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Journal of Biomechanics 46 (2013) 19001906Contents lists
available at SciVerse ScienceDirectjournal homepage:
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Journal of Biomechanics0021-92http://d
n Corrter ScieTel.: +61
E-mwww.JBiomech.comA large scale finite element study of a
cementless osseointegratedtibial tray
Francis Galloway a, Max Kahnt b, Heiko Rammb, Peter Worsley a,
Stefan Zachowb,Prasanth Nair c, Mark Taylor a,d,n
a Bioengineering Sciences Research Group, Faculty of Engineering
and the Environment, University of Southampton, UKb Medical
Planning Group, Zuse Institute Berlin (ZIB), Germanyc University of
Toronto Institute for Aerospace Studies, Toronto, Canadad Medical
Device Research Institute, School of Computer Science, Engineering
and Mathematics, Flinders University, Adelaide, Australiaa r t i c
l e i n f o
Article history:
Accepted 23 April 2013
The aim of this study was to investigate the performance of a
cementless osseointegrated tibial tray(P.F.C. s Sigmas, Depuys Inc,
USA) in a general population using finite element (FE)
analysis.Keywords:Population based studyFinite element
modellingCementless fixationTotal knee replacementAutomated
implantation90/$ - see front matter & 2013 Elsevier Ltd.
Ax.doi.org/10.1016/j.jbiomech.2013.04.021
esponding author at: Medical Device Researcnce, Engineering and
Mathematics, Flinders U8 8201 5732.
ail address: [email protected] (M. Ta b s t r a c
t
Computational testing of total knee replacements (TKRs)
typically only use a model of a single patientand assume the
results can be extrapolated to the general population. In this
study, two statistical models(SMs) were used; one of the shape and
elastic modulus of the tibia, and one of the tibiofemoral
jointloads over a gait cycle, to generate a population of FE
models. A method was developed to automaticallysize, position and
implant the tibial tray in each tibia, and 328 models were
successfully implanted andanalysed. The peak strain in the bone of
the resected surface was examined and the percentage surfacearea of
bone above yield strain (PSAY) was used to determine the risk of
failure of a model. Using anarbitrary threshold of 10% PSAY, the
models were divided into two groups (higher risk and lower risk)in
order to explore factors that may influence potential failure. In
this study, 17% of models were in thehigher risk group and it was
found that these models had a lower elastic modulus (mean 275.7
MPa), ahigher weight (mean 85.3 kg), and larger peak loads, of
which the axial force was the most significant.This study showed
the mean peak strain of the resected surface and PSAY were not
significantly differentbetween implant sizes.
& 2013 Elsevier Ltd. All rights reserved.1. Introduction
Due to the increasing number of total knee replacement
(TKR)procedures, assessment of TKR performance in the general
popu-lation is becoming more important. To evaluate the
performanceof a tibial tray, computational models are often used.
Many studiesonly use a model of a single patient (Keja et al.,
1994; Tissakhtet al., 1995; Taylor et al., 1998; Hashemi and
Shirazi-Adl, 2000;Barker et al., 2005; Perillo-Marcone and Taylor,
2007; Chong et al.,2010). A problem with such an approach is that
populationvariability is not taken into account and the results
cannot beapplied to the general population.
Studies using multiple patients have investigated tibial
trayperformance. Perillo-Marcone et al. (2004) modelled four
patients,ranking the models using percentage volume of bone at risk
offailure. The rank order matched the measured implant migrationll
rights reserved.
h Institute, School of Compu-niversity, Adelaide, Australia.
aylor).from radiostereometric analysis. Wong et al. (2010)
looked at thefactors influencing the risk of subsidence, modelling
four speci-mens in neutral and varus alignment. The volume of bone
at risk ofdamage was significantly higher for varus alignment,
despite thevariation among specimens. Rawlinson et al. (2005)
carried outexperimental tests and finite element (FE) analyses on
ninepaired-tibiae to compare stemmed and un-stemmed tibial
trays.From the FE analyses, it was seen that a stem reduced the
stressesand strains in the bone beneath the tibial tray. However,
due to thebiological variability between specimens, the
displacementbetween the bone and implant was highly variable and
the effectof the stem inconclusive. Despite the use of multiple
patientgeometries, the loading was limited to a single magnitude
for allspecimens.
