Can Field-Based Screening Predict ACL Injury Risk in Women Footballers? External Validation of a Prediction Model

Area of Science:

• Sports Medicine
• Biomechanics
• Injury Epidemiology

Background:

• Anterior cruciate ligament (ACL) ruptures are significantly more common in female football players.
• Effective ACL injury prediction models are crucial for targeted prevention strategies in women's football.
• No existing field-based ACL injury prediction models have been externally validated in female football players.

Purpose of the Study:

• To externally validate a previously developed field-based ACL injury prediction model in an independent cohort of women footballers.
• To assess the model's predictive performance using discrimination and calibration metrics.
• To update the model incorporating additional relevant biomechanical and strength measures.

Main Methods:

• Prospective cohort study involving 320 women footballers.
• Preseason assessments included single-leg hop kinematics, countermovement jump (CMJ) kinetics, hip strength, and injury history.
• Players were monitored for 18 months for non-contact ACL injuries; model performance was evaluated, and the model was subsequently updated and internally validated.

Main Results:

• The original model exhibited poor predictive performance (AUC=0.67) and calibration in the external validation cohort.
• An updated 5-variable model demonstrated improved discrimination (optimism-corrected AUC=0.72) and calibration intercept.
• However, the updated model's calibration slope remained suboptimal, and predictions were unstable across validation samples.

Conclusions:

• The original ACL injury prediction model did not generalize effectively to women footballers.
• Model updating enhanced group-level risk stratification but did not yield precise individual risk predictions.
• Field-based strength and biomechanical assessments are practical for identifying groups at elevated ACL injury risk.