In Vivo Estimation of Compressive Loads in the ACL-Reconstructed Knee

Abstract

It is not possible to measure joint compressive loads non-invasively in vivo. This is unfortunate because high compressive loads lead to articular cartilage failure and osteoarthritis. While gait analysis can be used to estimate net joint moments, it cannot be used to estimate compressive forces because high compressive forces can be found where net joint moments are minimal (and vice versa). Biomechanical models using optimization have been used to estimate joint compressive loads, but they do not account for the way different individuals use their muscles, which is a key determinant of joint compressive load. PURPOSE: To estimate joint compressive loads in vivo in unimpaired subjects and those with ACL reconstructions using biologically-driven data and to compare results associated with different autograft types. METHODS: The hybrid approach taken here uses both inverse dynamics and forward dynamics. Joint moments were determined from inverse dynamics and muscle forces were determined from EMGs recorded from ten muscles that cross the knee (Buchanan et al., MSSE 37:1911-1916, 2005). Joint compressive loads were calculated by resolving the joint and muscle forces into their compressive components and summing. Three subject groups were tested: unimpaired subjects, subjects who have had ACL reconstructed knees using bone-patellar tendon-bone (BPTB) autografts and those with quadruple-bundled semitendinosus-gracilis (QSTG) autografts. To date, two subjects in each group have been examined. RESULTS: Average peak joint compressive loads were found to be lower in ACL reconstructed subjects than unimpaired subjects: 10.8% lower for the QSTG autografts and 28.7% lower for the BPTB autografts. CONCLUSIONS: Joint compressive loads may be an important indicator of future degenerative joint disease. Although compressive forces in the different autograft groups were found to be smaller when compared to the unimpaired group, more subjects are needed for a statistically definitive analysis. Supported by NIH Grant R01-AR046386.