基于场地的筛查能否预测女足运动员的ACL损伤风险？一项预测模型的外部验证
简介
该研究对一项基于场边测试的女性足球运动员ACL损伤预测模型进行了外部验证（n=320，随访18个月）。原始模型在独立队列中区分度下降（AUC 0.67）且校准不佳（风险高估），经更新后模型区分度改善（校正AUC 0.72），但个体风险预测仍不稳定。临床含义：场边力量与生物力学评估可用于群体ACL损伤风险分层（分类准确率72%），但尚不足以实现个体精准预测。
英文摘要
BACKGROUND: Anterior cruciate ligament (ACL) rupture is a devastating injury that occurs 3 to 7 times more frequently in women footballers than in their male counterparts. Identifying players at elevated risk is critical for targeted injury prevention; however, no ACL injury prediction model has undergone external validation in women. Therefore, this study aimed to externally validate a field-based ACL injury prediction model in women footballers. HYPOTHESIS: A previously developed field-based ACL injury prediction model would demonstrate good predictive performance in an independent cohort of women footballers. STUDY DESIGN: Prospective cohort study. LEVEL OF EVIDENCE: Level 2. METHODS: Women footballers (n = 320) completed preseason assessments of single-leg hop kinematics, countermovement jump (CMJ) kinetics, hip adductor/abductor strength, and self-reported injury history. Players were followed prospectively for 18 months for noncontact ACL injuries. Model performance was evaluated via discrimination (area under the curve [AUC]) and calibration (observed versus predicted risk). The model was updated subsequently using the combined development and validation cohorts (n = 642), incorporating the hip adductor/abductor strength ratio and ipsilateral trunk flexion angles. Internal validation was performed via bootstrapping. RESULTS: In the external validation cohort, the original model demonstrated reduced discrimination (AUC, 0.67; 95% CI, 0.49-0.82) and poor calibration (intercept, -0.79; slope, 0.53), indicating risk overestimation. The updated 5-variable model improved discrimination (apparent AUC, 0.76; optimism-corrected AUC, 0.72; 95% CI, 0.61-0.82), and calibration intercept (-0.00), but the calibration slope (1.38) and instability across bootstrapped samples indicated imprecise individual risk estimates. CONCLUSION: The original ACL injury prediction model did not generalize well to an independent cohort of women footballers. Although model updating improved overall predictive performance, calibration remained suboptimal, and predictions were unstable. Field-based strength and biomechanical assessments may support group-level ACL injury risk stratification but are not yet suitable for precise individual risk prediction. CLINICAL RELEVANCE: Field-based measures of strength and biomechanics can help identify groups of women footballers at elevated ACL injury risk, achieving 72% classification accuracy. These screening measures are practical and scalable, requiring <10 minutes per player, and assess modifiable factors that may inform targeted injury prevention strategies.