Elise C Pegg, Hemant G Pandit, Harinderjit S Gill, David W Murray Tibial Fracture after Unicompartmental Knee Replacement: The Importance of Surgical Cut Accuracy Elise C Pegg, Hemant G Pandit, Harinderjit S Gill, David W Murray International Society for Biomechanics (ISB), 14th July 2015

Introduction 8.7 % of primary knee replacements performed are unicondylar knee replacement (UKR) [1] Reported incidence of periprosthetic tibial fracture: 0.8-5.0%. Can occur intra-operatively or post-operatively ~ 20% patients left to heal naturally ~ 12% cases treated intra-operatively ~ 68 % have secondary surgery Of those treated surgically, 25% require > 1 revision operation Fracture reported in all UKR designs, fixed and mobile Cause: unknown [1] National Joint Registry 11th Annual Report (2014) www.njrcentre.org.uk

Aims Determine the typical surgical cut positions and depths made during UKR Investigate the influence of cut position and depth on the risk of periprosthetic fracture using FEA

Sawbone measurements Cuts were more variable posteriorly In 14 or 23 tibias, the pin hole had gone into the keel slot (bimodal distribution) Calculated distributions were used as inputs for the Monte Carlo method to create representative FEA models Parameter Mean Standard Deviation a (resection depth, mm) 8.8 1.7 b (angle between cuts, deg) 90.6 1.4 c (wall to keel distance, mm) 8.5 0.7 d (vertical cut anterior, mm) 0.5 1.0 e (horizontal cut anterior, mm) 0.9 f (vertical cut posterior, mm) 4.2 3.9 g (horizontal cut posterior, mm) 1.3 2.1 h (pin depth, mm) 28.6 6.8

Finite Element Model 1000 FEA Monte Carlo models were created using ABAQUS One tibial geometry (used previously [2]) Bone cuts for cementless UKR automated using python scripting UKR tibial tray – rigid body, tied to tibia 400 material assignments for tibia based on CT scan Hounsfield units [2] (Mimics) Muscle and joint contact loads from instrumented TKR musculoskeletal model for gait (University of Florida) applied Risk of fracture (ROF) [3] calculated [2] HA Gray et al. (2008) J Biomech Eng 130 p031016 [3] E Schileo, et al. (2008) J Biomech 41 p356

Finite Element Model Mesh convergence study found an optimal overall element size of 2.4 mm with a 0.8 mm size for the cut surfaces A failed element = ROF > 1. Radius of failed region found ROF map correlated with typical fracture paths

Statistical Findings Linear regression model found the Posterior Vertical Cut and the Resection Depth to increase the ROF ROF = 0.04*rd + 0.03*vcp + 0.14 Where: rd = resection depth vcp = vertical cut posterior R2 = 0.76

Conclusions Limitations The cause of fracture is multifactorial For minimal risk of fracture a surgeon should: Ensure that the vertical cut does not go too deep posteriorly Be conservative with resection of the tibia Improvements in surgical instrumentation and training Limitations Patient factors such as size, gait and loading were not studied Load data was from a TKR instead of a UKR (does not exist) Component loosening was not simulated Cuts made in surgery may differ from Sawbones

Acknowledgements National Institutes of Health Dr Jonathan Walter (University of Melbourne) Dr Hans Grey (University of Melbourne) Prof Benjamin Fregly (University of Florida) Prof Darryl D’Lima (Scripps Research Institute) Mr Keith Thomas (Biomet)