Dynamic prediction of renal survival among deeply phenotyped kidney transplant recipients using artificial intelligence: an observational, international, multicohort study

Author:

Raynaud Marc,Aubert Olivier,Divard Gillian,Reese Peter P,Kamar Nassim,Yoo Daniel,Chin Chen-Shan,Bailly Élodie,Buchler Matthias,Ladrière Marc,Le Quintrec Moglie,Delahousse Michel,Juric Ivana,Basic-Jukic Nikolina,Crespo Marta,Silva Helio Tedesco,Linhares Kamilla,Ribeiro de Castro Maria Cristina,Soler Pujol Gervasio,Empana Jean-Philippe,Ulloa Camilo,Akalin Enver,Böhmig Georg,Huang Edmund,Stegall Mark D,Bentall Andrew J,Montgomery Robert A,Jordan Stanley C,Oberbauer Rainer,Segev Dorry L,Friedewald John J,Jouven Xavier,Legendre Christophe,Lefaucheur Carmen,Loupy AlexandreORCID

Funder

Fondation Bettencourt Schueller

Publisher

Elsevier BV

Subject

Health Information Management,Decision Sciences (miscellaneous),Health Informatics,Medicine (miscellaneous)

Reference38 articles.

1. Global kidney health 2017 and beyond: a roadmap for closing gaps in care, research, and policy;Levin;Lancet,2017

2. Developing new immunosuppression for the next generation of transplant recipients: the path forward;Stegall;Am J Transplant,2016

3. Prediction system for risk of allograft loss in patients receiving kidney transplants: international derivation and validation study;Loupy;BMJ,2019

4. Relationship between eGFR decline and hard outcomes after kidney transplants;Clayton;J Am Soc Nephrol,2016

5. Estimated glomerular filtration rate decline as a predictor of dialysis in kidney transplant recipients;McCaughan;Am J Nephrol,2014

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