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 BöhmigGaurav Gupta, Denis Glotz, Christophe Legendre, Robert A. Montgomery, Mark D. Stegall, Jean Philippe Empana, Xavier Jouven, Dorry L. Segev, Carmen Lefaucheur

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article numberl4923
JournalThe BMJ
Volume366
DOIs
StatePublished - Sep 17 2019

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Validation Studies
Allografts
Transplants
Kidney
Renal Insufficiency
Randomized Controlled Trials
Immunologic Factors
Physiologic Monitoring
Immunosuppressive Agents
North America
Glomerular Filtration Rate
Proteinuria
Calibration
Dialysis
Cohort Studies
Biomarkers
Outcome Assessment (Health Care)
Clinical Trials
Confidence Intervals

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Loupy, A., Aubert, O., Orandi, B. J., Naesens, M., Bouatou, Y., Raynaud, M., ... Lefaucheur, C. (2019). Prediction system for risk of allograft loss in patients receiving kidney transplants: International derivation and validation study. The BMJ, 366, [l4923]. https://doi.org/10.1136/bmj.l4923

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.

In: The BMJ, Vol. 366, l4923, 17.09.2019.

Research output: Contribution to journalArticle

Loupy, A, Aubert, O, Orandi, BJ, Naesens, M, Bouatou, Y, Raynaud, M, Divard, G, Jackson, AM, Viglietti, D, Giral, M, Kamar, N, Thaunat, O, Morelon, E, Delahousse, M, Kuypers, D, Hertig, A, Rondeau, E, Bailly, E, Eskandary, F, Böhmig, G, Gupta, G, Glotz, D, Legendre, C, Montgomery, RA, Stegall, MD, Empana, JP, Jouven, X, Segev, DL & Lefaucheur, C 2019, 'Prediction system for risk of allograft loss in patients receiving kidney transplants: International derivation and validation study', The BMJ, vol. 366, l4923. https://doi.org/10.1136/bmj.l4923
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. In: The BMJ. 2019 ; Vol. 366.
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title = "Prediction system for risk of allograft loss in patients receiving kidney transplants: International derivation and validation study",
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.",
author = "Alexandre Loupy and Olivier Aubert and Orandi, {Babak J.} and Maarten Naesens and Yassine Bouatou and Marc Raynaud and Gillian Divard and Jackson, {Annette M.} and Denis Viglietti and Magali Giral and Nassim Kamar and Olivier Thaunat and Emmanuel Morelon and Michel Delahousse and Dirk Kuypers and Alexandre Hertig and Eric Rondeau and Elodie Bailly and Farsad Eskandary and Georg B{\"o}hmig and Gaurav Gupta and Denis Glotz and Christophe Legendre and Montgomery, {Robert A.} and Stegall, {Mark D.} and Empana, {Jean Philippe} and Xavier Jouven and Segev, {Dorry L.} and Carmen Lefaucheur",
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TY - JOUR

T1 - Prediction system for risk of allograft loss in patients receiving kidney transplants

T2 - International derivation and validation study

AU - Loupy, Alexandre

AU - Aubert, Olivier

AU - Orandi, Babak J.

AU - Naesens, Maarten

AU - Bouatou, Yassine

AU - Raynaud, Marc

AU - Divard, Gillian

AU - Jackson, Annette M.

AU - Viglietti, Denis

AU - Giral, Magali

AU - Kamar, Nassim

AU - Thaunat, Olivier

AU - Morelon, Emmanuel

AU - Delahousse, Michel

AU - Kuypers, Dirk

AU - Hertig, Alexandre

AU - Rondeau, Eric

AU - Bailly, Elodie

AU - Eskandary, Farsad

AU - Böhmig, Georg

AU - Gupta, Gaurav

AU - Glotz, Denis

AU - Legendre, Christophe

AU - Montgomery, Robert A.

AU - Stegall, Mark D.

AU - Empana, Jean Philippe

AU - Jouven, Xavier

AU - Segev, Dorry L.

AU - Lefaucheur, Carmen

PY - 2019/9/17

Y1 - 2019/9/17

N2 - 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.

AB - 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.

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