We are offering a 12 months postdoc to work at Aix-Marseille Université on the estimation of the probability of developping chronic lung allograft dysfunctions (CLAD) ni lung transplantation patients. This study will be based on the COLT dataset of more than 1800 lung transplant patients in France.
Link to the full offer: https://www.lis-lab.fr/wp-content/uploads/2024/06/postdoc_CLAD.pdf
Project
Lung transplantation is the ultimate treatment for end-stage respiratory failure. However, 30% of lung transplant recipients develop chronic lung allograft dysfunction (CLAD) within 3 years, which, once diagnosed, results in a life expectancy of about 2 to 3 years. Moreover, there are fewer available grafts than needs. One way to improve this situation could be to better target recipients based on the probability of graft rejection. The medical question is therefore: ’Is it possible to estimate the probability of lung graft rejection in a given patient, the potential organ recipient, in order to optimize graft allocation?’
The aim of this project is to develop a tool to estimate the probability of the occurrence of CLAD. We will work with the COLT database of more than 1800 lung transplant patients across the national territory during the years 2010-2023, with follow-up for up to 10 years for most of them. The first step will be to model the ’center effect,’ that is, the biases related to the population pool managed by each transplant center. and its specific care characteristics. In a second step, a model for estimating the probability of individual rejection based on this center effect and, among other factors, on the time series associated with the clinical follow-up of each patient will be proposed. In particular, it will be necessary to account for and correct the impact of practice evolution during the cohort formation.
Ethical questions naturally arise on the legitimacy of having and using a tool that can contraindicate a potentially life-saving medical-surgical procedure (the transplant) for a patient otherwise doomed to short-term death in favor of another patient whose survival would be ensured. An ethical reflection will be conducted throughout the work.
Position details and candidate profile
We are looking for a person holding a PhD in mathematical modeling and/or machine learning/artificial intelligence, with a strong interest in applying these techniques in the healthcare field. Experience in the medical field would be appreciated but is not essential.
Required skills:
Application
Apply by email to the supervision team members, with [post-doc CLAD] as subject. Attach a CV, motivation letter (2 pages max), and two references (letters of recommendation are appreciated).
Application limit date : 30/09/2024
Supervision team:
Stéphane Delliaux, stephane.delliaux@univ-amu.fr
Raquel Urena, raquel.urena@univ-amu.fr
Paul Chauchat, paul.chauchat@lis-lab.fr
Christophe Gonzales, christophe.gonzales@lis-lab.fr
(c) GdR IASIS - CNRS - 2024.