Crédits: Allogreffe d’aorte abdominale décellularisée, Inserm/Allaire, Eric
Prof. Alexander Loupy, Hospital Necker Children AP-HP and Prof. Carmen Lefaucheur, the Saint-Louis Hospital AP-HP and the University Paris Diderot in the Cardiovascular Research Center (Inserm / Paris Descartes University), showed, in an article published in the journal New England Journal of Medicine September 20, 2018, the latest advances and applications of artificial intelligence carried out in the field of transplantation, including the diagnosis and the treatment of allograft rejection.
These interdisciplinary work focused on transplant patients of heart, kidney and lung. They have helped to change the past five years, three times, the International Classification of rejection. They contribute to improving the management of transplant patients on diagnosis and treatment plans.
The transplantation has become the treatment of choice at the onset of organ impairment. 120,000 new organ transplants are performed each year worldwide, but only one million people living with a functioning graft. This finding can be explained by a lack of improvement in graft survival in recent decades and a number of available organs sometimes limited.
The rejection of the organ caused by the production of antibodies by the recipient patient is recognized as one of the main causes of failure of a transplant. A better understanding of the mechanisms of this rejection now allows to diagnose accurately and offer a personalized therapeutic approach.
A multidisciplinary approach involving clinical specialists, pathologists, immunologists transplantation, epidemiologist and statisticians, has been developed in close collaboration with Professor Xavier Jouven, head of the cardiology department of the European Hospital Georges Pompidou AP-HP and the team “cardiovascular Epidemiology and sudden death” of the cardiovascular research Center Inserm and Université Paris Descartes, to evaluate this rejection to the population level. New diagnostic categories were established and patient groups likely to lose their accelerated graft were identified and defined.
The allograft rejection may for example be detected by
> An integrative analysis of multiple biomarkers (reactive antibodies directed against the donor, inflammatory markers);
> A detailed study of the transplanted organ (identification of gene expression and characterization of cells infiltrating the graft that may cause rejection of the short or medium / long-term allograft).
Work by a team AP-HP / Inserm / Paris Descartes, and coordinated by Professor Alexander Loupy have thus demonstrated that the ultra-fine analysis of genes expressed by cells of the heart via a new technique called “molecular microscope “identifies precisely and patients with early beginnings of heart transplant rejection. (Read more: >> Diagnosis of transplant rejection in heart: a French team shows the interest of a new method, molecular microscope (March 2017) ). Other more recent studies have demonstrated the usefulness of algorithms to improve efficiency and performance of clinical trials in transplantation *.
Finally, the interest in this approach to artificial intelligence “machine learning” applied to transplantation was realized by obtaining two funding within the hospital research future investment program (RHU ) and the European program for research and innovation 2020.
This research thus open the way to a medicine of the future in which the mathematical algorithms will be used for daily monitoring of patients and the medical decision making. A concrete example is the development of a predictive tool for the survival of kidney transplants.
* Complement-Activating Anti-HLA Antibodies in Kidney Transplantation: Allograft Gene Expression Profiling and Response to Treatment. J Am Soc Nephrol. 2018 Feb; 29 (2): 620-635. doi: 10.1681 / ASN.2017050589. Epub 2017 Oct. 17.
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