The first universal algorithm for predicting the risk of kidney transplant loss, named iBox, has been developed, validated and just made public by teams from Europe and the US
iBox is designed for clinicians to personalize and improve patient follow-up. iBox could also accelerate the development of new immunosuppressive treatments by reducing the duration of clinical trials and defining a valid surrogate endpoint
PARIS, Sept. 21, 2019 /PRNewswire/ -- Chronic kidney disease affects 1 out of 10 people worldwide and is steadily increasing. When it reaches end-stage renal disease and endangers the lives of patients, dialysis or transplantation is required. Renal transplantation is the treatment of choice, offering a better quality of life and survival chances to patients. Unfortunately, due to the lack of available organs, approximately 55% of patients with end-stage renal disease are still treated by dialysis, representing an annual cost of €2.6 billion in France and more than $40 billion in the USA.
The survival of kidney transplants is therefore crucial. As emphasized by regulatory agencies including the U.S. Food & Drug Administration and the European Medicines Agency, there is a substantial need to develop a robust tool that can predict long-term graft survival. Indeed, over the past 20 years, long-term graft survival did not improve. Therefore, implementing such a prediction tool in clinical practice would facilitate therapeutic interventions by guiding clinical decision-making and would also facilitate clinical trials in kidney transplantation.
So far, no algorithm predicting long-term graft survival has been validated in different countries, populations and clinical scenarios, nor in the setting of randomized controlled trials.
Within this context, an international consortium gathering research teams from Europe and the US and led by Professor Alexandre Loupy, head of the Paris Transplant Group, looked at long-term patient data, including a wide variety of patient, donor and graft parameters. Combining all these data into one prediction system with the use of artificial intelligence proved successful to lead to a robust integrative tool.
The international research consortium has thus developed the first universal tool for predicting the risk of kidney graft loss, called iBox. Based on data easily available during patient follow-up after a kidney transplant, the tool generates probabilities of graft loss up to 10 years after patient evaluation. Tested on over 7,500 patients, the predictions obtained are highly reliable regardless of the health system, the clinical situation, the therapeutic intervention, or the treatment of the patient.
"By optimizing medical decision-making, the use of the iBox could have a significant impact not only on the quality of life of each patient but also on the long-term survival of transplanted kidneys," explained Professor Alexandre Loupy, "This tool perfectly illustrates the potential of algorithms for decision making process in the field of organ transplantation. It will be deployed at the end of 2019 in two European transplant centers."
Apart from clinical use, the iBox could also contribute to the development of new treatments. Indeed, current clinical research in the field of kidney transplantation is limited. Evaluating treatment effectiveness on graft survival requires years. The iBox should be able to significantly reduce the time, and therefore the cost, of these studies by providing an early-stage reliable prediction of the long-term graft survival. The iBox is thus emerging as a surrogate endpoint for the development of new clinical studies in kidney transplantation.
The article describing the iBox has just been published in the British Medical Journal. This study was conducted by research groups, clinical centers and patient associations in France, the United States and Belgium.
About the Paris Transplant Group
The Paris Transplant Group is an active network specialized in organ transplantation that brings together doctors and professors from major Parisian hospitals, immunologists, pathologists, epidemiologists, statisticians and mathematicians. Its global aim is to accelerate the translation of immunological and gene expression discoveries into the clinical field of transplantation by filling the gap between basic science and applied biomedical researches. It develops a personalized approach to transplant medicine that will integrate multidimensional information from different specialties.
More information on www.paristransplantgroup.org/
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