Predictive mathematical modeling of knee static laxity after ACL reconstruction: in vivo analysis. Issue 15 (17th November 2016)

• Record Type: Journal Article
• Authors: Signorelli, C.; Bonanzinga, T.; Grassi, A.; Lopomo, N.; Zaffagnini, S.; Marcacci, M.
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 19:Issue 15(2016)
• Page Start: 1610
• Page End: 1617
• Publication Date: 2016-11-17
• Subjects: Static laxity -- surgical variables -- modeling -- ACL reconstruction -- surgery outcome; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; Biomechanics -- Periodicals; Biomedical Engineering -- methods -- Periodicals; Computing Methodologies -- Periodicals; 612.7
• DOI: 10.1080/10255842.2016.1176152
• Languages: English
• ISSNs: 1025-5842

Abstract

Abstract: Previous studies did not take into consideration such large variety of surgery variables which describe the performed anterior cruciate ligament (ACL) reconstruction and the interaction among them in the definition of postoperative outcome. Seventeen patients who underwent navigated Single Bundle plus Lateral Plasty ACL reconstruction were enrolled in the study. Static laxity was evaluated as the value of anterior/posterior displacement at 30° and at 90° of flexion, internal/external rotation at 30° and 90° of knee flexion, varus/valgus test at 0° and 30° of flexion. The evaluated surgical variables were analyzed through a multivariate analysis defining the following models: AP30estimate, AP90estimate, IE30estimate, IE90estimate, VV0estimate, VV30estimate. Surgical variables has been defined as the angles between the tibial tunnel and the three planes, the lengths of the tunnel and the relationship between native footprints and tunnels. An analogous characterization was performed for the femoral side. Performance and significance of the defined models have been quantified by the correlation ratio ( η 2 ) and the corresponding p -value (* p < 0.050). The analyzed models resulted to be statistically significant ( p < 0.05) for prediction of postoperative static laxity values. The only exception was the AP90estimate model. The η 2 ranged from 0.568 (IE90estimate ) to 0.995 (IE30estimate ). The orientation of the tibial tunnel resulted to be the most important surgical variable for the performed laxity estimation. Mathematical models for postoperative knee laxity is a useful tool to evaluate the effects of different surgical variables on the postoperative outcome.