Larger scale studies have focussed on the hip; two studies of
ahip resurfacing used 16 patient specific models to
investigatevarusvalgus alignment (Radcliffe and Taylor, 2007a) and
cement-ing technique (Radcliffe and Taylor, 2007b). A statistical
shape andintensity model (SSIM) of the femur (Bryan et al., 2010)
has beenused to analyse hip fracture risk (Bryan et al., 2009) and
influenceof head diameter of a hip resurfacing (Bryan et al.,
2012). Using the
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F. Galloway et al. / Journal of Biomechanics 46 (2013) 19001906
1901SSIM, variability of both the femur geometry and elastic
moduluswas captured and a large numbers of FE-ready meshes
repre-senting a population were easily generated.
Inter-patient variability has been observed in clinical
measure-ments of knee loads (Kutzner et al., 2010). However, in
moststudies variation of loading is not taken into account and a
fixedmagnitude static load is applied for all cases. Loads are
oftenscaled by body weight (Perillo-Marcone et al., 2004;
Rawlinsonet al., 2005; Perillo-Marcone and Taylor, 2007) which does
notrepresent the significant variation in the ratio of the load
compo-nents (e.g. anteriorposterior to axial force) known to
occurbetween subjects (Kutzner et al., 2010). To capture this
inter-patient variability, a statistical model (SM) has been used
togenerate a population of load cycles (Galloway et al., 2012).
In this study, the inter-patient variability of both the bone
andloading is considered to assess a cementless osseointegrated
tibialtray (P.F.C. s Sigmas, Depuys Inc, USA) in a large
population.The outcome of a TKR is dependent on many factors;
pre-operativefunction, surgical technique, fixation type, implant
design, and thephysical, emotional and social health of the patient
(Wylde et al.,2007). Cementless fixation is of interest as it is
thought to providelong-term fixation for younger more active
patients without theproblems associated with cement degradation
(Lombardi et al.,2007) and studies have reported good survivorship
rates of around95% after 10 years for cementless tibial trays
(Hofmann et al.,2001; Oliver et al., 2005; Baker et al., 2007;
Epinette and Manley,Fig. 1. Comparison of the internal joint
reaction forces from Orthoload (light) andmusculoskeletal data have
been scaled by 0.5. The heavy line represents the mean ofmoments
act in the directions defined in Fig. 4.2007). The objective of the
present work is to develop a metho-dology for carrying out
population based studies and to investigatefactors which increase
the failure risk of the tibial tray.
2. Methods
A SSIM of the complete tibia incorporating both geometry and
elastic modulusvariation was created using principal component
analysis (PCA), following themethod of Bryan et al. (2010) as
detailed in the Appendix A. A set of 32 leftcomputed tomography
(CT) scans of mixed resolution and an unknown demo-graphic were
used to train the SSIM. The full tibia from each CT scan was
semi-automatically segmented using Avizo (Visualization Sciences
Group, Bordeaux,France) and a tetrahedral mesh of each was
generated using Ansys ICEM CFD(Ansys Inc., PA, USA). The maximum
element size for the proximal and distalregions was set to 1.5 mm
and 5 mm, respectively. The baseline volume mesh,which consisted of
65,655 nodes and 337,205 tetrahedra, was then morphed to
eachtraining case in a two-step process, first through elastic
registration of the surfacemesh and then volumetric morphing of the
tetrahedral mesh. Having establishedcorrespondence between each
member of the training set, PCAwas then performed togenerate the
SSIM. The SSIM was then used to generate a large population of
tibiamodels, where each tibia model is described by a tetrahedral
mesh and associatedelement material properties, based upon the
smaller training population of tibiamodels. A population of 500
tibiae was generated by sampling the first 24 of 32 PCweights,
which explained 95% variance, assuming each had a normal
distributionwithmean and standard deviation s for each PC and
truncated to 73s. The generatedpopulation was considered to be
realistic in shape, size, and modulus distribution (seeAppendix
A).
