The iBox technology :the leading way to predict long term allograft survival
External validation on worldwide clinical data
Several independent validation cohorts, including data from 3500+ kidney transplant recipients followed up to 12 years, demonstrate the exportability of the iBox algorithm in Europe and the US. The predictions are highly reliable regardless of the healthcare system, clinical setting or type of therapeutic intervention.
Unmatched performance in renal transplantation prognosis
The iBox, not only uses easy to get data from the standard of care to predict allograft survival but also, the model was found to have a good prediction capacity with a concordance statistic (C-Stat) of 0.81.
Perspectives: iBox algorithm as a surrogate endpoint in clinical trials
Additional validation of the performance of the iBox risk prediction was conducted in 3 randomized clinical trials (phase II and III). The iBox risk prediction system has the potential to improve the design of next generation clinical trials by reducing the time of follow-up as an early surrogate endpoint for clinical trials.
Prediction system for risk of allograft loss in patients receiving kidney transplants: international derivation and validation study
Alexandre Loupy, Olivier Aubert, Babak J. Orandi, Maarten Naesens, Yassine Bouatou, Marc Raynaud, Gillian Divard, Annette M. Jackson, Denis Viglietti, Magali Giral, Nassim Kamar, Olivier Thaunat, Emmanuel Morelon, Michel Delahousse, Dirk Kuypers, Alexandre Hertig, Eric Rondeau, Elodie Bailly, Farsad Eskandary, Georg Böhmig, Gaurav Gupta, Denis Glotz, Christophe Legendre, Robert A. Montgomery, Mark D. Stegall, France Rein – Reseau Solidaire en action FNAIR, RENALOO, Jean-Philippe Empana, Xavier Jouven, Dorry L. Segev and Carmen Lefaucheur.
Get training on the Ibox
The goal of this activity is to educate physicians on the iBox, a tool that can help optimizing medical decision-making and advancing personalized treatment in kidney transplant patients