A diagnostic model integrating clinical, magnetic resonance imaging, and arthroscopic findings accurately identified hypermobile lateral meniscus: Development and internal validation

To develop and internally validate a diagnostic model for hypermobile lateral meniscus (HLM) integrating clinical symptoms, magnetic resonance imaging (MRI) findings, and dynamic arthroscopic assessment.

A retrospective diagnostic model study was performed, including 100 consecutive patients evaluated for suspected lateral meniscal pathology at a tertiary knee surgery center. All patients underwent standardized clinical examination, preoperative MRI, and arthroscopic assessment of lateral meniscal mobility, which served as the reference standard. Candidate predictors were restricted to preoperative clinical and MRI variables, including intermittent locking, posterolateral McMurray test, meniscocapsular gap, and suspected meniscotibial discontinuity. Arthroscopic findings were used exclusively to establish the reference-standard diagnosis and were not entered into the diagnostic model. A penalized logistic regression model with least absolute shrinkage and selection operator (LASSO) selection was used to identify independent predictors and construct the Hypermobile Meniscus Index (HMI). Internal validation was performed using bootstrap resampling, and robustness was assessed with Monte Carlo simulation.

HLM was confirmed arthroscopically in 47% of patients. The final model included five independent predictors and demonstrated excellent discriminative performance, with an area under the receiver operating characteristic curve (AUC) of 0.84 (95% confidence interval [CI], 0.83-0.95). At the optimal cut-off (≥5 points), sensitivity was 81%, specificity 79.2%, positive predictive value 77.6%, and negative predictive value 82.4%. Calibration analysis showed good agreement between predicted and observed probabilities (calibration slope 0.94, intercept 0.03; and Brier score 0.13). Bootstrap validation yielded an optimism-corrected AUC of 0.82. Monte Carlo simulation confirmed stability of model performance across varying prevalence scenarios, with consistent sensitivity and specificity ranges.

A diagnostic model combining clinical and MRI findings demonstrated internally validated performance for identifying HLM as defined by dynamic arthroscopy. The HMI provides a structured framework to improve preoperative recognition and guide targeted arthroscopic assessment in patients with mechanical lateral compartment symptoms and inconclusive imaging. External validation is required prior to routine clinical implementation.

Level III, diagnostic study.