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SENSITIVITY PREDICTED OF KNEE IMPLANT WEAR TO INSERT THICKNESS AND BODY MASS *Bei, Y; +*Fregly, B J; *Sawyer, W G; **Banks, S A +*University of Florida, Gainesville, FL INTRODUCTION: Mild wear continues to contribute to the failure of total knee replacements. Numerous factors affect knee implant wear, including polyethylene material properties, implant geometry, implant alignment, insert thickness, and patient body mass. These factors influence wear through kinematics and contact pressures. Numerous studies have investigated knee implant contact pressures, with increasing peak and average pressures presumed to correlate with increasing wear. Modeling studies of contact pressures have suggested a minimum insert thickness of about 8 mm to reduce wear-related problems. Clinicians also recommend that overweight knee implant patients lose weight to maximize the longevity of the implant. Since wear is due to a combination of contact pressures and kinematics, contact pressure analyses only tell part of the story. The contribution of kinematics to wear is more difficult to assess since sliding conditions at individual locations on the insert surface are difficult to measure. This study uses a validated computational model to predict mild wear as a function of tibial insert thickness and patient body mass, with the goal of assessing the relative sensitivity to each factor. METHODS: A previously validated computational wear model was used to make the damage predictions using a three-step process. First, single-plane fluoroscopy was used to measure in vivo knee implant kinematics (Fig. 1a). Treadmill gait and stair data were collected from a single patient (female, age 65 at surgery, height 170 cm, mass 70 kg) 21 months after surgery. The patient received an Osteonics 7000 cruciate-retaining implant (Stryker Howmedica Osteonics, Inc, Allendale, NJ) with a minimum insert thickness of 6.8 mm. IRB approval was obtained prior to the study and the patient gave written informed consent. predictions from the model (Fig. 2a) with the tibial insert retrieved postmortem from the same patient who provided the in vivo kinematics (Fig. 2b). The good qualitative and quantitative agreement (predicted maximum damage within 0.1 mm of retrieval) suggested that the computational model is able to provide reasonable predictions of insert damage under neighboring conditions. Starting from this simulation, damage was predicted for 25 neighboring combinations of minimum insert thickness (6, 8, 10, 12, and 14 mm) and body mass (50, 75, 100, 125, and 150 kg). Only gait data were analyzed, the prescribed kinematics were unaltered, and the axial load was rescaled based on each new body mass. To assess sensitivity to insert thickness and body mass, the predicted values of wear, creep, and total damage depth, along with total damage area and volume, were fit via multiple linear regression using Prediction= a 0 + a1 Insert Thickness + a 2 Body Mass . RESULTS: The multiple linear regression model fitted the predicted quantities extremely well, with all R 2 values close to 0.98 (Tables 1 & 2). Increasing insert thickness decreased all predicted quantities except for damage area ( a1 columns mostly negative), since a thicker insert corresponds to a softer foundation. In contrast, increasing body mass increased predicted all quantities ( a 2 columns all positive). Table 1: Regression coefficients for medial damage predictions. Prediction Wear depth (mm) Creep depth (mm) Damage depth (mm) Damage area ( mm 2 ) Damage volume ( mm 3 ) a0 a1 a2 R2 0.307 0.467 0.767 272.6 38.52 -0.015 -0.022 -0.037 6.380 -2.032 0.004 0.003 0.007 0.736 1.407 0.990 0.975 0.987 0.988 0.999 Table 2: Regression coefficients for lateral damage predictions. Figure 1: Process used to develop computational wear predictions. Second, the fluoroscopic kinematics were used to drive a multibody dynamic contact model, where the femoral component and tibial insert were treated as rigid bodies possessing deformable contact surfaces. Contact pressures were calculated using an elastic foundation model with linear material properties. The fluoroscopic kinematics were used to prescribe flexion/extension, internal/external rotation, and anterior/ posterior translation. The other degrees of freedom were predicted via forward dynamic simulation. An off-center vertical load was applied to the femoral component to produce a 70% medial, 30% lateral load split, where a vertical ground reaction force curve scaled to be between 0.25 and 3 times body weight defined the load magnitude. Prediction Wear depth (mm) Creep depth (mm) Damage depth (mm) Damage area ( mm 2 ) Damage volume ( mm 3 ) a0 a1 a2 R2 0.458 0.271 0.730 238.5 18.70 -0.023 -0.014 -0.036 5.450 -0.945 0.003 0.002 0.005 0.625 0.605 0.982 0.983 0.982 0.984 0.998 a x x b x x 0.90 By dividing the a 2 column by the a1 column, one can predict how losing body mass compares to increasing insert thickness. For wear depth, each 10 kg of body fat lost is equivalent to gaining 1 to 2 mm of insert thickness. Similar trends exist for other quantities with the exception of total damage volume, where 6 to 7 mm of effective insert thickness is gained for each 10 kg lost. DISCUSSION: Even though the R 2 values were high, 3D surface plots revealed a slight quadratic trend with each predicted variable. This explains why the a 0 column, which is the predicted value with zero insert thickness and body mass, is not closer to zero. Other factors, such as the assumptions of invariant prescribed kinematics and activity level with changes in body mass, may also influence the predictions. Overall, these simulations highlight the importance of losing excess body fat for knee implant recipients. The results suggest that each 10 kg increase in body mass is roughly equivalent to a 1 to 2 mm decrease in insert thickness, which can have significant tribological consequences. AFFILIATED INSTITUTIONS FOR CO-AUTHORS: ** The Biomotion Foundation, West Palm Beach, FL Medial Lateral Medial Lateral 0.25 Figure 2: Comparison of a) insert damage predicted by computational model, and b) insert damage observed upon retrieval. X indicates locations of maximum damage. Third, a computational damage model was used to predict wear, creep, and total damage (= wear + creep) from the predicted contact pressure and slip velocity time histories. The wear model used Archard s wear law, while the creep model was derived from data in the literature. The damage prediction process was evaluated by comparing damage 50th Annual Meeting of the Orthopaedic Research Society Poster No: 1465

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