Background
Meniscal injuries are common among amateur athletes and are a major cause of knee dysfunction and long-term joint degeneration. Current strategies for identifying athletes at risk rely mainly on isolated structural or functional factors and remain largely reactive. An integrative predictive approach combining multiple risk domains to guide individualized prevention is lacking.
Purpose
To develop and internally validate a multidomain predictive model for meniscal injury in amateur athletes and to derive a personalized prevention algorithm based on individual risk profiles.
Methods
A combined prospective-retrospective cohort study included 620 amateur athletes aged 15-50 years, followed for 24-36 months. Baseline assessment integrated five domains: osseous and meniscal morphology, functional biomechanics, training load and sport exposure, biological and lifestyle factors, and subjective symptoms. Predictor selection was performed using least absolute shrinkage and selection operator regression, followed by multivariable logistic regression. Model performance was evaluated using the area under the receiver operating characteristic curve, calibration analysis, and Decision Curve Analysis. A clinically interpretable risk score, the Meniscal Injury Risk Index, was derived to stratify athletes and guide prevention strategies.
Results
During follow-up, 92 athletes (14.8%) sustained a meniscal injury. Seven independent predictors were retained. The model demonstrated strong discrimination, excellent calibration, and consistent net clinical benefit. The risk score stratified athletes into four categories with progressively increasing injury incidence.
Conclusion
This multidomain model accurately identifies amateur athletes at risk for meniscal injury and provides a practical framework for personalized, risk-based prevention.
Level of evidence
II-III.
Study design
Prognostic cohort study.
