Tissue differentiation around a short stemmed metaphyseal loading implant employing a modified mechanoregulatory algorithm: A finite element study

Abstract

Short stemmed cementless implants are being used increasingly to avoid problems associated with their long stemmed counterparts such as size, stiffness, and bulky nature, which can contribute to stress shielding, fractures, and hence loosening. They are also thought to enhance physiological loading of the femur. We performed a computational investigation of the possible tissue differentiation and bone ingrowth processes for a specific type of stemless implant using a mechanoregulatory hypothesis, with modifications to simulate tissue differentiation, and simplified loading conditions. The peak forces during stair climbing and normal walking were investigated to evaluate their influence on the process. The results were compared to clinical studies for relevance and corroboration. The majority of the tissue type formed was fibrous, occupying the proximal regions of the implant. The lateral flare design feature of the implant was predicted to enhance bone and cartilage formation in regions beneath it compared to the same design without a flare. The percentage of bone formed increased through the iterations and accounted for nearly 35% of the tissue at the end of the iterations in Gruen zones 2 and 6, replacing cartilage tissue as differentiation progressed. This agreed well with clinical data showing similar regions of bone formation and suggests that the distal regions of the implant under the lateral flare, resting in the metaphyseal region of the bone, promoted implant stability. ß

Bone ingrowth around porous-coated acetabular implant: a three-dimensional finite element study using mechanoregulatory algorithm

“…fibrous tissue or cartilage or bone tissue) depends on the local mechanical stimuli, specifically the hydrostatic pressure and deviatoric strain (Claes and Heigele 1999;Dickinson et al 2012). Initially, the inter-bead spacing was assumed to be filled with granulation tissue (Liu and Niebur 2008;Dickinson et al 2012;Puthumanapully 2010;Puthumanapully and Browne 2011;Chou and Müftü 2013). A rule of mixture (Lacroix and Prendergast 2002a, b;Lacroix et al 2002) was implemented to calculate the effective material properties (E n+1 ) of the mixture of granulation tissue and newly formed tissue.…”

“…A sequential mechanoregulatory algorithm (Liu and Niebur 2008;Dickinson et al 2012;Puthumanapully and Browne 2011;Chou and Müftü 2013), as shown in Table 2, was employed to simulate tissue differentiation in inter-bead spacing elements of each microscale model separately. The following diffusion model (Lacroix and Prendergast 2002a, b) was implemented to simulate the migration and proliferation of mesenchymal stem cell (MSC):…”

“…Bone ingrowth around cementless porous-coated implants has been simulated employing the mechanoregulatory tissue differentiation algorithms (Liu and Niebur 2008;Checa and Prendergast 2009;Dickinson et al 2012;Puthumanapully and Browne 2011). A previous study by Isaksson et al (2006) indicated that none of these different mechanoregulatory algorithms could be rejected or determined as the superior one, since the differences in tissue differentiation predictions were not severe.…”

Four decades of finite element analysis of orthopaedic devices: Where are we now and what are the opportunities?

“…Although used extensively in the analysis of fracture healing Andreykiv et al, 2007;Isaksson et al, 2006), it has had limited application in the prediction of tissue differentiation around implants (Andreykiv et al, 2005;Puthumanapully and Browne, 2011;Gray et al, 2010). Various other approaches have also been implemented Claes and Heigele, 1999;Carter et al, 1988), but the methodology is limited by the data required to populate the algorithms, often coming from disparate sources in the literature (Isaksson et al, 2006).…”

“…Various other approaches have also been implemented Claes and Heigele, 1999;Carter et al, 1988), but the methodology is limited by the data required to populate the algorithms, often coming from disparate sources in the literature (Isaksson et al, 2006). Studies (Andreykiv et al, 2005;Puthumanapully and Browne, 2011;Gray et al, 2010) assume that the implant is surrounded by a layer of low modulus granulation tissue, whereas, in reality the implant is in direct contact with the supporting bone.…”