To generate loading for each tibia, a SM of internal
tibiofemoral joint loads for asingle gait cycle (heel strike to
heel strike) was generated following Galloway et al.(2012). The
training data were taken from musculoskeletal models of 20
olderhealthy subjects (9 male, 11 female, age 5579) (Worsley et
al., 2011). The loadsmusculoskeletal models (dark). The AP force,
FE and VV moments of theeach component and the shaded area is 71
standard deviation. The forces and
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F. Galloway et al. / Journal of Biomechanics 46 (2013)
190019061902consisted of anteriorposterior (AP), mediallateral
(ML), and axial (AX) forcesand flexionextension (FE), varusvalgus
(VV), and internalexternal (IE) rota-tion moments, time normalised
from 0 to 100% gait (sampled at 1% intervals), andnormalised by
body weight. The AP force, FE moment, and VV moment werescaled by a
factor of 0.5 to make the peak magnitudes more comparable
totelemetric implant data (Fig. 1). A set of 500 gait cycles were
generated from the SMby sampling the first 13 of 20 PC weights,
which explained 95% variance, againassuming each had a normal
distribution with mean and standard deviation s foreach PC and
truncated to 73s. The sampled gait cycles were seen to be similar
inpattern to the original training data (Galloway et al.,
2012).
Each tibia was associated with one gait cycle. To compute the
actual loads itwas necessary to estimate the weight of each model.
Using the full leg CT scans,from which the training tibiae were
taken, a regression relationship was foundbetween tibia and femur
length. This was modified with a femurstature ratio(Feldesman and
Fountain, 1996) to predict model height. A BMI for each model
wassampled from a distribution based on NHANES data (NHANES, 1999)
and used withthe predicted height to calculate the mass.
The alignment of the tibia, tibial tray position, and tibial
tray implantation wereall performed automatically using ZIBAmira
2010.07-rc7 (Zuse Institute Berlin (ZIB),Berlin, Germany
http://amira.zib.de). Each tibia was aligned in a coordinatesystem,
such that +x is medial, +y is anterior, and +z is superior (Fig.
2), using thetransformation described by Fitzpatrick et al. (2007).
The tibia was resected 1.5 mmbelow the lowest point of the condyles
(Fig. 3a). The resected surface posteriorcondylar line (line
joining the most posterior medial and lateral points of theresected
surface) was used to determine the IE rotation angle (Moreland,
1988)and the ML width of the resected surface was measured to size
the tibial tray(Fig. 3b). The tibial tray was translated to align
with the centre of the ML width inML direction and the centre of
the resected surface in the AP direction (Fig. 3b).Models were
excluded from the study if the implant overhung the bone,
becausethe implant could impinge on a ligament and cause pain.
Mesh operations were performed to implant the tibial tray (Fig.
3c). The tibiawas (i) resected using mesh cutting operations, (ii)
merged with the tray geometryusing a mesh union operation, and
(iii) remeshed to ensure good element qualityFig. 2. Alignment of
the tibia to the global axis.(a) Inferior view and (b)
Medialview.
Fig. 3. Selected steps of implanting the tibial tray: (a) shows
the position of the cutting pland (c) is an exploded view of mesh
components (Tetra).(Zilske et al., 2008; Kahnt et al., 2011). A
first order tetrahedral mesh was generatedin ZIBAmira with element
sizes of 2 mm and 1.5 mm for the tibia and trayrespectively, and
the elastic modulus was interpolated to the new mesh. Thecombined
mesh of tibia and tray was imported into Abaqus 6.9 (Simulia, RI,
USA).The elastic modulus of the tibia was grouped into 10 MPa bands
and the tibial traywas modelled as cobalt chrome with a modulus of
210 GPa. The interface betweenthe tray and tibia was assumed to be
tied, simulating complete osseointegration.
All loads were assumed to act at the centre of the knee relative
to the tibia. Thethree forces, +FAP, +FML, and +FAX, act in the
medial (+x), anterior (+y), and superior(+z) directions (Fig. 4).
