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 A. Böhmig,Gaurav Gupta,Denis Glotz,Christophe Legendre,Robert A. Montgomery,Mark D. Stegall,Jean Philippe Empana,Xavier Jouven,Dorry L. Segev,Carmen Lefaucheur +28 more
TLDR
The iBox system showed accuracy when assessed at different times of evaluation post-transplant, was validated in different clinical scenarios, and outperformed previous risk prediction scores as well as a risk score based solely on functional parameters including estimated glomerular filtration rate and proteinuria.Abstract:
Objective To develop and validate an integrative system to predict long term kidney allograft failure. Design International cohort study. Setting Three cohorts including kidney transplant recipients from 10 academic medical centres from Europe and the United States. Participants Derivation cohort: 4000 consecutive kidney recipients prospectively recruited in four French centres between 2005 and 2014. Validation cohorts: 2129 kidney recipients from three centres in Europe and 1428 from three centres in North America, recruited between 2002 and 2014. Additional validation in three randomised controlled trials (NCT01079143, EudraCT 2007-003213-13, and NCT01873157). Main outcome measure Allograft failure (return to dialysis or pre-emptive retransplantation). 32 candidate prognostic factors for kidney allograft survival were assessed. Results Among the 7557 kidney transplant recipients included, 1067 (14.1%) allografts failed after a median post-transplant follow-up time of 7.12 (interquartile range 3.51-8.77) years. In the derivation cohort, eight functional, histological, and immunological prognostic factors were independently associated with allograft failure and were then combined into a risk prediction score (iBox). This score showed accurate calibration and discrimination (C index 0.81, 95% confidence interval 0.79 to 0.83). The performance of the iBox was also confirmed in the validation cohorts from Europe (C index 0.81, 0.78 to 0.84) and the US (0.80, 0.76 to 0.84). The iBox system showed accuracy when assessed at different times of evaluation post-transplant, was validated in different clinical scenarios including type of immunosuppressive regimen used and response to rejection therapy, and outperformed previous risk prediction scores as well as a risk score based solely on functional parameters including estimated glomerular filtration rate and proteinuria. Finally, the accuracy of the iBox risk score in predicting long term allograft loss was confirmed in the three randomised controlled trials. Conclusion An integrative, accurate, and readily implementable risk prediction score for kidney allograft failure has been developed, which shows generalisability across centres worldwide and common clinical scenarios. The iBox risk prediction score may help to guide monitoring of patients and further improve the design and development of a valid and early surrogate endpoint for clinical trials. Trial registration Clinicaltrials.gov NCT03474003.read more
Citations
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The Banff 2019 Kidney Meeting Report (I): updates on and clarification of criteria for T cell- and antibody-mediated rejection
Alexandre Loupy,Mark Haas,Candice Roufosse,Maarten Naesens,Benjamin Adam,Marjan Afrouzian,Enver Akalin,Nada Alachkar,Serena M. Bagnasco,Jan U. Becker,Lynn D. Cornell,Marian C. Clahsen-van Groningen,Anthony J. Demetris,Duska Dragun,Jean-Paul Duong Van Huyen,Alton B. Farris,Agnes B. Fogo,Ian W. Gibson,Denis Glotz,Juliette Gueguen,Zeljko Kikic,Nicolas Kozakowski,Edward S. Kraus,Carmen Lefaucheur,Helen Liapis,Roslyn B. Mannon,Robert A. Montgomery,Brian J. Nankivell,Volker Nickeleit,Peter Nickerson,Marion Rabant,Lorraine C. Racusen,Parmjeet Randhawa,Blaise Robin,Ivy A. Rosales,Ruth Sapir-Pichhadze,Carrie A. Schinstock,Daniel Serón,Harsharan K. Singh,Rex Neal Smith,Mark D. Stegall,Adriana Zeevi,Kim Solez,Robert B. Colvin,Michael Mengel +44 more
TL;DR: This report on kidney transplant pathology details clarifications and refinements to the criteria for chronic active (CA) T cell–mediated rejection, borderline, and antibody‐mediated rejection (ABMR).
Journal ArticleDOI
Banff 2019 Meeting Report: Molecular diagnostics in solid organ transplantation–Consensus for the Banff Human Organ Transplant (B-HOT) gene panel and open source multicenter validation
Michael Mengel,Alexandre Loupy,Mark Haas,Candice Roufosse,Maarten Naesens,Enver Akalin,Marian C. Clahsen-van Groningen,Jessy Dagobert,Anthony J. Demetris,Jean-Paul Duong Van Huyen,Juliette Gueguen,Fadi Issa,Blaise Robin,Ivy A. Rosales,Jan H. von der Thüsen,Alberto Sanchez-Fueyo,Rex Neal Smith,Kathryn J. Wood,Benjamin Adam,Robert B. Colvin +19 more
TL;DR: The 770 gene B‐HOT panel includes the most pertinent genes related to rejection, tolerance, viral infections, and innate and adaptive immune responses, and this commercially available panel uses the NanoString platform, which can quantitate transcripts from formalin‐fixed paraffin‐embedded samples.
Journal ArticleDOI
Sensitization in transplantation: Assessment of risk (STAR) 2019 Working Group Meeting Report.
Anat R. Tambur,Patricia Campbell,Anita S. Chong,Sandy Feng,Mandy L. Ford,Howard M. Gebel,Ronald G. Gill,Garnett Kelsoe,Vasilis Kosmoliaptsis,Roslyn B. Mannon,Michael Mengel,Elaine F. Reed,Nicole Valenzuela,Chris Wiebe,I. Esme Dijke,Harold C. Sullivan,Peter Nickerson +16 more
TL;DR: The purpose of the STAR 2019 Working Group was to build on findings from the initial STAR report to further clarify the expectations, limitations, perceptions, and utility of alloimmune assays that are currently in use or in development for risk assessment in the setting of organ transplantation.
Journal ArticleDOI
Artificial intelligence and machine learning in nephropathology
Jan U. Becker,David Mayerich,Meghana Padmanabhan,Jonathan Barratt,Angela Ernst,Peter Boor,Pietro Antonio Cicalese,Chandra Mohan,Hien M. Nguyen,Badrinath Roysam +9 more
TL;DR: This review explains how AI can enhance the reproducibility of nephropathology results for certain parameters in the context of precision medicine using advanced architectures, such as convolutional neural networks, that are currently the state of the art in machine learning software for this task.
Journal ArticleDOI
Evidence for the alloimmune basis and prognostic significance of Borderline T cell-mediated rejection.
Chris Wiebe,David N. Rush,Ian W. Gibson,Denise Pochinco,Patricia E. Birk,Aviva Goldberg,Tom Blydt-Hansen,Martin Karpinski,Jamie Shaw,Julie Ho,Peter Nickerson +10 more
TL;DR: The correlation of HLA‐DR/DQ molecular mismatch category with TCMR, including Borderline, provides evidence for their alloimmune basis and can be applied to tailor immunosuppression or design clinical trials based on individual patient risk.
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