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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

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Abstract

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.

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Authors and Affiliations

  1. Department of Orthopaedic Surgery, Wake Forest School of Medicine, 475 Vine St, Bowman Gray Medical Building, Winston-Salem, NC, 27101, USA

    Garrett S. Bullock, Joseph Mylott & Kristen F. Nicholson

  2. Centre for Sport, Exercise and Osteoarthritis Research Versus Arthritis, University of Oxford, Oxford, UK

    Garrett S. Bullock

  3. Baltimore Orioles Baseball Club, Baltimore, USA

    Joseph Mylott

  4. Manchester United Football Club, Manchester, UK

    Tom Hughes

  5. Department of Health Professions, Manchester Metropolitan University, Manchester, UK

    Tom Hughes

  6. Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK

    Richard D. Riley

  7. Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, UK

    Gary S. Collins

  8. Oxford University Hospitals NHS Foundation Trust, Oxford, UK

    Gary S. Collins

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  1. Garrett S. Bullock
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Correspondence to Garrett S. Bullock.

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Funding

GSC was supported by the NIHR Biomedical Research Centre, Oxford, and Cancer Research UK (programme Grant: C49297/A27294).

Conflict of interest

Garrett Bullock, Joseph Mylott, Tom Hughes, Kristen Nicholson, Richard Riley and Gary Collins declare that they have no conflicts of interest relevant to the content of this review.

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Extracted data are available through the Open Science Framework at https://osf.io/52mzn/.

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GSB, TH and GSC conceived the study idea. GSB, TH and GSC were involved in design and planning. GSB and GSC wrote the first draft of the manuscript. GSB, JM, TH, KFN, RDR and GSC critically revised the manuscript. GSB, JM, TH, KFN, RDR and GSC approved the final version of the manuscript.

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A series of symposiums are planned at various sports medicine conferences to help further educate clinicians on this topic.

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Bullock, G.S., Mylott, J., Hughes, T. et al. 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 Med 52, 2469–2482 (2022). https://doi.org/10.1007/s40279-022-01698-9

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