Computer simulating a trial of a load-bearing implant: example of an intramedullary prosthesis

Computational modelling is becoming ever more important for obtaining regulatory approval for new medical devices. An accepted approach is to infer performance in a population from an analysis conducted for an idealised or ‘average’ patient; we present here a method for predicting the performance of an orthopaedic implant when released into a population—effectively simulating a clinical trial. Specifically we hypothesise that an analysis based on a method for predicting the performance in a population will lead to different conclusions than an analysis based on an idealised or ‘average’ patient. To test this hypothesis we use a finite element model of an intramedullary implant in a bone whose size and remodelling activity is different for each individual in the population. We compare the performance of a low Young’s modulus implant (View the MathML source) to one with a higher Young’s modulus (200 GPa). Cyclic loading is applied and failure is assumed when the migration of the implant relative to the bone exceeds a threshold magnitude. The analysis for an idealised of ‘average’ patient predicts that the lower modulus device survives longer whereas the analysis simulating a clinical trial predicts no statistically-significant tendency (p=0.77) for the low modulus device to perform better. It is concluded that population-based simulations of implant performance–simulating a clinical trial–present a very valuable opportunity for more realistic computational pre-clinical testing of medical devices.

A probabilistic modelling scheme for analysis of long-term failure of cemented femoral joint replacements

Reliable prediction of long-term medical device performance using computer simulation requires consideration of variability in surgical procedure, as well as patient-specific factors. However, even deterministic simulation of long-term failure processes for such devices is time and resource consuming so that including variability can lead to excessive time to achieve useful predictions. This study investigates the use of an accelerated probabilistic framework for predicting the likely performance envelope of a device and applies it to femoral prosthesis loosening in cemented hip arthroplasty.
A creep and fatigue damage failure model for bone cement, in conjunction with an interfacial fatigue model for the implant–cement interface, was used to simulate loosening of a prosthesis within a cement mantle. A deterministic set of trial simulations was used to account for variability of a set of surgical and patient factors, and a response surface method was used to perform and accelerate a Monte Carlo simulation to achieve an estimate of the likely range of prosthesis loosening. The proposed framework was used to conceptually investigate the influence of prosthesis selection and surgical placement on prosthesis migration.
Results demonstrate that the response surface method is capable of dramatically reducing the time to achieve convergence in mean and variance of predicted response variables. A critical requirement for realistic predictions is the size and quality of the initial training dataset used to generate the response surface and further work is required to determine the recommendations for a minimum number of initial trials. Results of this conceptual application predicted that loosening was sensitive to the implant size and femoral width. Furthermore, different rankings of implant performance were predicted when only individual simulations (e.g. an average condition) were used to rank implants, compared with when stochastic simulations were used. In conclusion, the proposed framework provides a viable approach to predicting realistic ranges of loosening behaviour for orthopaedic implants in reduced timeframes compared with conventional Monte Carlo simulations.

Late referral for assessment of renal failure

It has been recommended that adult patients with a serum creatinine above 150 µmol/l should be referred to a nephrologist for specialist assessment. This study ascertained all patients in Northern Ireland with creatinine above this concentration in 2001 (n?=?19 286 ) to see if this triggered referral within the subsequent year. After exclusion of those who were already known to a nephrologist and those who had acute renal failure, it was found that younger patients and diabetic patients were more likely to be referred. There was no difference in referral rates between male and female patients. However, only 6.5% of all non-diabetic subjects and 19% of diabetic patients were referred within 12 months after a first increased serum creatinine test.