gms | German Medical Science

Deutscher Kongress für Orthopädie und Unfallchirurgie (DKOU 2015)

20.10. - 23.10.2015, Berlin

Intraoperative computer assisted prediction of lower limb alignment during high tibial osteotomy

Meeting Abstract

  • presenting/speaker Eduardo M. Suero - Medizinische Hochschule Hannover, Unfallchirurgische Klinik, Hannover, Germany
  • Ralf Westphal - TU Braunschweig, Institut für Robotik und Prozessinformatik, Braunschweig, Germany
  • Musa Citak - Medizinische Hochschule Hannover, Unfallchirurgie, Hannover, Germany
  • Nael Hawi - Medizinische Hochschule Hannover, Unfallchirurgische Klinik, Hannover, Germany
  • Emmanouil Liodakis - Medizinische Hochschule Hannover, Unfallchirurgische Klinik, Hannover, Germany
  • Christian Krettek - Medizinische Hochschule Hannover, Klinik für Unfallchirurgie, Hannover, Germany
  • Timo Stübig - Medizinische Hochschule Hannover, Unfallchirurgische Klinik, Hannover, Germany

Deutscher Kongress für Orthopädie und Unfallchirurgie (DKOU 2015). Berlin, 20.-23.10.2015. Düsseldorf: German Medical Science GMS Publishing House; 2015. DocPO26-1668

doi: 10.3205/15dkou776, urn:nbn:de:0183-15dkou7762

Veröffentlicht: 5. Oktober 2015

© 2015 Suero et al.
Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). Lizenz-Angaben siehe http://creativecommons.org/licenses/by/4.0/.


Gliederung

Text

Objectives: High tibial osteotomy (HTO) is a commonly used surgical technique for treating moderate osteoarthritis (OA) of the medial compartment of the knee by shifting the center of force towards the lateral compartment. The amount of alignment correction to be performed is usually calculated prior to surgery and it's based on the patient's lower limb alignment using long-leg radiographs. While the procedure is generally effective at relieving symptoms, an accurate estimation of change in intraarticular contact pressures and contact surface area has not been developed. We hypothesized that it would be possible to predict the change in intraarticular pressures based on extraarticular data acquisition.

Methods: Seven cadavers underwent an HTO procedure with sequential 5º valgus realignment of the leg up to 15º of correction. A previously developed stainless-steel device with integrated load cell was used to axially load the leg. Pressure-sensitive sensors were used to measure intraarticular contact pressures. Intraoperative changes in alignment were monitored in real time using computer navigation. An axial loading force was applied to the leg in the caudal-craneal direction and gradually ramped up from 0 to 550 N. Intraarticular contact pressure (kg) and contact area (mm2) data were collected. Generalized linear models were constructed to estimate the change in contact pressure based on extraarticular force and alignment data.

Results and Conclusion: The application of an axial load results in axial angle changes and load distribution changes inside the knee joint. Preliminary analysis has shown that it is possible to predict lateral and medial compartment pressures using externally acquired data. For lateral compartment pressure estimation, the following equation had an R2 of 0.86: Lateral compartment pressure = -1.26*axial_force + 37.08*horizontal_force - 2.40*vertical_force - 271.66*axial_torque - 32.64*horizontal_torque + 18.98*vertical_torque - 24.97*varusvalgus_angle_change + 86.68*anterecurvature_angle_change - 17.33*axial_angle_change - 26.14. For medial compartment pressure estimation, the following equation had an R2 of 0.86: Medial compartment pressure = -2.95*axial_force - 22.93*horizontal_force - 9.48*vertical_force - 34.53*axial_torque + 6.18*horizontal_torque - 127.00*vertical_torque - 110.10*varusvalgus_angle_change - 15.10*anterecurvature_angle_change + 55.00*axial_angle_change + 193.91.

We have established a framework for estimating the change in intraarticular contact pressures based on extraarticular, computer-navigated measurements. Quantifying the resulting changes in load distribution, alignment changes, torque generation and deflection will be essential for generating appropriate algorithms able to estimate joint alignment changes based on applied loads. Further research is currently underway to better understand the relationship between these variables and to construct a more accurate predictive model.