The three moments, +MFE, +MVV, and +MIE, act clockwise inthe median
(yz), frontal (xz), and horizontal (xy) planes (Fig. 4). The forces
andmoments were split across groups of nodes positioned medial,
lateral, anterior, andposterior relative to the centre of the
tibial tray (Table 1).
To assess the risk of failure, the equivalent strain (, referred
to as strain) wascomputed from the principal strains (1, 2, and
3),
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi122
232 312
2
s
Only the resected surface was considered in the analysis and
this was definedas the tibial bone directly in contact with the
tibial tray, excluding the stem of thetray, based on the assumption
that for the tibial tray to migrate or subside, theresection
surface must fail (Perillo-Marcone et al., 2004; Perillo-Marcone
andTaylor, 2007). Two metrics were computed for the resection
interface; thecomposite peak strain (CPS) which is the peak strain
of each element which occursduring the complete gait cycle, and the
percentage surface area with strain aboveyield (PSAY). The
compressive yield strain of trabecular bone was used and taken
as7300 microstrain (Morgan and Keaveny, 2001).
A mesh convergence study was performed using a single model with
theelement sizes ranging from 5 mm for the bone and 3 mm for the
tray (coarsest), to1.75 mm for bone and 1 mm for the tray (finest).
The mean CPS and PSAY changedby less than 5% between mesh
densities, and the distribution of both metrics wasane, (b) shows
the landmarks used on the resected surface to position the tibial
tray,
Fig. 4. The directions of the applied forces and node groups to
which loads areapplied of the FE model.
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Table 1Split of loads over the four groups of nodes on the
tibial tray. The fraction and direction of a load are given for the
positive direction. x, y, and z are the directions in which
theforce is applied. ra, rp, rm, and rl are the moment arms from
the centre to the anterior, posterior, medial, and lateral group of
nodes respectively.
Load Group of nodes
Medial Lateral Anterior Posterior
Anteriorposterior force (FAP) +0.5y +0.5yMediallateral force
(FML) +0.5x +0.5xAxial force (FAX) +0.5z +0.5zFlexionextension
moment (MFE) +0.5raz 0.5rpzVarusvalgus moment (MVV) 0.5raz
+0.5rpzInternalexternal rotation moment (MIE) +0.25rmy 0.25rly
0.25rax +0.25rpx
F. Galloway et al. / Journal of Biomechanics 46 (2013) 19001906
1903similar for all mesh densities. This was used as a guide to
select an appropriatemesh density because each model varies in
shape, size, and modulus distribution,and different loading was
applied. The choice of mesh density was a compromisebetween
solution time and solution accuracy.
To estimate the models at risk of failure, the models were split
into two groups,lower risk and higher risk, using a threshold of
10% PSAY. To visualise differencesbetween the two groups of models,
the correlation between CPS, PSAY, andmodulus was examined and the
resection surface for models representing a lowerrisk case, border
case (with a PSAY close to 10%), and higher risk cases
wasvisualised. A paired t-test with a Bonferroni correction factor
was performed to testif factors were significantly different
between the two groups (5%). The factorsconsidered were mean
elastic modulus of the resected surface, weight, BMI, andpeak
forces and moments in all directions (e.g. the peak anterior force
is themaximum of FAP and the peak posterior force is the minimum of
FAP). Further, aone-way ANOVA test was used to test if mean CPS and
PSAY were differentbetween the series of implant sizes (5%).Fig. 5.
Distribution of the mean CPS and 95th percentile CPS.
Fig. 6. Correlation between mean CPS, PSAY, and mean modulus of
the resectedsurface, with yield and ultimate strain of cancellous
bone marked. Three examplecases are highlighted, one lower risk
case, one border case, and one higherrisk case.3. Results
From the population of 500 tibiae, 328 (65.6%) were included
inthe study and 172 (34.4%) were not. Of the excluded models,
for134 (26.8%) the tibial tray was found to overhang the
resectedsurface. The implantation process failed for the remaining
38(7.4%) due to geometric limitations. FE analysis was
successfullycompleted for 328 models.
