Introduction of the axial malalignment test: a classification system for torsional malalignment in the lower limb.
cross_sectional · Level IV
Where this comes from
- Record sourced from PubMed, PMID 41943937.
- Also identified by DOI 10.1097/BPB.0000000000001346.
- No licence information is recorded for this record.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
Axial alignment of the lower limbs affects foot progression angle, knee biomechanics, patellofemoral joint instability, and hip impingement, although a standardized method of reference has not yet been described. An understanding of torsional malalignment is critical in many subspecialty areas of orthopaedics including paediatric orthopaedics, deformity correction, sports medicine, hip and knee surgery, as well as trauma. This article aims to provide a reference system to objectively classify axial plane malalignment of the femur and tibia using the 'axial malalignment test' (AMAT). A cohort of patellofemoral dislocators and control subjects underwent bilateral lower limb MRI scans as part of a research protocol. Their femoral and tibial torsion were calculated on axial slices, and femoral torsion for each patient was plotted with respect to their tibial torsion. The resulting scatterplot was divided into a matrix based on normal values constructed from the dataset. Normal, simple, and complex torsional profiles were defined based on whether there was torsion greater than normal in neither, one, or both bones respectively. Measurements of foot progression angle were also compared in selected patients, with extension of the algorithm to include assessment of any soft tissue contribution to finalize descriptive and quantitative values for the AMAT. The AMAT offers a structured approach that incorporates both bony anatomy and soft tissue function to support systematic assessment of torsional deformities. Validation in a larger cohort is needed to confirm these preliminary findings, given the small sample size.