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PhD position now open in the PhD Programme Health & Technology
Project title: In silico trials for predicting the risk of failure in joint replacements in patients with neurodegenerative co-morbidities
Among the risk factors associated to total knee replacements failure are gait abnormalities and instabilities, usually caused by the presence of neurodegenerative diseases, such as Parkinson’s Disease, or diabetic neuropathy.
In silico trials can be used to simulate movement disorder patterns, and also predict the risk of occurrence for most common failures modes and prevent the adverse outcomes. By combining these modelling efforts, it should be possible to estimate the increase in risk of failure associated to specific failure modes (massive wear, aseptic loosening, etc.) due to the presence of a neurodegenerative comorbidity.
The aim of this project is to develop patient-specific and disease-specific models that can systematically explore the effect of these comorbidities on the risk of failure of joint replacements, as a first step toward a more personalised management of these patients.
- Integration in In Silico Trials models to predict risk of massive wear, aseptic loosening, and other failure models, the functional overloading due to the progression of the neurodegenerative disease.
- Develop patient-specific neuromusculoskeletal dynamics models to predict the level of functional overloading associated to specific neurodegenerative diseases.
- Validation of the model’s predictions using clinical outcome as provided by the regional RIPO outcome registry (Registro dell’implantologia Protesica Ortopedica), and of the REPO retrievals collection, which includes more 3000 failed joint replacements.
Applicants must have:
– MSc degree (or equivalent 5-years degree) in biomedical engineering or mechanical engineering or in one the the following: engineering, physics, mathematics, computer science, chemistry, materials science or related disciplines
– Previous experience with finite element simulations and orthopaedic biomechanics would be an advantage
– Good written and spoken English
Prof. Marco Viceconti (first supervisor), Department of Industrial Engineering
Prof. Cesare Faldini, Department of Biomedical and Neuromotor Science – DIBINEM
Prof Viceconti group has worked over the years on the prediction of the risk of failure for joint replacements (Viceconti, 2004; Viceconti, 2012; Curreli, 2018). In parallel we have done extensive work on the prediction of the joint loading in suboptimal neuromuscular control (Martelli, 2011; van Veen, 2020).
Our involvement in the Mobilise-D project gave us exposure to some of the best research in Europe on the effect of Parkinson’s Disease on mobility.
There is extensive evidence that neurodegenerative diseases produce suboptimal neuromuscular control during ambulation. Our work showed that such suboptimal control almost always produces an increase in the forces being transmitted through the joint of the lower limbs. Considering that joint overloading is a major co-factor for various failure modes in joint replacements, it is reasonable to expect (and epidemiology seems to confirm) that such co-morbidities may have an adverse effect on the outcome of joint replacements.
Viceconti, 2004. https://doi.org/10.1007/bf02345207
Martelli, 2011. https://doi.org/10.1016/j.jbiomech.2011.03.039
Viceconti, 2012. https://doi.org/10.1016/j.medengphy.2011.07.006
Curreli, 2018. https://doi.org/10.1002/nme.5940
van Veen, 2020. https://doi.org/10.1109/tnsre.2020.3003559
Prof Viceconti team has developed, over the years, various patient-specific finite element models to simulate the most common failure modes for joint replacements. Also, in the Medical Technology Lab, the tribology and the epidemiology group have already published several important scientific results on the wear phenomena and the influencing factors on knee arthroplasty survival.
– Perform a retrospective analysis of data from the Register of Orthopaedic Prosthetic Implants (RIPO) and the Register of Explanted Prostheses of the Rizzoli Institute (REPO) and identify correlations between the most critical modes of failure for joint replacement and the presence of neuromuscular disease (NMD).
– Create a subcollection of retrieval knee implants from the REPO registry which corresponds to patients affected by NMDs.
– Extract anthropometric measurements of those patients and, when possible, radiographies or CT data.
– Extract from the literature quantitative information on the movement disorder patterns related to the main NMDs in terms of kinematic and dynamic response of the knee joints.
– Classify and quantitatively characterize the collected retrieval implants in terms of typical adverse outcome of the main failure modes and degenerative patterns (e.g., wear rate measurements in the polyethylene tibial insert).
– Create models for each of retrieval implant using reverse-engineering techniques to reconstruct the original prosthesis geometry and finite element (FE) techniques.
– Simulate the failure scenarios using as boundary conditions for the FE model, typical patient-pathology-specific kinematic and dynamic outputs.
– Validate the models by comparing the simulation results and the predicted joint replacement risk of failure associated to the pathology with the actual outcome for that implant.
The use of computer modelling and simulation is widely recognised today as a powerful tool in the era of precision medicine. FE predictive models can explore the risk of failure of specific implant design and patient specific loading condition with low cost and high level of accuracy.
This project will have a significant impact on the study of the effect of comorbidity on TKA outcomes and will contribute to promote the application of the in silico trial technology to the development of personalized joint replacements.
The project will be conducted at the Medical Technology Lab, within the Rizzoli Orthopaedic Institute. The lab also manages the RIPO and REPO registers, essential resources for this project, which collected data of nearly 100.000 joint replacements
Computational infrastructure of the In Silico Medicine team lead by Prof Viceconti.
No additional funding is available at the moment to support this scholarship.
However, all other costs associated to this research project, including costs for hardware, software, conferences attendance, etc. will be sustained through the funding of Prof Viceconti from the In Silico World project.
– Patient-specific models of Parkinson’s Disease patients – Prof. Dr. med. Walter Maetzler, Neurology Department, University of Kiel (DE)
– Movement analysis of Parkinson’s Patients – Prof Lynn Rochester, Brain and Movement Research Group, University of Newcastle (UK)