The distribution of the mean CPS shows that the majority
ofmodels (95.1%) were below the yield strain limit (Fig. 5). For
alarge proportion of models (70.7%) the 95th percentile CPS is
alsobelow yield strain (Fig. 5). The mean of the mean CPS for
allmodels is 2843 microstrain, below yield strain, with a range
of64817060 microstrain.
The mean CPS was correlated with PSAY, indicating the mod-ulus
of each model (Fig. 6). Using a threshold of 10% PSAY, 56models
(17.1%) were in the higher risk group with a minimummean CPS of
3510 microstrain and maximum of 17,060 micro-strain. The majority
of models in this group have a mean elasticmodulus of less than 400
MPa, with several models in the 400600 MPa range.
It was observed that the distribution of the CPS changed as
thePSAY increased. For models with a low PSAY (e.g. lower risk
caseFig. 7), higher strains were seen around the anterior and
posterioredges. As the PSAY increases, models around the 10%
threshold(e.g. border case Fig. 7) tended to have bone above yield
strainaround the periphery. The strains on the lateral side tended
to behigher in comparison to the medial side. In the higher risk
group,bone above yield tended to be distributed over the whole
resectedsurface (e.g. higher risk case Fig. 7), although in some
cases onlythe lateral side was above yield.
Using a paired t-test, only the mean modulus and peak
flexionmoment were found to be significantly different between
thelower risk and higher risk groups (Table 2). The one-way
ANOVAtest showed no significant difference of mean CPS or
PSAYbetween the series of implant sizes (p0.28 and
p0.45respectively).
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Fig. 7. Three example cases of the resected surface. Top is a
lower risk case (PSAY1.45%), middle is a border case (PSAY9.05%),
and bottom is a higher risk case(PSAY39.75%). Each is plotted with
the CPS (left), the point in the gait cycle at which the peak
strain occurs (middle), and modulus (right). These cases are
highlighted in Fig. 6.
F. Galloway et al. / Journal of Biomechanics 46 (2013)
1900190619044. Discussion
A large scale, multi-subject study was performed of a
cement-less osseointegrated tibial tray incorporating inter-patient
varia-bility of bone geometry and elastic modulus, and gait
cycleloading. The process was fully automated, allowing an FE
meshto be generated in 510 min using ZIBAmira and configured
usingAbaqus in a further 10 min. The solution and post-processing
timewas around 30 min per model. The system was designed such
thateach stage was automated and could run multiple
processesunattended.
In examining the strain of the proximal tibia during a gait
cycle,Perillo-Marcone and Taylor (2007) reported the PSAY of
theresected surface between 59 and 70% when using the
minimumprincipal strain as the failure criteria. In this study the
mean PSAYof the population was 6% (minimum 0%, maximum 83%).
Thedifference is likely because Perillo-Marcone and Taylor
(2007)modelled a 116 kg patient, whereas in this study the mean
weightof a model was 79 kg resulting in smaller applied loads.
However,the pattern of strain on the resected surface is
comparablebetween Perillo-Marcone and Taylor (2007) and this study;
higherstrains were seen around the anterior and lateral edges.
Thepattern of strain is most likely due to the difference in
modulusbetween the lateral and medial side of the resection surface
asseen in Fig. 7.
Models were determined to be at risk of failure using the
PSAYmetric. Loosening is the leading cause of tibial tray
failures(Sharkey et al., 2002; National Joint Registry for England
andWales, 2010), and it is reasoned that if a large proportion of
bonesupporting the tray fails because it experiences a high strain,
thetray could migrate and loosen. Using the 10% PSAY threshold,
theproportion of models in the higher risk group was 17.1%,
anoverestimation in comparison to the reported survivorship
rates;96.3% at 5 years for all cementless TKRs (National Joint
Registry forEngland and Wales, 2010) and 93.3% at 10 years for the
PFCimplant (Baker et al., 2007). This could be because the
chosenPSAY threshold is too conservative. Defining a more
accuratethreshold would require matched clinical data. The tray is
perhapsundersized to avoid overhangs between it and the bone,
resultingin the tray not being supported by stronger cortical bone
andincreasing the strain in the cancellous bone. Further to this,
boneremodelling was not simulated, which would affect the
straindistribution in the tibia. It is expected that models in this
studywould be at lower risk of failure because the tray was assumed
tobe fully osseointegrated, which takes time to occur in vivo.
