Externally Validated Machine Learning Algorithm Accurately Predicts Medial Tibial Stress Syndrome in Military Trainees; A Multi-Cohort Study.

Abstract

Objectives: Medial Tibial Stress Syndrome (MTSS) is a common musculoskeletal injury, both in sports and the military. There is no reliable treatment and reoccurrence rates are high. Prevention of MTSS is critical to reducing operational burden. Therefore, this study aimed to build a decision-making model to predict the individual risk of MTSS within officer cadets and test the external validity of the model on a separate military population. Design: Prospective cohort study. Methods: This study collected a suite of key variables previously established for predicting MTSS. Data was obtained from 107 cadets (34 females and 73 males). A follow-up survey was conducted at 3-months to determine MTSS diagnoses. Six ensemble learning algorithms were deployed and trained 5 times on random stratified samples of 75% of the dataset. The resultant algorithms were tested on the remaining 25% of the dataset and the models were compared for accuracy. The most accurate new algorithm was tested on an unrelated data sample of 123 Australian Navy recruits to establish external validity of the model. Results: Random Forest modelling was the most accurate in identifying a diagnosis of MTSS; (AUC = 98%). When the model was tested on an external dataset, it performed with an accuracy of 94% (F1= 0.88). Conclusion: This model is highly accurate in predicting those who will develop MTSS. The model provides important preventive capacity which should be trialled as a risk management intervention. 

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study did not receive any funding

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethics approval was obtained from The Departments of Defence and Veterans' Affairs (DDVA) Human Research Ethics Committee (HREC) (167-19), and the University of Canberra HREC (20193336)

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Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

Yes

Data Availability

All data produced in the present study are available upon reasonable request to the authors

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