Classification of Forearm Involvement in Multiple Hereditary Exostoses

BACKGROUND: Existing classifications for forearm deformity in multiple hereditary exostoses (MHE) have demonstrated low interrater reliability and limited clinical applicability. Our primary research question was whether a Delphi consensus process could generate a more reliable and clinically useful classification for longitudinal surveillance of MHE-related forearm deformities. METHODS: Fifteen pediatric hand surgeons from the Congenital Upper Limb Difference (CoULD) Study Group participated in 7 rounds of online Delphi surveys (June 2022 to September 2023). An investigator team facilitated the process with anonymized feedback. Round 1 identified key clinical and radiographic features. Subsequent rounds refined these features, determined categorical structure, and piloted the system with representative radiographs. Consensus was defined as ≥70% agreement. After final consensus, interrater reliability was tested by 4 independent surgeons who classified 101 radiographs from 67 patients with forearm MHE. Reliability was assessed with kappa and concordance coefficients and compared with the Masada, Gottschalk, and Jo classifications. RESULTS: The Delphi process identified radiographic predictors that included ulnar variance, the radial articular angle (RAA), radial head alignment, and osteochondroma size. Consensus favored a 3-tier classification: Type 1, mild; Type 2, moderate; and Type 3, severe. Closing consensus was achieved, with 93% agreement. Reliability testing showed moderate agreement on the first reading (kappa = 0.55), significantly higher than for the Masada (kappa = 0.21 [original classification], kappa = 0.35 [modified version]), Gottschalk (kappa = 0.43), and Jo (kappa = 0.43) classifications. After refinement that incorporated osteochondroma size and the radial head subtypes (3A, subluxation, and 3B, dislocation), agreement improved to substantial (kappa = 0.64) (p < 0.001), with high concordance (0.82). CONCLUSIONS: The CoULD MHE Classification, developed through expert consensus, integrates validated radiographic predictors with defined thresholds. It demonstrated significantly improved reliability over prior systems and provides a practical framework for longitudinal monitoring of forearm deformities in patients with MHE. LEVEL OF EVIDENCE: Diagnostic Level V. See Instructions for Authors for a complete description of levels of evidence.

Authors

Institutions

Publication Details

Journal
Journal of Bone and Joint Surgery
Published
2026-09-15
DOI
https://doi.org/10.2106/jbjs.25.01593
Primary Topic
Bone Tumor Diagnosis and Treatments
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Classification of Forearm Involvement in Multiple Hereditary Exostoses

Ann E. Van Heest, Carley Vuillermin, Patricia E. Miller, Suzanne Steinman et al.
Journal of Bone and Joint Surgery
Bone Tumor Diagnosis and Treatments
article

Classification of Forearm Involvement in Multiple Hereditary Exostoses

Ann E. Van Heest, Carley Vuillermin, Patricia E. Miller, Suzanne Steinman, Maria F. Canizares, Lindley B. Wall, on behalf of the CoULD Study Group
article en

Abstract

BACKGROUND: Existing classifications for forearm deformity in multiple hereditary exostoses (MHE) have demonstrated low interrater reliability and limited clinical applicability. Our primary research question was whether a Delphi consensus process could generate a more reliable and clinically useful classification for longitudinal surveillance of MHE-related forearm deformities. METHODS: Fifteen pediatric hand surgeons from the Congenital Upper Limb Difference (CoULD) Study Group participated in 7 rounds of online Delphi surveys (June 2022 to September 2023). An investigator team facilitated the process with anonymized feedback. Round 1 identified key clinical and radiographic features. Subsequent rounds refined these features, determined categorical structure, and piloted the system with representative radiographs. Consensus was defined as ≥70% agreement. After final consensus, interrater reliability was tested by 4 independent surgeons who classified 101 radiographs from 67 patients with forearm MHE. Reliability was assessed with kappa and concordance coefficients and compared with the Masada, Gottschalk, and Jo classifications. RESULTS: The Delphi process identified radiographic predictors that included ulnar variance, the radial articular angle (RAA), radial head alignment, and osteochondroma size. Consensus favored a 3-tier classification: Type 1, mild; Type 2, moderate; and Type 3, severe. Closing consensus was achieved, with 93% agreement. Reliability testing showed moderate agreement on the first reading (kappa = 0.55), significantly higher than for the Masada (kappa = 0.21 [original classification], kappa = 0.35 [modified version]), Gottschalk (kappa = 0.43), and Jo (kappa = 0.43) classifications. After refinement that incorporated osteochondroma size and the radial head subtypes (3A, subluxation, and 3B, dislocation), agreement improved to substantial (kappa = 0.64) (p < 0.001), with high concordance (0.82). CONCLUSIONS: The CoULD MHE Classification, developed through expert consensus, integrates validated radiographic predictors with defined thresholds. It demonstrated significantly improved reliability over prior systems and provides a practical framework for longitudinal monitoring of forearm deformities in patients with MHE. LEVEL OF EVIDENCE: Diagnostic Level V. See Instructions for Authors for a complete description of levels of evidence.

Journal of Bone and Joint Surgery
Boston Children's Hospital (US), Seattle Children's Hospital (US), St. Louis Children's Hospital (US), Gillette Children's Specialty Healthcare (US), Boston Children's Museum (US), Shriners Hospitals for Children - St. Louis (US)
Quality Education
Openalex Percentile: Top 10%
Bone Tumor Diagnosis and Treatments
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.