Bullock, GS, Mylott, J, Hughes, T, Nicholson, KF, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735 and Collins, GS (2022) 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.

[img] Text
Sr_Sport_Prediction_10.27.21.docx - Accepted Version
Restricted to Repository staff only until 11 June 2023.
Available under License Creative Commons Attribution Non-commercial.

Download (471kB)

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.

Item Type: Article
Additional Information: The final version of this article and all relevant information related to it, including copyrights, can be found on the publisher website.
Subjects: R Medicine > R Medicine (General)
R Medicine > RC Internal medicine > RC925 Diseases of the musculoskeletal system
Divisions: Faculty of Medicine and Health Sciences > School of Medicine
Related URLs:
Depositing User: Symplectic
Date Deposited: 26 Jul 2022 14:30
Last Modified: 26 Jul 2022 14:30
URI: https://eprints.keele.ac.uk/id/eprint/11157

Actions (login required)

View Item View Item