In this study, two separate training sets were used to create
theSMs, consisting of 32 tibiae for the tibia SSIM and 20
healthysubjects for the gait cycle SM. These sets represent only a
smallproportion of the general population, and sampling the PC
weightsto 73s increases the chances of generating outlier cases.
How-ever, these cases are of interest, because they represent the
worst-case scenarios, important to consider in the assessment of a
TKR.
By using separate SMs of the tibia and gait cycle, the
two-wayinteraction between tibia morphology and loading, and
loadingand tibia strength are neglected. The weight of a model
(used tocompute the magnitude of the loads) was estimated from
thelength of the tibia and BMI. This provides a link between the
tibiamorphology and loading, but the BMI of a model was sampledfrom
an independent population and is not directly related to eachmodel.
There is a chance that models of a small tibia with a lowmodulus
could have an inappropriately high BMI, which might beconsidered
unrealistic and result in the model being classified ashigher risk.
The relationship between bone properties and load-ing are complex.
Tibial bone properties are known to be a functionof age, gender,
applied loads and activity levels. To better capturethe
interactions between the tibia, loading, and patient factors
(e.g.height, weight, BMI, age, gender), a data set of CT scans,
motioncapture data and patient information would be required for a
SM.
Comparing factors between the two groups (Table 2), it wasfound
that the higher risk group was subjected to larger peak
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Table 2The significance of factors between the lower risk and
higher risk groups for a PSAY threshold of 10%. Result was
significant po0.0036 with Bonferroni correction factor.
Factor P Lower risk, n272 Higher risk, n56
Min Mean Max Min Mean Max
Mean modulus*(MPa) 0.000 180.7 590.0 1169.0 106.0 275.7
526.5Peak flexion moment*(Nm) 0.002 5.3 19.5 60.8 5.8 22.8 40.3Peak
axial force (N) 0.004 5206.5 2588.3 1247.7 5622.4 2875.9
1456.2Model weight (kg) 0.005 37.8 77.9 149.2 52.8 85.3 138.7Model
BMI (kg/m2) 0.006 17.0 28.6 47.7 19.8 30.9 48.5Peak posterior force
(N) 0.007 843.4 353.1 101.9 778.4 401.5 147.5Peak extension moment
(Nm) 0.023 31.3 9.8 2.2 29.4 11.6 4.1Peak varus moment (Nm) 0.030
11.1 25.8 61.9 11.6 28.5 61.8Peak internal moment (Nm) 0.039 0.8
7.7 32.7 1.8 9.1 28.4Peak external moment (Nm) 0.047 21.7 5.7 2.2
18.0 6.7 0.1Peak medial force (N) 0.167 10.2 130.9 333.8 55.6 141.5
320.8Peak anterior force (N) 0.875 3.8 43.3 452.3 4.9 44.7
271.9Peak lateral force (N) 0.926 120.7 30.4 25.6 136.5 30.9
34.6Peak valgus moment (Nm) 0.550 23.9 2.5 1.9 17.0 2.8 1.3
F. Galloway et al. / Journal of Biomechanics 46 (2013) 19001906
1905loads, implying a greater contact force in the knee. The
magni-tudes of the loads applied are dependent on weight. In the
higherrisk group the mass of the models ranged from 52.8 kg to
138.7 kgand the BMI range was 19.848.5 kg/m2, not unrealistic for
apopulation. If the peak loads are normalised by weight, they
arecomparable between the lower risk and higher risk groups.Strain
of the resected surface is also dependent on the bonemodulus and a
two-fold difference in the mean modulus ofresection interface was
found between the lower risk and higherrisk groups (590.0 MPa
compared to 275.7 MPa respectively). Themodulus of cancellous bone
in the proximal tibia has beenmeasured to be around 500 MPa
(Keaveny et al., 2001), with upto an order of magnitude difference
within the proximal tibia(Goldstein et al., 1983). It was also seen
that the medial side of theresected interface was stronger than the
lateral side (examplemodels Fig. 7), a pattern found by Goldstein
et al. (1983).
