MedElevator Войти в исследования
← Поиск публикаций

DOI 10.1136/bmj.l4923

Медицинская библиотека · BMJ (Clinical research ed.) · 2019

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

Клиническое исследование II фазыКлиническое исследование III фазыСтатьяМногоцентровое исследованиеРандомизированное исследованиеПоддержка исследования: вне правительства СШАПеревод в очереди

Аннотация

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.

Для цитирования: 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.. BMJ (Clinical research ed.). 2019. DOI: 10.1136/bmj.l4923.

Предметные рубрики MeSH

Подготовка и перевод публикации
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…

История · было → стало

Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.

Тип публикации указан по данным источника. Уровень доказательности автоматически не присваивается.