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Prediction system for risk of allograft loss in patients receiving kidney transplants: international derivation and validation study
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 cohor...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group Ltd.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6746192/ https://www.ncbi.nlm.nih.gov/pubmed/31530561 http://dx.doi.org/10.1136/bmj.l4923 |
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author | Loupy, Alexandre Aubert, Olivier Orandi, Babak J Naesens, Maarten Bouatou, Yassine Raynaud, Marc Divard, Gillian Jackson, Annette M Viglietti, Denis Giral, Magali Kamar, Nassim Thaunat, Olivier Morelon, Emmanuel Delahousse, Michel Kuypers, Dirk Hertig, Alexandre Rondeau, Eric Bailly, Elodie Eskandary, Farsad Böhmig, Georg Gupta, Gaurav Glotz, Denis Legendre, Christophe Montgomery, Robert A Stegall, Mark D Empana, Jean-Philippe Jouven, Xavier Segev, Dorry L Lefaucheur, Carmen |
author_facet | Loupy, Alexandre Aubert, Olivier Orandi, Babak J Naesens, Maarten Bouatou, Yassine Raynaud, Marc Divard, Gillian Jackson, Annette M Viglietti, Denis Giral, Magali Kamar, Nassim Thaunat, Olivier Morelon, Emmanuel Delahousse, Michel Kuypers, Dirk Hertig, Alexandre Rondeau, Eric Bailly, Elodie Eskandary, Farsad Böhmig, Georg Gupta, Gaurav Glotz, Denis Legendre, Christophe Montgomery, Robert A Stegall, Mark D Empana, Jean-Philippe Jouven, Xavier Segev, Dorry L Lefaucheur, Carmen |
author_sort | Loupy, Alexandre |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6746192 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BMJ Publishing Group Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-67461922019-09-27 Prediction system for risk of allograft loss in patients receiving kidney transplants: international derivation and validation study Loupy, Alexandre Aubert, Olivier Orandi, Babak J Naesens, Maarten Bouatou, Yassine Raynaud, Marc Divard, Gillian Jackson, Annette M Viglietti, Denis Giral, Magali Kamar, Nassim Thaunat, Olivier Morelon, Emmanuel Delahousse, Michel Kuypers, Dirk Hertig, Alexandre Rondeau, Eric Bailly, Elodie Eskandary, Farsad Böhmig, Georg Gupta, Gaurav Glotz, Denis Legendre, Christophe Montgomery, Robert A Stegall, Mark D Empana, Jean-Philippe Jouven, Xavier Segev, Dorry L Lefaucheur, Carmen BMJ Research 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. BMJ Publishing Group Ltd. 2019-09-17 /pmc/articles/PMC6746192/ /pubmed/31530561 http://dx.doi.org/10.1136/bmj.l4923 Text en Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Research Loupy, Alexandre Aubert, Olivier Orandi, Babak J Naesens, Maarten Bouatou, Yassine Raynaud, Marc Divard, Gillian Jackson, Annette M Viglietti, Denis Giral, Magali Kamar, Nassim Thaunat, Olivier Morelon, Emmanuel Delahousse, Michel Kuypers, Dirk Hertig, Alexandre Rondeau, Eric Bailly, Elodie Eskandary, Farsad Böhmig, Georg Gupta, Gaurav Glotz, Denis Legendre, Christophe Montgomery, Robert A Stegall, Mark D Empana, Jean-Philippe Jouven, Xavier Segev, Dorry L Lefaucheur, Carmen Prediction system for risk of allograft loss in patients receiving kidney transplants: international derivation and validation study |
title | Prediction system for risk of allograft loss in patients receiving kidney transplants: international derivation and validation study |
title_full | Prediction system for risk of allograft loss in patients receiving kidney transplants: international derivation and validation study |
title_fullStr | Prediction system for risk of allograft loss in patients receiving kidney transplants: international derivation and validation study |
title_full_unstemmed | Prediction system for risk of allograft loss in patients receiving kidney transplants: international derivation and validation study |
title_short | Prediction system for risk of allograft loss in patients receiving kidney transplants: international derivation and validation study |
title_sort | prediction system for risk of allograft loss in patients receiving kidney transplants: international derivation and validation study |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6746192/ https://www.ncbi.nlm.nih.gov/pubmed/31530561 http://dx.doi.org/10.1136/bmj.l4923 |
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