Studies of multiple patients have suggested that the
variabilityof bone quality and loading affected the volume of bone
at risk ofdamage (Wong et al., 2010). The higher risk group had a
largerpeak varus moment, which results in a larger medial axial
force,suggesting varus alignment. Clinical studies have shown that
varusalignment increases the likelihood of revision (Ryd et al.,
1995;Fang et al., 2009). FE studies have also shown that with
varusalignment, a larger volume of bone is at risk of failure
(Perillo-Marcone et al., 2004; Wong et al., 2010). It has also been
reportedthat the combination of increased patient BMI (or mass) and
asmaller tibial component is associated with the failure of a
TKR(Berend et al., 2008). This is perhaps because the stress in the
tibiawas simply computed as the ratio of patient weight to tibial
trayarea, hence a larger mass and smaller implant will increase
stress.In this study, the higher risk group had a larger mass and
BMI, butthe mean CPS and PSAY were not significantly different
betweenthe series of implant sizes. The populations in each study
are alsodifferent, Berend et al., (2008) examined metal-backed and
all-polyethylene cemented tibial trays which had failed by
asepticloosening, whereas in this study an osseointegrated
uncementedtibial tray was modelled.
A limitation of the FE model is that loads are applied directly
tothe tibial tray assuming that the forces are evenly distributed
andthe tibiofemoral contact area is static. This is not the case in
vivo,where the tibiofemoral contact area moves as the knee flexes
andextends (Iwaki et al., 2002) and soft tissue constraints
influencethe load distribution.
Telemeterised TKRs provide in vivo measurement of the inter-nal
forces and moments in the knee, but given the observed
inter-patient variability of the telemetric data (Kutzner et al.,
2010), thefive available sets of data were not considered enough to
create aSM. For this study, the next best available data of
internal kneeloads were from musculoskeletal models of 20 healthy
subjectsand these were used to create the gait cycle SM. However,
theaccuracy of the predicted kinematics is limited by errors
andassumption inherent in the MS modelling process, e.g. soft
tissueartefacts, use of a generic linear scaling law, and the
assumptionthat the knee only has 1 degree-of-freedom (Schwartz et
al., 2010;Worsley, 2011). In this study, it was observed that the
MSmodelling and in vivo loads had a similar pattern but
themagnitudes were overestimated by the MS modelling. Therefore,the
loads seen to be most overestimated (the AP force, FEmoment, and VV
moment) were scaled by a factor of 0.5 to bringthem more in-line
with the in vivo data (Fig. 1).5. Conclusions
This study demonstrates the use of SMs in a population
basedstudy assessing the performance of a cementless
osseointegratedtibial tray. The process of positioning and
implanting a tibial traywas fully automated allowing fast
generation of a large number ofmodels. The study showed that the
higher risk models have alower mean resected surface modulus, and
higher model weightand peak loads; expected for an osseointegrated
tibial tray. Theadvantage of a population based study is that
inter-patientvariability incorporated in the pre-clinical testing,
and by includ-ing additional patient specific information in the
SM, populationbased studies can also potentially enable the
identification ofclinical factors that influence the performance of
a tibial traydesign.Conflicts of interest statement
Mark Taylor is a consultant to DePuy Orthopaedics Inc. None
ofthe other authors have any conflicts of
interest.Acknowledgements
This project was funded by DePuy (a Johnson &
Johnsoncompany), Engineering and Physical Sciences Research
Council(EPSRC). Parts of the research leading to these results
receivedfunding from the European Union Seventh Framework
Programme(FP7/2009-ICT) under grant agreement no. 248693.
-
F. Galloway et al. / Journal of Biomechanics 46 (2013)
190019061906Appendix A. Supporting information
Supplementary data associated with this article can be found
inthe online version at
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A large scale finite element study of a cementless
osseointegrated tibial
trayIntroductionMethodsResultsDiscussionConclusionsConflicts of
interest statementAcknowledgementsSupporting
informationReferences