A New Classification System for Forearm Deformities Caused by Hereditary Multiple Osteochondromas

Area of Science:

• Orthopedics
• Genetics
• Radiology

Background:

• Hereditary multiple osteochondroma (HMO) frequently causes forearm deformities.
• Existing classification systems (Masada and Jo) are inadequate for clinical use, with a significant portion of HMO-related forearm deformities being unclassifiable.
• Accurate classification is crucial for guiding clinical management and predicting complications like radial head dislocation (RHD).

Purpose of the Study:

• To evaluate the clinical utility of the Masada and Jo classifications for forearm deformities in patients with HMO.
• To propose a novel, comprehensive classification system for HMO-related forearm deformities.
• To develop a system that effectively guides clinical management and risk stratification for RHD.

Main Methods:

• A retrospective review of 275 forearms affected by HMO was conducted.
• A split-sample approach was utilized: 138 forearms for developing a new classification and 137 for validation.
• Radiographic parameters, including osteochondroma characteristics and presence of RHD, were analyzed using multivariable logistic regression to identify predictors of RHD.

Main Results:

• The Masada and Jo classifications were found to be unclassifiable in 34.5% of cases (95/275 forearms).
• Distal ulna osteochondromas were identified as the primary factor associated with RHD (42/42 cases).
• Proportional ulna length was a significant predictor of RHD in forearms with distal ulna lesions, with an ideal cutoff of ≤0.95 (AUC 0.89, sensitivity 0.86, specificity 0.86).

Conclusions:

• A new classification system for HMO-related forearm deformity is proposed, stratifying patients into high, moderate, and low risk for RHD.
• Type 1 (distal ulna osteochondromas) is further divided into Type 1A (high-risk RHD) and Type 1B (moderate-risk RHD) based on 'at-risk' criteria.
• Type 2 encompasses forearms without distal ulna osteochondromas (low-risk RHD), addressing limitations of prior systems and aiding clinical decision-making.