Geriatric Orthopaedic Surgery & Rehabilitation (Dec 2018)
The Circle-Fit Method Helps Make Reliable Cortical Thickness Measurements Regardless of Humeral Length
Abstract
Background: Although proximal humerus strength/quality can be assessed using cortical thickness measurements (eg, cortical index), there is no agreement where to make them. Tingart and coworkers used measurements where the proximal endosteum becomes parallel, while Mather and coworkers used measurements where the periosteum becomes parallel. The new circle-fit method (CFM) makes 2 metaphyseal (M1-M2) and 6 diaphyseal (D1-D6) measurements referenced from humeral head diameter (HHD). However, it is unknown whether these locations correlate to humeral length (HL). Accordingly, we asked: (1) Does HHD, Tingart distance, and Mather distance correlate with HL? (2) What is the location of HHD, Tingart distance, and Mather distance as a percentage of HL? and (3) Which CFM D1-D6 locations correlate with Tingart and Mather distances? Materials and Methods: Measurements made on cortical thickness (CT) scout views of 19 humeri (ages: 16-73 years) included HHD, distances from the superior aspect of the humerus to proximal Tingart and Mather locations, and HL. Results: Intraclass correlation was excellent for CFM-HHD, poor for Tingart, and moderate for Mather. The CFM-HHD had a stronger correlation to HL than Tingart and Mather. Mean HHD was 15.5% (0.9%) of HL while Tingart was 27.0% (4.1%) and Mather was 23.2% (3.8%). Tingart distance corresponded to D2/D3 CFM locations while the Mather distance was similar to D1/D2. Discussion: The CFM reliably correlates with HL and provides a stronger correlation and less variance between specimens than the Tingart or Mather Methods. Conclusions: Because the CFM produces reliable percent of HL locations, it should be used to define locations for obtaining biomechanically relevant CT measurements such as cortical index. Stronger correlations of these CFM-based measurements with proximal humerus strength will be important for developing advanced algorithms for fracture treatment.