A study protocol for the development and internal validation of a multivariable prognostic model to determine lower extremity muscle injury risk in elite football (soccer) players, with further exploration of prognostic factors

Abstract

Background: Indirect muscle injuries (IMIs) are a considerable burden to elite football (soccer) teams, and prevention of these injuries offers many benefits. Preseason medical, musculoskeletal and performance screening (termed periodic health examination (PHE)) can be used to help determine players at risk of injuries such as IMIs, where identification of PHE-derived prognostic factors (PF) may inform IMI prevention strategies. Furthermore, using several PFs in combination within a multivariable prognostic model may allow individualised IMI risk estimation and specific targeting of prevention strategies, based upon an individual's PF profile. No such models have been developed in elite football and the current IMI prognostic factor evidence is limited. This study aims to (1) develop and internally validate a prognostic model for individualised IMI risk prediction within a season in elite footballers, using the extent of the prognostic evidence and clinical reasoning; and (2) explore potential PHE-derived PFs associated with IMI outcomes in elite footballers, using available PHE data from a professional team. Methods: This is a protocol for a retrospective cohort study. PHE and injury data were routinely collected over 5 seasons (1 July 2013 to 19 May 2018), from a population of elite male players aged 16-40 years old. Of 60 candidate PFs, 15 were excluded. Twelve variables (derived from 10 PFs) will be included in model development that were identified from a systematic review, missing data assessment, measurement reliability evaluation and clinical reasoning. A full multivariable logistic regression model will be fitted, to ensure adjustment before backward elimination. The performance and internal validation of the model will be assessed. The remaining 35 candidate PFs are eligible for further exploration, using univariable logistic regression to obtain unadjusted risk estimates. Exploratory PFs will also be incorporated into multivariable logistic regression models to determine risk estimates whilst adjusting for age, height and body weight.

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

“…The methods have been described in a published protocol [20] so will only be briefly outlined. This study has been registered on ClinicalTrials.gov (identifier: NCT03782389) and is reported according to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement [21,22].…”

“…We allowed a maximum of one candidate prognostic factor parameter per 10 I-IMIs, which at the time of protocol development, was the main recommendation to minimise overfitting (Additional file 1) [20,26]. The whole dataset was used for model development and internal validation, which agrees with methodological recommendations [27].…”

“…The available dataset contained 60 candidate factors [20]. Because of the sample size considerations, before any analysis, the set of candidate factors was reduced.…”

“…Because of the sample size considerations, before any analysis, the set of candidate factors was reduced. Initially, an audit was conducted to quantify missing values and to determine the measurement reliability of the eligible candidate factors [20]. Any candidate factors which had greater than 15% missing data or where reliability was classed as fair to poor (intraclass correlation [20] (Additional file 2).…”

Pre-season screening currently has no value for injury prediction: The development and internal validation of a multivariable prognostic model to predict indirect muscle injury risk in elite football (soccer) players

“…Missing values were assumed to be missing at random [20]. The continuous parameters generally demonstrated non-normal distributions, so were transformed using normal scores [34] to approximate normality before imputation, and back-transformed following imputation [35].…”