Just How Confident Can We Be in Predicting Sports Injuries? A Systematic Review of the Methodological Conduct and Performance of Existing Musculoskeletal Injury Prediction Models in Sport,Sports Medicine

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Just How Confident Can We Be in Predicting Sports Injuries? A Systematic Review of the Methodological Conduct and Performance of Existing Musculoskeletal Injury Prediction Models in Sport
Sports Medicine ( IF 9.3 ) Pub Date : 2022-06-11 , DOI: 10.1007/s40279-022-01698-9
Garrett S Bullock _{1,

2} , Joseph Mylott _{1,

3} , Tom Hughes _{4,

5} , Kristen F Nicholson ₁ , Richard D Riley ₆ , Gary S Collins _{7,

8}

Affiliation

Background

An increasing number of musculoskeletal injury prediction models are being developed and implemented in sports medicine. Prediction model quality needs to be evaluated so clinicians can be informed of their potential usefulness.

Objective

To evaluate the methodological conduct and completeness of reporting of musculoskeletal injury prediction models in sport.

Methods

A systematic review was performed from inception to June 2021. Studies were included if they: (1) predicted sport injury; (2) used regression, machine learning, or deep learning models; (3) were written in English; (4) were peer reviewed.

Results

Thirty studies (204 models) were included; 60% of studies utilized only regression methods, 13% only machine learning, and 27% both regression and machine learning approaches. All studies developed a prediction model and no studies externally validated a prediction model. Two percent of models (7% of studies) were low risk of bias and 98% of models (93% of studies) were high or unclear risk of bias. Three studies (10%) performed an a priori sample size calculation; 14 (47%) performed internal validation. Nineteen studies (63%) reported discrimination and two (7%) reported calibration. Four studies (13%) reported model equations for statistical predictions and no machine learning studies reported code or hyperparameters.

Conclusion

Existing sport musculoskeletal injury prediction models were poorly developed and have a high risk of bias. No models could be recommended for use in practice. The majority of models were developed with small sample sizes, had inadequate assessment of model performance, and were poorly reported. To create clinically useful sports musculoskeletal injury prediction models, considerable improvements in methodology and reporting are urgently required.

中文翻译：

我们在预测运动损伤方面有多自信？对现有运动中肌肉骨骼损伤预测模型的方法论行为和性能的系统评价

背景

运动医学中正在开发和实施越来越多的肌肉骨骼损伤预测模型。需要评估预测模型的质量，以便临床医生了解其潜在用途。

客观的

评估运动中肌肉骨骼损伤预测模型报告的方法学行为和完整性。

方法

从开始到 2021 年 6 月进行了系统回顾。如果研究包括：（1）预测运动损伤；(2) 使用回归、机器学习或深度学习模型；(3) 以英文书写；(4) 进行同行评议。

结果

包括 30 项研究（204 个模型）；60% 的研究仅使用回归方法，13% 仅使用机器学习，27% 同时使用回归和机器学习方法。所有研究都开发了预测模型，没有研究从外部验证预测模型。2% 的模型（7% 的研究）存在低偏倚风险，98% 的模型（93% 的研究）存在高偏倚风险或不明确的偏倚风险。三项研究 (10%) 进行了先验样本量计算；14 (47%) 人进行了内部验证。十九项研究 (63%) 报告了歧视，两项 (7%) 报告了校准。四项研究 (13%) 报告了用于统计预测的模型方程，没有机器学习研究报告代码或超参数。