1. The Value of Preseason Screening for Injury Prediction: The Development and Internal Validation of a Multivariable Prognostic Model to Predict Indirect Muscle Injury Risk in Elite Football (Soccer) Players.
- Author
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Hughes, Tom, Riley, Richard D., Callaghan, Michael J., and Sergeant, Jamie C.
- Subjects
MUSCLE injuries ,FORECASTING ,PERIODIC health examinations ,AKAIKE information criterion ,INJURY risk factors ,LEG injuries ,SKELETAL muscle injuries ,SOCCER injuries ,CONFIDENCE intervals ,RESEARCH evaluation ,RANGE of motion of joints ,MEDICAL examinations of athletes ,MULTIPLE regression analysis ,CALIBRATION ,AGE distribution ,DISCRIMINANT analysis ,RETROSPECTIVE studies ,EPIDEMIOLOGY ,ACQUISITION of data ,RISK assessment ,COMPARATIVE studies ,RESEARCH funding ,DESCRIPTIVE statistics ,INTRACLASS correlation ,MUSCLE strength ,ROTATIONAL motion ,MEDICAL records ,PREDICTION models ,PREDICTIVE validity ,RECEIVER operating characteristic curves ,SENSITIVITY & specificity (Statistics) ,WOUNDS & injuries ,BODY mass index ,LONGITUDINAL method ,ALGORITHMS ,DISEASE risk factors ,EVALUATION - Abstract
Background: In elite football (soccer), periodic health examination (PHE) could provide prognostic factors to predict injury risk. Objective: To develop and internally validate a prognostic model to predict individualised indirect (non-contact) muscle injury (IMI) risk during a season in elite footballers, only using PHE-derived candidate prognostic factors. Methods: Routinely collected preseason PHE and injury data were used from 152 players over 5 seasons (1st July 2013 to 19th May 2018). Ten candidate prognostic factors (12 parameters) were included in model development. Multiple imputation was used to handle missing values. The outcome was any time-loss, index indirect muscle injury (I-IMI) affecting the lower extremity. A full logistic regression model was fitted, and a parsimonious model developed using backward-selection to remove factors that exceeded a threshold that was equivalent to Akaike's Information Criterion (alpha 0.157). Predictive performance was assessed through calibration, discrimination and decision-curve analysis, averaged across all imputed datasets. The model was internally validated using bootstrapping and adjusted for overfitting. Results: During 317 participant-seasons, 138 I-IMIs were recorded. The parsimonious model included only age and frequency of previous IMIs; apparent calibration was perfect, but discrimination was modest (C-index = 0.641, 95% confidence interval (CI) = 0.580 to 0.703), with clinical utility evident between risk thresholds of 37–71%. After validation and overfitting adjustment, performance deteriorated (C-index = 0.589 (95% CI = 0.528 to 0.651); calibration-in-the-large = − 0.009 (95% CI = − 0.239 to 0.239); calibration slope = 0.718 (95% CI = 0.275 to 1.161)). Conclusion: The selected PHE data were insufficient prognostic factors from which to develop a useful model for predicting IMI risk in elite footballers. Further research should prioritise identifying novel prognostic factors to improve future risk prediction models in this field. Trial registration: NCT03782389 [ABSTRACT FROM AUTHOR]
- Published
- 2020
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