rank-test; P < .05). Additionally, the effect of the most common class imbalance handling techniques was evaluated.

Results:

For the best classifier (linear support vector machine), the mean AUC-ROC was 0.63. Regardless of the classifier, the results were significantly better than chance, confirming the predictive ability of the data and methods used. AUC-ROC values varied substantially across repetitions and methods (0.51-0.69). Class imbalance handling did not improve the results.

Conclusion:

The authors’ approach and data showed statistically significant predictive ability, indicating that there exists information in this prospective data set that may be valuable for understanding injury causation. However, the predictive ability remained low from the perspective of clinical assessment, suggesting that included variables cannot be used for ACL prediction in practice.

中文翻译：

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使用机器学习对 880 名女性精英运动员的广泛筛查测试组数据预测 ACL 损伤

背景：

伤害风险预测是​​一个新兴领域，需要更多的研究来识别准确的伤害风险评估的最佳实践。需要考虑与预测机器学习相关的重要问题，例如，避免过度解释观察到的预测性能。

目的：

仔细研究多种预测机器学习方法对前交叉韧带 (ACL) 损伤的大量风险因素数据的预测潜力；所提出的方法考虑了预测性能中机会和随机变化的影响。

学习规划：

病例对照研究；证据水平，3。

方法：

作者使用了从 791 名女性精英手球和足球运动员收集的 3 维运动分析和身体数据。四种常见的分类器用于预测 ACL 损伤（n = 60）。将 100 次交叉验证运行（平均 AUC-ROC）平均的接受者操作特征曲线下面积（AUC-ROC）用作性能指标。结果通过重复排列检验得到证实（配对 Wilcoxon 符号秩检验；P < .05）。此外，评估了最常见的类不平衡处理技术的效果。

结果：

对于最佳分类器（线性支持向量机），平均 AUC-ROC 为 0.63。无论分类器如何，结果都明显优于偶然性，证实了所用数据和方法的预测能力。AUC-ROC 值在重复次数和方法之间有很大差异 (0.51-0.69)。类不平衡处理并没有改善结果。

结论：

作者的方法和数据显示出具有统计学意义的预测能力，表明该前瞻性数据集中存在可能对了解伤害因果关系有价值的信息。然而，从临床评估的角度来看，预测能力仍然很低，这表明包含的变量在实践中不能用于 ACL 预测。